https://learnlab.org/research/wiki/api.php?action=feedcontributions&user=Liuying&feedformat=atomLearnLab - User contributions [en]2024-03-28T08:54:33ZUser contributionsMediaWiki 1.31.12https://learnlab.org/wiki/index.php?title=File:Model.jpg&diff=9221File:Model.jpg2009-05-14T01:22:34Z<p>Liuying: </p>
<hr />
<div></div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integration_of_reading,_writing_and_typing_in_learning_Chinese_words&diff=9220Integration of reading, writing and typing in learning Chinese words2009-05-14T01:22:13Z<p>Liuying: /* Explanation */</p>
<hr />
<div>--------<br />
Summary table<br />
*Node Title: Integration of reading, writing and typing in learning Chinese words<br />
*Researchers: Ying Liu, Charles Perfetti, Qun (Connie) Guan, Suemei Wu, Min Wang<br />
*PIs: Ying Liu, Charles Perfetti<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Sep 1, 2008<br />
*Study End Date July 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 60<br />
*Planned Participant Hours for the study: 200<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical knowledge components that should be learned and integrated (Perfetti, Liu, and Tan, 2005). <br />
*Working together with the CMU Chinese online course, the present project will compare three methods: handwriting, Pinyin based computer typing, and both. Handwriting focuses on the semantic-orthography connections, whereas pinyin typing focuses on the semantic-phonology connection. We hypothesize that the combination of handwriting and pinyin typing can facilitate the integration of constituents. Theoretical framework and practical suggestions will be given on the learning of Chinese handwriting and typing in a modern technology rich learning environment. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning; Typing; Handwriting<br />
<br />
<br />
== Research question ==<br />
*How does [[integration]] of language constituents lead to [[robust learning]]?<br />
*Does writing Chinese lead to better integration and more robust Chinese reading?<br />
*Does the combination of writing and typing lead to more robust learning via better integration? <br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
* Visual recognition (lexical decision, partial character recognition ),handwriting, pinyin visual and auditory skills<br />
<br />
== Independent variables ==<br />
*Integration of handwriting vs. Pinyin typing vs. both<br />
[[Image:writingvstyping.jpg]]<br />
<br />
== Hypothesis ==<br />
*General: Instructional Events that integrate receptive and productive components lead to robust representations of Chinese characters.<br />
*Specific: Lexical constituents are interconnected in skilled performance and that supporting this interconnection during learning leads to more robust learning. Decomposed feature learning aids the acquisition of constituents and partial connections, but robust learning and fluency depend upon constituent integration. We hypothesize that handwriting plus pinyin typing will provide the most robust integration of perception and production in learning Chinese.<br />
<br />
<br />
== Expected Findings ==<br />
We predict that in the visual identification task, handwriting group will do better than the typing group, whereas typing group will do better in the auditory identification. In the translation task, the group received the combined method will do better than handwriting only group. We predict the above results because visual recognition task depends more on the orthographic information which is more practiced in the handwriting training. Auditory task depends more on the phonological information on the contrary, which is more practiced in the typing training. The translation task depends more on the integrated representation of Chinese words. When trained on both handwriting and typing, both orthographic and phonological routes are made available to the task.<br />
<br />
== Explanation ==<br />
The predicted results will be explained under the general framework of interactive constituency model for learners.<br />
*[[Image:model.jpg]]<br />
<br />
== Descendants ==<br />
None.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=File:Framework.jpg&diff=9219File:Framework.jpg2009-05-14T01:21:12Z<p>Liuying: uploaded a new version of "Image:Framework.jpg"</p>
<hr />
<div></div>Liuyinghttps://learnlab.org/wiki/index.php?title=File:Writingvstyping.jpg&diff=9218File:Writingvstyping.jpg2009-05-14T01:19:35Z<p>Liuying: </p>
<hr />
<div></div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integration_of_reading,_writing_and_typing_in_learning_Chinese_words&diff=9217Integration of reading, writing and typing in learning Chinese words2009-05-14T01:19:22Z<p>Liuying: /* Independent variables */</p>
<hr />
<div>--------<br />
Summary table<br />
*Node Title: Integration of reading, writing and typing in learning Chinese words<br />
*Researchers: Ying Liu, Charles Perfetti, Qun (Connie) Guan, Suemei Wu, Min Wang<br />
*PIs: Ying Liu, Charles Perfetti<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Sep 1, 2008<br />
*Study End Date July 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 60<br />
*Planned Participant Hours for the study: 200<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical knowledge components that should be learned and integrated (Perfetti, Liu, and Tan, 2005). <br />
*Working together with the CMU Chinese online course, the present project will compare three methods: handwriting, Pinyin based computer typing, and both. Handwriting focuses on the semantic-orthography connections, whereas pinyin typing focuses on the semantic-phonology connection. We hypothesize that the combination of handwriting and pinyin typing can facilitate the integration of constituents. Theoretical framework and practical suggestions will be given on the learning of Chinese handwriting and typing in a modern technology rich learning environment. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning; Typing; Handwriting<br />
<br />
<br />
== Research question ==<br />
*How does [[integration]] of language constituents lead to [[robust learning]]?<br />
*Does writing Chinese lead to better integration and more robust Chinese reading?<br />
*Does the combination of writing and typing lead to more robust learning via better integration? <br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
* Visual recognition (lexical decision, partial character recognition ),handwriting, pinyin visual and auditory skills<br />
<br />
== Independent variables ==<br />
*Integration of handwriting vs. Pinyin typing vs. both<br />
[[Image:writingvstyping.jpg]]<br />
<br />
== Hypothesis ==<br />
*General: Instructional Events that integrate receptive and productive components lead to robust representations of Chinese characters.<br />
*Specific: Lexical constituents are interconnected in skilled performance and that supporting this interconnection during learning leads to more robust learning. Decomposed feature learning aids the acquisition of constituents and partial connections, but robust learning and fluency depend upon constituent integration. We hypothesize that handwriting plus pinyin typing will provide the most robust integration of perception and production in learning Chinese.<br />
<br />
<br />
== Expected Findings ==<br />
We predict that in the visual identification task, handwriting group will do better than the typing group, whereas typing group will do better in the auditory identification. In the translation task, the group received the combined method will do better than handwriting only group. We predict the above results because visual recognition task depends more on the orthographic information which is more practiced in the handwriting training. Auditory task depends more on the phonological information on the contrary, which is more practiced in the typing training. The translation task depends more on the integrated representation of Chinese words. When trained on both handwriting and typing, both orthographic and phonological routes are made available to the task.<br />
<br />
== Explanation ==<br />
The predicted results will be explained under the general framework of interactive constituency model for learners.<br />
*[[Image:framework.jpg]]<br />
<br />
== Descendants ==<br />
None.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=File:LearningCondition3.jpg&diff=9216File:LearningCondition3.jpg2009-05-14T01:18:46Z<p>Liuying: uploaded a new version of "Image:LearningCondition3.jpg"</p>
<hr />
<div></div>Liuyinghttps://learnlab.org/wiki/index.php?title=File:LearningCondition3.jpg&diff=9215File:LearningCondition3.jpg2009-05-14T01:17:34Z<p>Liuying: uploaded a new version of "Image:LearningCondition3.jpg"</p>
<hr />
<div></div>Liuyinghttps://learnlab.org/wiki/index.php?title=File:LearningCondition3.jpg&diff=9214File:LearningCondition3.jpg2009-05-14T01:16:56Z<p>Liuying: uploaded a new version of "Image:LearningCondition3.jpg"</p>
<hr />
<div></div>Liuyinghttps://learnlab.org/wiki/index.php?title=File:LearningCondition3.jpg&diff=9213File:LearningCondition3.jpg2009-05-14T01:14:33Z<p>Liuying: uploaded a new version of "Image:LearningCondition3.jpg"</p>
<hr />
<div></div>Liuyinghttps://learnlab.org/wiki/index.php?title=File:Framework.jpg&diff=9212File:Framework.jpg2009-05-14T01:13:32Z<p>Liuying: </p>
<hr />
<div></div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integration_of_reading,_writing_and_typing_in_learning_Chinese_words&diff=9211Integration of reading, writing and typing in learning Chinese words2009-05-14T01:12:39Z<p>Liuying: /* Explanation */</p>
<hr />
<div>--------<br />
Summary table<br />
*Node Title: Integration of reading, writing and typing in learning Chinese words<br />
*Researchers: Ying Liu, Charles Perfetti, Qun (Connie) Guan, Suemei Wu, Min Wang<br />
*PIs: Ying Liu, Charles Perfetti<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Sep 1, 2008<br />
*Study End Date July 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 60<br />
*Planned Participant Hours for the study: 200<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical knowledge components that should be learned and integrated (Perfetti, Liu, and Tan, 2005). <br />
*Working together with the CMU Chinese online course, the present project will compare three methods: handwriting, Pinyin based computer typing, and both. Handwriting focuses on the semantic-orthography connections, whereas pinyin typing focuses on the semantic-phonology connection. We hypothesize that the combination of handwriting and pinyin typing can facilitate the integration of constituents. Theoretical framework and practical suggestions will be given on the learning of Chinese handwriting and typing in a modern technology rich learning environment. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning; Typing; Handwriting<br />
<br />
<br />
== Research question ==<br />
*How does [[integration]] of language constituents lead to [[robust learning]]?<br />
*Does writing Chinese lead to better integration and more robust Chinese reading?<br />
*Does the combination of writing and typing lead to more robust learning via better integration? <br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
* Visual recognition (lexical decision, partial character recognition ),handwriting, pinyin visual and auditory skills<br />
<br />
== Independent variables ==<br />
*Integration of handwriting vs. Pinyin typing vs. both<br />
[[Image:LearningCondition3.jpg]]<br />
<br />
== Hypothesis ==<br />
*General: Instructional Events that integrate receptive and productive components lead to robust representations of Chinese characters.<br />
*Specific: Lexical constituents are interconnected in skilled performance and that supporting this interconnection during learning leads to more robust learning. Decomposed feature learning aids the acquisition of constituents and partial connections, but robust learning and fluency depend upon constituent integration. We hypothesize that handwriting plus pinyin typing will provide the most robust integration of perception and production in learning Chinese.<br />
<br />
<br />
== Expected Findings ==<br />
We predict that in the visual identification task, handwriting group will do better than the typing group, whereas typing group will do better in the auditory identification. In the translation task, the group received the combined method will do better than handwriting only group. We predict the above results because visual recognition task depends more on the orthographic information which is more practiced in the handwriting training. Auditory task depends more on the phonological information on the contrary, which is more practiced in the typing training. The translation task depends more on the integrated representation of Chinese words. When trained on both handwriting and typing, both orthographic and phonological routes are made available to the task.<br />
<br />
== Explanation ==<br />
The predicted results will be explained under the general framework of interactive constituency model for learners.<br />
*[[Image:framework.jpg]]<br />
<br />
== Descendants ==<br />
None.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integration_of_reading,_writing_and_typing_in_learning_Chinese_words&diff=9210Integration of reading, writing and typing in learning Chinese words2009-05-14T01:12:28Z<p>Liuying: /* Explanation */</p>
<hr />
<div>--------<br />
Summary table<br />
*Node Title: Integration of reading, writing and typing in learning Chinese words<br />
*Researchers: Ying Liu, Charles Perfetti, Qun (Connie) Guan, Suemei Wu, Min Wang<br />
*PIs: Ying Liu, Charles Perfetti<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Sep 1, 2008<br />
*Study End Date July 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 60<br />
*Planned Participant Hours for the study: 200<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical knowledge components that should be learned and integrated (Perfetti, Liu, and Tan, 2005). <br />
*Working together with the CMU Chinese online course, the present project will compare three methods: handwriting, Pinyin based computer typing, and both. Handwriting focuses on the semantic-orthography connections, whereas pinyin typing focuses on the semantic-phonology connection. We hypothesize that the combination of handwriting and pinyin typing can facilitate the integration of constituents. Theoretical framework and practical suggestions will be given on the learning of Chinese handwriting and typing in a modern technology rich learning environment. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning; Typing; Handwriting<br />
<br />
<br />
== Research question ==<br />
*How does [[integration]] of language constituents lead to [[robust learning]]?<br />
*Does writing Chinese lead to better integration and more robust Chinese reading?<br />
*Does the combination of writing and typing lead to more robust learning via better integration? <br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
* Visual recognition (lexical decision, partial character recognition ),handwriting, pinyin visual and auditory skills<br />
<br />
== Independent variables ==<br />
*Integration of handwriting vs. Pinyin typing vs. both<br />
[[Image:LearningCondition3.jpg]]<br />
<br />
== Hypothesis ==<br />
*General: Instructional Events that integrate receptive and productive components lead to robust representations of Chinese characters.<br />
*Specific: Lexical constituents are interconnected in skilled performance and that supporting this interconnection during learning leads to more robust learning. Decomposed feature learning aids the acquisition of constituents and partial connections, but robust learning and fluency depend upon constituent integration. We hypothesize that handwriting plus pinyin typing will provide the most robust integration of perception and production in learning Chinese.<br />
<br />
<br />
== Expected Findings ==<br />
We predict that in the visual identification task, handwriting group will do better than the typing group, whereas typing group will do better in the auditory identification. In the translation task, the group received the combined method will do better than handwriting only group. We predict the above results because visual recognition task depends more on the orthographic information which is more practiced in the handwriting training. Auditory task depends more on the phonological information on the contrary, which is more practiced in the typing training. The translation task depends more on the integrated representation of Chinese words. When trained on both handwriting and typing, both orthographic and phonological routes are made available to the task.<br />
<br />
== Explanation ==<br />
The predicted results will be explained under the general framework of interactive constituency model for learners.<br />
*<br />
[[Image:framework.jpg]]<br />
<br />
== Descendants ==<br />
None.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integration_of_reading,_writing_and_typing_in_learning_Chinese_words&diff=9209Integration of reading, writing and typing in learning Chinese words2009-05-14T01:11:56Z<p>Liuying: /* Explanation */</p>
<hr />
<div>--------<br />
Summary table<br />
*Node Title: Integration of reading, writing and typing in learning Chinese words<br />
*Researchers: Ying Liu, Charles Perfetti, Qun (Connie) Guan, Suemei Wu, Min Wang<br />
*PIs: Ying Liu, Charles Perfetti<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Sep 1, 2008<br />
*Study End Date July 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 60<br />
*Planned Participant Hours for the study: 200<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical knowledge components that should be learned and integrated (Perfetti, Liu, and Tan, 2005). <br />
*Working together with the CMU Chinese online course, the present project will compare three methods: handwriting, Pinyin based computer typing, and both. Handwriting focuses on the semantic-orthography connections, whereas pinyin typing focuses on the semantic-phonology connection. We hypothesize that the combination of handwriting and pinyin typing can facilitate the integration of constituents. Theoretical framework and practical suggestions will be given on the learning of Chinese handwriting and typing in a modern technology rich learning environment. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning; Typing; Handwriting<br />
<br />
<br />
== Research question ==<br />
*How does [[integration]] of language constituents lead to [[robust learning]]?<br />
*Does writing Chinese lead to better integration and more robust Chinese reading?<br />
*Does the combination of writing and typing lead to more robust learning via better integration? <br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
* Visual recognition (lexical decision, partial character recognition ),handwriting, pinyin visual and auditory skills<br />
<br />
== Independent variables ==<br />
*Integration of handwriting vs. Pinyin typing vs. both<br />
[[Image:LearningCondition3.jpg]]<br />
<br />
== Hypothesis ==<br />
*General: Instructional Events that integrate receptive and productive components lead to robust representations of Chinese characters.<br />
*Specific: Lexical constituents are interconnected in skilled performance and that supporting this interconnection during learning leads to more robust learning. Decomposed feature learning aids the acquisition of constituents and partial connections, but robust learning and fluency depend upon constituent integration. We hypothesize that handwriting plus pinyin typing will provide the most robust integration of perception and production in learning Chinese.<br />
<br />
<br />
== Expected Findings ==<br />
We predict that in the visual identification task, handwriting group will do better than the typing group, whereas typing group will do better in the auditory identification. In the translation task, the group received the combined method will do better than handwriting only group. We predict the above results because visual recognition task depends more on the orthographic information which is more practiced in the handwriting training. Auditory task depends more on the phonological information on the contrary, which is more practiced in the typing training. The translation task depends more on the integrated representation of Chinese words. When trained on both handwriting and typing, both orthographic and phonological routes are made available to the task.<br />
<br />
== Explanation ==<br />
The predicted results will be explained under the general framework of interactive constituency model for learners.<br />
<br />
== Descendants ==<br />
None.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integration_of_reading,_writing_and_typing_in_learning_Chinese_words&diff=9208Integration of reading, writing and typing in learning Chinese words2009-05-14T01:10:44Z<p>Liuying: /* Findings */</p>
<hr />
<div>--------<br />
Summary table<br />
*Node Title: Integration of reading, writing and typing in learning Chinese words<br />
*Researchers: Ying Liu, Charles Perfetti, Qun (Connie) Guan, Suemei Wu, Min Wang<br />
*PIs: Ying Liu, Charles Perfetti<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Sep 1, 2008<br />
*Study End Date July 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 60<br />
*Planned Participant Hours for the study: 200<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical knowledge components that should be learned and integrated (Perfetti, Liu, and Tan, 2005). <br />
*Working together with the CMU Chinese online course, the present project will compare three methods: handwriting, Pinyin based computer typing, and both. Handwriting focuses on the semantic-orthography connections, whereas pinyin typing focuses on the semantic-phonology connection. We hypothesize that the combination of handwriting and pinyin typing can facilitate the integration of constituents. Theoretical framework and practical suggestions will be given on the learning of Chinese handwriting and typing in a modern technology rich learning environment. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning; Typing; Handwriting<br />
<br />
<br />
== Research question ==<br />
*How does [[integration]] of language constituents lead to [[robust learning]]?<br />
*Does writing Chinese lead to better integration and more robust Chinese reading?<br />
*Does the combination of writing and typing lead to more robust learning via better integration? <br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
* Visual recognition (lexical decision, partial character recognition ),handwriting, pinyin visual and auditory skills<br />
<br />
== Independent variables ==<br />
*Integration of handwriting vs. Pinyin typing vs. both<br />
[[Image:LearningCondition3.jpg]]<br />
<br />
== Hypothesis ==<br />
*General: Instructional Events that integrate receptive and productive components lead to robust representations of Chinese characters.<br />
*Specific: Lexical constituents are interconnected in skilled performance and that supporting this interconnection during learning leads to more robust learning. Decomposed feature learning aids the acquisition of constituents and partial connections, but robust learning and fluency depend upon constituent integration. We hypothesize that handwriting plus pinyin typing will provide the most robust integration of perception and production in learning Chinese.<br />
<br />
<br />
== Expected Findings ==<br />
We predict that in the visual identification task, handwriting group will do better than the typing group, whereas typing group will do better in the auditory identification. In the translation task, the group received the combined method will do better than handwriting only group. We predict the above results because visual recognition task depends more on the orthographic information which is more practiced in the handwriting training. Auditory task depends more on the phonological information on the contrary, which is more practiced in the typing training. The translation task depends more on the integrated representation of Chinese words. When trained on both handwriting and typing, both orthographic and phonological routes are made available to the task.<br />
<br />
== Explanation ==<br />
*Not available yet<br />
<br />
== Descendants ==<br />
None.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Learning_a_tonal_language:_Chinese&diff=9207Learning a tonal language: Chinese2009-05-14T01:00:25Z<p>Liuying: /* Further information */</p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Learning a tonal language: Chinese<br />
*Researchers: Min Wang, Ying Liu, Suemei Wu, Derek Chan, Charles Perfetti<br />
*PIs: Min Wang, Charles Perfetti, Ying Liu<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Baoguo Chen<br />
*Graduate Students: Derek Chan, Brian Brubaker<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2006<br />
*LearnLab Site and Courses , CMU Chinese Online<br />
*Number of Students: 150<br />
*Total Participant Hours for the study: 300<br />
*Data in the Data Shop: Yes<br />
----<br />
== Abstract ==<br />
*The tonal feature of Chinese language poses a particular challenge for a beginning learner of Chinese as a second language. In this project, we test learning hypotheses based on the assumption that attending to the critical [[features]] of the tonal pitch contour facilitates learning.<br />
*This study consists of experiments on both tone perception and production tasks. In tone perception task, three training conditions were tested: 1) visual pitch contours that depict the acoustic information of the tones, together with Pinyin spelling of the spoken syllable; 2) numerical numbers that represent the tones in traditional classroom instruction, together with Pinyin spelling of the spoken syllable; 3) visual pitch contours, without Pinyin spelling. By comparing these three training conditions, we will test two hypotheses: 1) using visual information of the tone waveform facilitates students’ perception of auditory tones; 2) providing Pinyin spelling allows the students to focus on the tone, therefore yields more [[robust learning]], which was measured by [[transfer]] and [[long-term retention]] tasks.<br />
*In tone production task, we used a frequency analyzer to extract the fundamental frequency of student’s sound production. The pitch contour of production will be displayed to the student in real time during their production practice. By comparing the group which receives this individualized pitch contour with a group which does not, we predict the former will show more [[robust learning]] on tone production, which was shown as pronunciation [[refinement]]. <br />
<br />
<br />
== Glossary ==<br />
Tone; pitch contour; visual feedback<br />
<br />
<br />
== Research question ==<br />
How to optimally use crucial tonal information to facilitate Chinese tone learning.<br />
<br />
== Background ==<br />
*The basic speech unit of Chinese is the syllable, and each syllable is divided into two parts: onset and rime. The onset of a Chinese syllable is always a single consonant. In most syllables the rime segment consists of mainly vowels. As a result, Chinese has a much smaller number of syllables than does spoken English (Hanely, Tzeng, & Huang, 1999). This leads to a large number of homophones in Chinese. However, because of the existence of tone in Chinese syllables, the number of homophones is reduced. There are about 1,300 tone syllables in spoken Chinese (Taylor & Taylor, 1995). <br />
*The tonal feature of the Chinese language forms a sharp contrast to many alphabetic languages such as English. American college students learning Chinese language may encounter great difficulty in acquiring the tone skill. Wang, Perfetti, and Liu (2003) used a onset-rime-tone matching task to test beginning Chinese learners’ phonological processing skills. We found that these beginning Chinese learners showed poorer performance in tone matching compared to their performance in onset and rime matching.<br />
*There is very limited research on studying tone learning. Three-year-old Chinese native-speaking children have been shown to be able to detect when rime and tone are combined but they cannot detect rime and tone separately. Five-year-olds, on the other hand, can independently process rime and tone (Ho & Bryant, 1997). Wang, Spence, Jongman and Sereno (1999) trained American listeners to perceive Chinese tones. They found a significant increase of identification accuracy from pretest to posttest. <br />
<br />
== Dependent variables ==<br />
<br />
[[Normal post-test]]: Accuracy of making tone selection and decision tasks, and evaluations of productions.<br />
<br />
== Independent variables ==<br />
<br />
''Tone perception study'': 1) visual pitch contours that depict the acoustic information of the tones, together with Pinyin spelling of the spoken syllable; 2) numerical numbers that represent the tones in traditional classroom instruction, together with Pinyin spelling of the spoken syllable; 3) visual pitch contours, without Pinyin spelling.<br />
<br />
''Tone production study'': 1) visual feedback based on tone analyzer of student’s pronunciation; 2) no visual feedback.<br />
<br />
== Hypothesis ==<br />
Having student focusing on tonal feature by providing visual pitch contour plus segmental information facilitates tonal perception and production.<br />
<br />
== Findings ==<br />
Current results from two terms of tone perception experiment showed providing segmental information (Pinyin) provides a better learning curve. The y-axis in the figure is error rate(?). The learning curve of term 1 (lesson 1 to 8) showed Pinyin+contour and Pinyin+number conditions are better than contour only condition. The following Figure of fitted learning curve showed that the former two conditions have more negative slope (faster learning rate).<br />
*[[Image:tone1.jpg]]<br />
<br />
== Explanation ==<br />
Learning Chinese tone was facilitated by having students [[focusing]] on the tonal features. Proving segmental information (Pinyin) before learning to a syllable sound provides more [[assistance]] to beginners, which makes it easier for them to pay more attention to the tone. <br />
Furthremore, the visual pitch contour and auditory tone are [[complementary]] information for learning tones. The mental representation of tones are more complete when visual pintch contour are provided together with Pinyin.<br />
<br />
== Descendents ==<br />
Tone perception (the present page)<br />
Tone production (under construction)<br />
<br />
== Further information ==</div>Liuyinghttps://learnlab.org/wiki/index.php?title=File:LearningCondition3.jpg&diff=9206File:LearningCondition3.jpg2009-05-14T00:57:53Z<p>Liuying: </p>
<hr />
<div></div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integration_of_reading,_writing_and_typing_in_learning_Chinese_words&diff=9205Integration of reading, writing and typing in learning Chinese words2009-05-14T00:57:25Z<p>Liuying: </p>
<hr />
<div>--------<br />
Summary table<br />
*Node Title: Integration of reading, writing and typing in learning Chinese words<br />
*Researchers: Ying Liu, Charles Perfetti, Qun (Connie) Guan, Suemei Wu, Min Wang<br />
*PIs: Ying Liu, Charles Perfetti<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Sep 1, 2008<br />
*Study End Date July 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 60<br />
*Planned Participant Hours for the study: 200<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical knowledge components that should be learned and integrated (Perfetti, Liu, and Tan, 2005). <br />
*Working together with the CMU Chinese online course, the present project will compare three methods: handwriting, Pinyin based computer typing, and both. Handwriting focuses on the semantic-orthography connections, whereas pinyin typing focuses on the semantic-phonology connection. We hypothesize that the combination of handwriting and pinyin typing can facilitate the integration of constituents. Theoretical framework and practical suggestions will be given on the learning of Chinese handwriting and typing in a modern technology rich learning environment. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning; Typing; Handwriting<br />
<br />
<br />
== Research question ==<br />
*How does [[integration]] of language constituents lead to [[robust learning]]?<br />
*Does writing Chinese lead to better integration and more robust Chinese reading?<br />
*Does the combination of writing and typing lead to more robust learning via better integration? <br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
* Visual recognition (lexical decision, partial character recognition ),handwriting, pinyin visual and auditory skills<br />
<br />
== Independent variables ==<br />
*Integration of handwriting vs. Pinyin typing vs. both<br />
[[Image:LearningCondition3.jpg]]<br />
<br />
== Hypothesis ==<br />
*General: Instructional Events that integrate receptive and productive components lead to robust representations of Chinese characters.<br />
*Specific: Lexical constituents are interconnected in skilled performance and that supporting this interconnection during learning leads to more robust learning. Decomposed feature learning aids the acquisition of constituents and partial connections, but robust learning and fluency depend upon constituent integration. We hypothesize that handwriting plus pinyin typing will provide the most robust integration of perception and production in learning Chinese.<br />
<br />
<br />
== Findings ==<br />
*Not available yet<br />
<br />
== Explanation ==<br />
*Not available yet<br />
<br />
== Descendants ==<br />
None.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integration_of_reading,_writing_and_typing_in_learning_Chinese_words&diff=9204Integration of reading, writing and typing in learning Chinese words2009-05-14T00:38:13Z<p>Liuying: New page: Research question: Does the combination of writing and typing Chinese lead to more robust learning via better integration? Learning lab: Chinese Required students: 40 Total study tim...</p>
<hr />
<div><br />
<br />
<br />
<br />
Research question: Does the combination of writing and typing Chinese lead to more robust learning via better integration? <br />
<br />
Learning lab: Chinese<br />
Required students: 40<br />
Total study time: 3 to 4 hours<br />
Study period: Oct 13 – Nov 7, 2008 (four weeks)<br />
<br />
Research design and procedure<br />
Students in the Chinese online course at CMU will participate this study as part of their course. There will be 4 one hour long sessions with one week interval between them (see Table 1). The first and the last are testing sessions, and the 2nd and the 3rd are training sessions. In the first session, a pretest on the visual recognition, handwriting, and typing skills will be carried out. In each training sessions, 20 novel Chinese two character words will be taught online through the Integrated Chinese Tutor (ICT). The 3rd session starts with a test on visual recognition and auditory identification task.<br />
<br />
Table 1 Experimental sessions in the first semester<br />
Session 1 Session 2 Session 3 Session 4<br />
Group A Pretest Handwriting training Test,<br />
Handwriting training Posttest<br />
Group B Pretest Typing training Test,<br />
Handwriting training Posttest<br />
<br />
A post-test will be conducted near the end of the semester. The post-test includes a visual recognition task, an auditory identification task, and a translation task.<br />
<br />
The ICT will log all student activities including accuracies to the data shop server. Currently the student handwriting images can not be logged automatically to the data shop. Instead, the images are logged to the local computer and uploaded to the data shop manually.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Refinement_and_Fluency&diff=9203Refinement and Fluency2009-05-14T00:30:49Z<p>Liuying: /* Knowledge accessibility */</p>
<hr />
<div>= The PSLC Refinement and Fluency cluster =<br />
<br />
== Abstract ==<br />
The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br />
<br />
1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br />
<br />
2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br />
<br />
3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br />
<br />
4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information ("facts") or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br />
<br />
5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br />
<br />
6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br />
<br />
7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br />
<br />
8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br />
<br />
9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br />
<br />
10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br />
<br />
The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br />
<br />
This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br />
<br />
<br><center>[[Image:Rf.JPG]]</center><br />
<br />
==Significance==<br />
A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br />
<br />
== Glossary ==<br />
[[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br />
<br />
== Research question ==<br />
The overall research question is how can instruction optimally support the acquisition, refinement, and fluent use of complex targeted knowledge, taking into account the learner’s existing knowledge in relation to the knowledge demands of the target domain? In examining this general question, the studies focus on features of the learning situation, including the following: the cognitive demands of targeted knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br />
<br />
== Independent variables ==<br />
At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br />
<br />
== Dependent variables ==<br />
The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br />
<br />
== Hypotheses ==<br />
Instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. More specifically, the initial acquisition of knowledge and its refinement benefit from instructional activities that require the learner to attend to and encode [[valid features]] of the learning content. The fluency corollary: Fluency builds on the knowledge components acquired and refined in learning, strengthening and integrating these components through practice.<br />
<br />
<br />
Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br />
<br />
1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br />
<br />
2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby "divergent coding systems" here may be the same as "multiple input sources" in co-training.)<br />
<br />
3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br />
<br />
4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br />
<br />
5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br />
<br />
6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br />
<br />
7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br />
<br />
8. Learning to learn hypothesis: The acquisition of certain skills in one context support future learning in other contexts. Such skills include problem analysis, help-seeking, or advance organizers. <br />
<br />
9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br />
<br />
10. Active learning hypothesis: Even in simple tasks, learning is more robust when the learner actively engages in the learning material.<br />
<br />
== Explanation ==<br />
All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br />
<br />
== Descendents ==<br />
<br />
=== Explicit instruction ===<br />
'''A. Explicit vs Implicit.''' These projects typically compare a more explict form of instruction with a more implict form <br />
* [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br />
* [[Basic skills training|French dictation training]] (MacWhinney)<br />
* [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br />
<br />
'''B. Explicit attention manipulations''' studies typically vary features available to learner<br />
* [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br />
* [[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]<br />
* [[French gender cues | French grammatical gender cue learning]] (Presson, MacWhinney)<br />
** [[Learning French gender cues with prototypes | Instruction of French gender cues]] (Presson, MacWhinney)<br />
**[[French gender prototypes | Lab study of grammar learning contrasting explicit and implicit instruction and prototype usage]] (Presson, MacWhinney)<br />
**[[French gender attention | Lab study of effects of time pressure and explicitness on gender learning]] (Presson, MacWhinney)<br />
<br />
'''C. Explicit instruction: Practice and Scheduling''' Typical studies control practice events and provide feedback<br />
* [[Optimizing the practice schedule]] (Pavlik et al.) [[Applying optimal scheduling of practice in the Chinese Learnlab|1]]<br />
* [[Japanese fluency]] (Yoshimura-MacWhinney)<br />
* [[Fostering fluency in second language learning]] (De Jong, Halderman, Perfetti)<br />
* [[Using learning curves to optimize problem assignment]] (Cen & Koedinger)<br />
* [[Learning ESL Vocabulary with Context and Definitions: Order Effects and Self-Generation]] (Balass, Nelson, Perfetti)<br />
<br />
=== Knowledge accessibility ===<br />
'''A. Background knowledge''' These projects directly study effects of learners' background knowledge<br />
* [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura, Wylie)<br />
* [[Assistance_Dilemma_English_Articles | The Assistance Dilemma and the English Article System]] (Wylie, Mitamura, Koedinger)<br />
* [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in Interactive Communication]<br />
* [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br />
<br />
'''B. Availability of knowledge during learning'''<br />
* [[Optimizing the practice schedule]] (Pavlik et al.) [[Understanding paired associate transfer effects based on shared stimulus components|2]], [[Applying optimal scheduling of practice in the Chinese Learnlab|1]], [[Understanding encoding inhibition, retrieval inhibition and destructive interference effects of errors during practice|3]]<br />
* [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br />
* [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br />
* [[Arithmetical fluency project]] (Fiez)<br />
* [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, & Liu)<br />
* [[Integrated Learning of Chinese]] (Liu, Perfetti, Wang, Wu)<br />
* [[Integration of reading, writing and typing in learning Chinese words]] (Liu, Perfetti, Guan, Wu, Wang)<br />
<br />
=== Active processing ===<br />
These projects also include some addressing issues of learner control<br />
* [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br />
*[[Note-Taking_Technologies | Note-taking Project Page (Bauer & Koedinger)]] [Also in Coordinative Learning]<br />
**[[Note-Taking: Restriction and Selection]] (completed)<br />
**[[Note-Taking: Focusing On Concepts]] (planned)<br />
**[[Note-Taking: Focusing On Quantity]] (planned)<br />
*[[Handwriting Algebra Tutor]] (Anthony, Yang & Koedinger) [Also in Coordinative Learning]<br />
**[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) <br />
**[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress)<br />
<br />
===Other===<br />
<br />
* [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br />
<br />
== Annotated bibliography ==<br />
Forthcoming<br />
<br />
[[Category:Cluster]]</div>Liuyinghttps://learnlab.org/wiki/index.php?title=File:Study1.jpg&diff=8517File:Study1.jpg2008-11-03T03:15:14Z<p>Liuying: </p>
<hr />
<div></div>Liuyinghttps://learnlab.org/wiki/index.php?title=Co-training_of_Chinese_characters&diff=8516Co-training of Chinese characters2008-11-03T03:13:57Z<p>Liuying: /* Independent variables */</p>
<hr />
<div>----<br />
'''Summary Table'''<br />
*Node Title: Learning to read Chinese: [[Co-training]] in human (Study 1)<br />
*Researchers: Ying Liu, Charles Perfetti, Susan Dunlap, Gusheng Zi, Tom Mitchell<br />
*PIs: Ying Liu, Charles Perfetti, Tom Mitchell<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Gusheng Zi<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2005<br />
*LearnLab Site and Courses: LRDC, pull out study<br />
*Number of Students: 44<br />
*Total Participant Hours for the study: 44<br />
*Data in the Data Shop: Yes<br />
----<br />
<br />
== Abstract ==<br />
The present study explored how native English speakers learn to speak and read Chinese in a cotraining environment. The experiment consisted of two parts. The first part was training, which was used to teach the input (Chinese fonts and sounds) to output (English translations) mapping of 16 Chinese characters. Training methods were manipulated in this part. A quarter of the subjects only received labeled training trials (English translation provided), the others received extra training trials with [[unlabeled examples|non-labeled trials]] (only the orthography or/and phonology without English translation). The non-labeled trials were further separated into three types: unpaired, correlated paired and uncorrelated paired, with each type used for one quarter of subjects.<br />
The second part was posttest, in which students produced the English translation when they saw the Chinese fonts or hear the Chinese sounds one by one. The accuracy of translation was recorded. It showed that [[unlabeled examples]] did help the learning, and uncorrelated paired examples did the best among all three types of unlabeled examples.<br />
<br />
== Glossary ==<br />
2. A glossary that defines terms used elsewhere in this node but not defined in the nodes that are parents, grandparents, etc. of this node; <br />
<br />
labeling; source pairing; source correlation.<br />
<br />
== Research question ==<br />
<br />
How native English speakers learn to speak and read Chinese under various coordinative learning conditions. <br />
<br />
== Background ==<br />
<br />
In machine learning research, it has been found that multiple-strategies and multiple modalities facilitate learning (Blum and Mitchell, 1998). However, the effectiveness of the properties of “co-training” theory have not been tested in human learners yet. We carried out this study to directly test two important properties of this theory in human learners. There are two results from the finished experiment and one non-result of interest. Most dramatic is the advantage of written over spoken input. This has nothing to do with co-training but is interesting and important for L2 word learning (translation). Second is the pairs effect, the advantage of spoken + written input presented during unlabelled training compared with either one separately. The independence of the surface features of these inputs (specific speaker, specific font) was not a factor.<br />
<br />
To understand the pairs effect, we have to know whether it is restricted to or larger for [[unlabeled examples|unlabeled trials]]. Experiment 1 did not manipulate pairing in labeled trials. In the fall of 2006, we tested the pairing property under both labeled and unlabeled trails.<br />
<br />
To understand the correlation feature better, we are testing the correlation feature in an in-vivo setup with more learning sessions.<br />
<br />
== Dependent variables ==<br />
<br />
[[Normal post-test]]: Accuracy of producing the English word under reading and/or listening situation.<br />
<br />
== Independent variables ==<br />
Labeling variable and correction variable.<br />
Four training conditions, between subject design. All subjects received 48 Labeled examples, then followed by<br />
A) none;<br />
B) 192 unpaired unlabeled examples;<br />
C) correlated paired unlabeled examples;<br />
D) uncorrelated paired unlabeled examples.<br />
[[Image:study1.jpg]]<br />
<br />
== Hypothesis ==<br />
<br />
Pairing of visual font and auditory sound of Chinese characters should enhance learning under both labeled and unlabeled trials, but the benefit is most significant when the trials are unlabeled.<br />
*<br />
[[Image:cotraining1.jpg]]<br />
<br />
== Findings ==<br />
<br />
*“Unlabelled paired” trials may aid learning. Learning meanings was facilitated by the addition of unlabeled paired trials that did not provide meaning.<br />
**However, this unlabeled-trials effect was restricted to cross-modal pairs (spoken syllable and written character); it was absent when only one (spoken syllable) or the other (written character) modality was presented.<br />
**Implication: Cross-modal inputs in this situation can establish multiple representations (speech-writing pairs) from which meaning links are more readily retrieved.<br />
*Written form learned better than spoken form Large advantage for the presentation of written characters compared with their corresponding spoken syllables for learning a form-meaning pair.<br />
*Benefits of uncorrelated examples was not observed. <br />
**Correlated examples: Given font and given speaker always co-occur (conditional dependent)<br />
**Uncorrelated examples: Given font occurs with all speakers; and given speaker occurs with all fonts (conditional independent)<br />
**This is still being assessed by using multiple learning sessions. <br />
<br />
[[Image:cotraining2.jpg]]<br />
<br />
== Explanation ==<br />
<br />
Learning meanings was facilitated by the addition of unlabeled paired trials that did not provide meaning implicates that predictions of the label are generated for unlabeled trials, so they serve as self-generated labeled trials and work as meaningful materials for learning. This effect is especially significant in multiple input situation (paired trials) because the establishment of multiple representations (speech-writing pairs) makes the “label prediction” more accurate.<br />
<br />
== Descendents ==<br />
<br />
None.<br />
<br />
== Further information ==</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Co-training_of_Chinese_characters&diff=8515Co-training of Chinese characters2008-11-03T03:13:13Z<p>Liuying: /* Independent variables */</p>
<hr />
<div>----<br />
'''Summary Table'''<br />
*Node Title: Learning to read Chinese: [[Co-training]] in human (Study 1)<br />
*Researchers: Ying Liu, Charles Perfetti, Susan Dunlap, Gusheng Zi, Tom Mitchell<br />
*PIs: Ying Liu, Charles Perfetti, Tom Mitchell<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Gusheng Zi<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2005<br />
*LearnLab Site and Courses: LRDC, pull out study<br />
*Number of Students: 44<br />
*Total Participant Hours for the study: 44<br />
*Data in the Data Shop: Yes<br />
----<br />
<br />
== Abstract ==<br />
The present study explored how native English speakers learn to speak and read Chinese in a cotraining environment. The experiment consisted of two parts. The first part was training, which was used to teach the input (Chinese fonts and sounds) to output (English translations) mapping of 16 Chinese characters. Training methods were manipulated in this part. A quarter of the subjects only received labeled training trials (English translation provided), the others received extra training trials with [[unlabeled examples|non-labeled trials]] (only the orthography or/and phonology without English translation). The non-labeled trials were further separated into three types: unpaired, correlated paired and uncorrelated paired, with each type used for one quarter of subjects.<br />
The second part was posttest, in which students produced the English translation when they saw the Chinese fonts or hear the Chinese sounds one by one. The accuracy of translation was recorded. It showed that [[unlabeled examples]] did help the learning, and uncorrelated paired examples did the best among all three types of unlabeled examples.<br />
<br />
== Glossary ==<br />
2. A glossary that defines terms used elsewhere in this node but not defined in the nodes that are parents, grandparents, etc. of this node; <br />
<br />
labeling; source pairing; source correlation.<br />
<br />
== Research question ==<br />
<br />
How native English speakers learn to speak and read Chinese under various coordinative learning conditions. <br />
<br />
== Background ==<br />
<br />
In machine learning research, it has been found that multiple-strategies and multiple modalities facilitate learning (Blum and Mitchell, 1998). However, the effectiveness of the properties of “co-training” theory have not been tested in human learners yet. We carried out this study to directly test two important properties of this theory in human learners. There are two results from the finished experiment and one non-result of interest. Most dramatic is the advantage of written over spoken input. This has nothing to do with co-training but is interesting and important for L2 word learning (translation). Second is the pairs effect, the advantage of spoken + written input presented during unlabelled training compared with either one separately. The independence of the surface features of these inputs (specific speaker, specific font) was not a factor.<br />
<br />
To understand the pairs effect, we have to know whether it is restricted to or larger for [[unlabeled examples|unlabeled trials]]. Experiment 1 did not manipulate pairing in labeled trials. In the fall of 2006, we tested the pairing property under both labeled and unlabeled trails.<br />
<br />
To understand the correlation feature better, we are testing the correlation feature in an in-vivo setup with more learning sessions.<br />
<br />
== Dependent variables ==<br />
<br />
[[Normal post-test]]: Accuracy of producing the English word under reading and/or listening situation.<br />
<br />
== Independent variables ==<br />
Labeling variable and correction variable.<br />
Four training conditions, between subject design. All subjects received 48 Labeled examples, then followed by<br />
A) none;<br />
B) 192 unpaired unlabeled examples;<br />
C) correlated paired unlabeled examples;<br />
D) uncorrelated paired unlabeled examples.<br />
*[[Image:Study1.jpg]]<br />
<br />
== Hypothesis ==<br />
<br />
Pairing of visual font and auditory sound of Chinese characters should enhance learning under both labeled and unlabeled trials, but the benefit is most significant when the trials are unlabeled.<br />
*<br />
[[Image:cotraining1.jpg]]<br />
<br />
== Findings ==<br />
<br />
*“Unlabelled paired” trials may aid learning. Learning meanings was facilitated by the addition of unlabeled paired trials that did not provide meaning.<br />
**However, this unlabeled-trials effect was restricted to cross-modal pairs (spoken syllable and written character); it was absent when only one (spoken syllable) or the other (written character) modality was presented.<br />
**Implication: Cross-modal inputs in this situation can establish multiple representations (speech-writing pairs) from which meaning links are more readily retrieved.<br />
*Written form learned better than spoken form Large advantage for the presentation of written characters compared with their corresponding spoken syllables for learning a form-meaning pair.<br />
*Benefits of uncorrelated examples was not observed. <br />
**Correlated examples: Given font and given speaker always co-occur (conditional dependent)<br />
**Uncorrelated examples: Given font occurs with all speakers; and given speaker occurs with all fonts (conditional independent)<br />
**This is still being assessed by using multiple learning sessions. <br />
<br />
[[Image:cotraining2.jpg]]<br />
<br />
== Explanation ==<br />
<br />
Learning meanings was facilitated by the addition of unlabeled paired trials that did not provide meaning implicates that predictions of the label are generated for unlabeled trials, so they serve as self-generated labeled trials and work as meaningful materials for learning. This effect is especially significant in multiple input situation (paired trials) because the establishment of multiple representations (speech-writing pairs) makes the “label prediction” more accurate.<br />
<br />
== Descendents ==<br />
<br />
None.<br />
<br />
== Further information ==</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Co-training_of_Chinese_characters&diff=8514Co-training of Chinese characters2008-11-03T03:12:21Z<p>Liuying: /* Independent variables */</p>
<hr />
<div>----<br />
'''Summary Table'''<br />
*Node Title: Learning to read Chinese: [[Co-training]] in human (Study 1)<br />
*Researchers: Ying Liu, Charles Perfetti, Susan Dunlap, Gusheng Zi, Tom Mitchell<br />
*PIs: Ying Liu, Charles Perfetti, Tom Mitchell<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Gusheng Zi<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2005<br />
*LearnLab Site and Courses: LRDC, pull out study<br />
*Number of Students: 44<br />
*Total Participant Hours for the study: 44<br />
*Data in the Data Shop: Yes<br />
----<br />
<br />
== Abstract ==<br />
The present study explored how native English speakers learn to speak and read Chinese in a cotraining environment. The experiment consisted of two parts. The first part was training, which was used to teach the input (Chinese fonts and sounds) to output (English translations) mapping of 16 Chinese characters. Training methods were manipulated in this part. A quarter of the subjects only received labeled training trials (English translation provided), the others received extra training trials with [[unlabeled examples|non-labeled trials]] (only the orthography or/and phonology without English translation). The non-labeled trials were further separated into three types: unpaired, correlated paired and uncorrelated paired, with each type used for one quarter of subjects.<br />
The second part was posttest, in which students produced the English translation when they saw the Chinese fonts or hear the Chinese sounds one by one. The accuracy of translation was recorded. It showed that [[unlabeled examples]] did help the learning, and uncorrelated paired examples did the best among all three types of unlabeled examples.<br />
<br />
== Glossary ==<br />
2. A glossary that defines terms used elsewhere in this node but not defined in the nodes that are parents, grandparents, etc. of this node; <br />
<br />
labeling; source pairing; source correlation.<br />
<br />
== Research question ==<br />
<br />
How native English speakers learn to speak and read Chinese under various coordinative learning conditions. <br />
<br />
== Background ==<br />
<br />
In machine learning research, it has been found that multiple-strategies and multiple modalities facilitate learning (Blum and Mitchell, 1998). However, the effectiveness of the properties of “co-training” theory have not been tested in human learners yet. We carried out this study to directly test two important properties of this theory in human learners. There are two results from the finished experiment and one non-result of interest. Most dramatic is the advantage of written over spoken input. This has nothing to do with co-training but is interesting and important for L2 word learning (translation). Second is the pairs effect, the advantage of spoken + written input presented during unlabelled training compared with either one separately. The independence of the surface features of these inputs (specific speaker, specific font) was not a factor.<br />
<br />
To understand the pairs effect, we have to know whether it is restricted to or larger for [[unlabeled examples|unlabeled trials]]. Experiment 1 did not manipulate pairing in labeled trials. In the fall of 2006, we tested the pairing property under both labeled and unlabeled trails.<br />
<br />
To understand the correlation feature better, we are testing the correlation feature in an in-vivo setup with more learning sessions.<br />
<br />
== Dependent variables ==<br />
<br />
[[Normal post-test]]: Accuracy of producing the English word under reading and/or listening situation.<br />
<br />
== Independent variables ==<br />
Labeling variable and correction variable.<br />
Four training conditions, between subject design. All subjects received 48 Labeled examples, then followed by<br />
A) none;<br />
B) 192 unpaired unlabeled examples;<br />
C) correlated paired unlabeled examples;<br />
D) uncorrelated paired unlabeled examples.<br />
[[Image:Study1.jpg]]<br />
<br />
== Hypothesis ==<br />
<br />
Pairing of visual font and auditory sound of Chinese characters should enhance learning under both labeled and unlabeled trials, but the benefit is most significant when the trials are unlabeled.<br />
*<br />
[[Image:cotraining1.jpg]]<br />
<br />
== Findings ==<br />
<br />
*“Unlabelled paired” trials may aid learning. Learning meanings was facilitated by the addition of unlabeled paired trials that did not provide meaning.<br />
**However, this unlabeled-trials effect was restricted to cross-modal pairs (spoken syllable and written character); it was absent when only one (spoken syllable) or the other (written character) modality was presented.<br />
**Implication: Cross-modal inputs in this situation can establish multiple representations (speech-writing pairs) from which meaning links are more readily retrieved.<br />
*Written form learned better than spoken form Large advantage for the presentation of written characters compared with their corresponding spoken syllables for learning a form-meaning pair.<br />
*Benefits of uncorrelated examples was not observed. <br />
**Correlated examples: Given font and given speaker always co-occur (conditional dependent)<br />
**Uncorrelated examples: Given font occurs with all speakers; and given speaker occurs with all fonts (conditional independent)<br />
**This is still being assessed by using multiple learning sessions. <br />
<br />
[[Image:cotraining2.jpg]]<br />
<br />
== Explanation ==<br />
<br />
Learning meanings was facilitated by the addition of unlabeled paired trials that did not provide meaning implicates that predictions of the label are generated for unlabeled trials, so they serve as self-generated labeled trials and work as meaningful materials for learning. This effect is especially significant in multiple input situation (paired trials) because the establishment of multiple representations (speech-writing pairs) makes the “label prediction” more accurate.<br />
<br />
== Descendents ==<br />
<br />
None.<br />
<br />
== Further information ==</div>Liuyinghttps://learnlab.org/wiki/index.php?title=File:Study1.JPG&diff=8513File:Study1.JPG2008-11-03T03:08:55Z<p>Liuying: </p>
<hr />
<div></div>Liuyinghttps://learnlab.org/wiki/index.php?title=Co-training_of_Chinese_characters&diff=8512Co-training of Chinese characters2008-11-03T03:04:49Z<p>Liuying: /* Independent variables */</p>
<hr />
<div>----<br />
'''Summary Table'''<br />
*Node Title: Learning to read Chinese: [[Co-training]] in human (Study 1)<br />
*Researchers: Ying Liu, Charles Perfetti, Susan Dunlap, Gusheng Zi, Tom Mitchell<br />
*PIs: Ying Liu, Charles Perfetti, Tom Mitchell<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Gusheng Zi<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2005<br />
*LearnLab Site and Courses: LRDC, pull out study<br />
*Number of Students: 44<br />
*Total Participant Hours for the study: 44<br />
*Data in the Data Shop: Yes<br />
----<br />
<br />
== Abstract ==<br />
The present study explored how native English speakers learn to speak and read Chinese in a cotraining environment. The experiment consisted of two parts. The first part was training, which was used to teach the input (Chinese fonts and sounds) to output (English translations) mapping of 16 Chinese characters. Training methods were manipulated in this part. A quarter of the subjects only received labeled training trials (English translation provided), the others received extra training trials with [[unlabeled examples|non-labeled trials]] (only the orthography or/and phonology without English translation). The non-labeled trials were further separated into three types: unpaired, correlated paired and uncorrelated paired, with each type used for one quarter of subjects.<br />
The second part was posttest, in which students produced the English translation when they saw the Chinese fonts or hear the Chinese sounds one by one. The accuracy of translation was recorded. It showed that [[unlabeled examples]] did help the learning, and uncorrelated paired examples did the best among all three types of unlabeled examples.<br />
<br />
== Glossary ==<br />
2. A glossary that defines terms used elsewhere in this node but not defined in the nodes that are parents, grandparents, etc. of this node; <br />
<br />
labeling; source pairing; source correlation.<br />
<br />
== Research question ==<br />
<br />
How native English speakers learn to speak and read Chinese under various coordinative learning conditions. <br />
<br />
== Background ==<br />
<br />
In machine learning research, it has been found that multiple-strategies and multiple modalities facilitate learning (Blum and Mitchell, 1998). However, the effectiveness of the properties of “co-training” theory have not been tested in human learners yet. We carried out this study to directly test two important properties of this theory in human learners. There are two results from the finished experiment and one non-result of interest. Most dramatic is the advantage of written over spoken input. This has nothing to do with co-training but is interesting and important for L2 word learning (translation). Second is the pairs effect, the advantage of spoken + written input presented during unlabelled training compared with either one separately. The independence of the surface features of these inputs (specific speaker, specific font) was not a factor.<br />
<br />
To understand the pairs effect, we have to know whether it is restricted to or larger for [[unlabeled examples|unlabeled trials]]. Experiment 1 did not manipulate pairing in labeled trials. In the fall of 2006, we tested the pairing property under both labeled and unlabeled trails.<br />
<br />
To understand the correlation feature better, we are testing the correlation feature in an in-vivo setup with more learning sessions.<br />
<br />
== Dependent variables ==<br />
<br />
[[Normal post-test]]: Accuracy of producing the English word under reading and/or listening situation.<br />
<br />
== Independent variables ==<br />
Labeling variable and correction variable.<br />
Four training conditions, between subject design. All subjects received 48 Labeled examples, then followed by<br />
A) none;<br />
B) 192 unpaired unlabeled examples;<br />
C) correlated paired unlabeled examples;<br />
D) uncorrelated paired unlabeled examples.<br />
[[Image:study1.jpg]]<br />
<br />
== Hypothesis ==<br />
<br />
Pairing of visual font and auditory sound of Chinese characters should enhance learning under both labeled and unlabeled trials, but the benefit is most significant when the trials are unlabeled.<br />
*<br />
[[Image:cotraining1.jpg]]<br />
<br />
== Findings ==<br />
<br />
*“Unlabelled paired” trials may aid learning. Learning meanings was facilitated by the addition of unlabeled paired trials that did not provide meaning.<br />
**However, this unlabeled-trials effect was restricted to cross-modal pairs (spoken syllable and written character); it was absent when only one (spoken syllable) or the other (written character) modality was presented.<br />
**Implication: Cross-modal inputs in this situation can establish multiple representations (speech-writing pairs) from which meaning links are more readily retrieved.<br />
*Written form learned better than spoken form Large advantage for the presentation of written characters compared with their corresponding spoken syllables for learning a form-meaning pair.<br />
*Benefits of uncorrelated examples was not observed. <br />
**Correlated examples: Given font and given speaker always co-occur (conditional dependent)<br />
**Uncorrelated examples: Given font occurs with all speakers; and given speaker occurs with all fonts (conditional independent)<br />
**This is still being assessed by using multiple learning sessions. <br />
<br />
[[Image:cotraining2.jpg]]<br />
<br />
== Explanation ==<br />
<br />
Learning meanings was facilitated by the addition of unlabeled paired trials that did not provide meaning implicates that predictions of the label are generated for unlabeled trials, so they serve as self-generated labeled trials and work as meaningful materials for learning. This effect is especially significant in multiple input situation (paired trials) because the establishment of multiple representations (speech-writing pairs) makes the “label prediction” more accurate.<br />
<br />
== Descendents ==<br />
<br />
None.<br />
<br />
== Further information ==</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Co-training_of_Chinese_characters&diff=8511Co-training of Chinese characters2008-11-02T21:00:34Z<p>Liuying: /* Independent variables */</p>
<hr />
<div>----<br />
'''Summary Table'''<br />
*Node Title: Learning to read Chinese: [[Co-training]] in human (Study 1)<br />
*Researchers: Ying Liu, Charles Perfetti, Susan Dunlap, Gusheng Zi, Tom Mitchell<br />
*PIs: Ying Liu, Charles Perfetti, Tom Mitchell<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Gusheng Zi<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2005<br />
*LearnLab Site and Courses: LRDC, pull out study<br />
*Number of Students: 44<br />
*Total Participant Hours for the study: 44<br />
*Data in the Data Shop: Yes<br />
----<br />
<br />
== Abstract ==<br />
The present study explored how native English speakers learn to speak and read Chinese in a cotraining environment. The experiment consisted of two parts. The first part was training, which was used to teach the input (Chinese fonts and sounds) to output (English translations) mapping of 16 Chinese characters. Training methods were manipulated in this part. A quarter of the subjects only received labeled training trials (English translation provided), the others received extra training trials with [[unlabeled examples|non-labeled trials]] (only the orthography or/and phonology without English translation). The non-labeled trials were further separated into three types: unpaired, correlated paired and uncorrelated paired, with each type used for one quarter of subjects.<br />
The second part was posttest, in which students produced the English translation when they saw the Chinese fonts or hear the Chinese sounds one by one. The accuracy of translation was recorded. It showed that [[unlabeled examples]] did help the learning, and uncorrelated paired examples did the best among all three types of unlabeled examples.<br />
<br />
== Glossary ==<br />
2. A glossary that defines terms used elsewhere in this node but not defined in the nodes that are parents, grandparents, etc. of this node; <br />
<br />
labeling; source pairing; source correlation.<br />
<br />
== Research question ==<br />
<br />
How native English speakers learn to speak and read Chinese under various coordinative learning conditions. <br />
<br />
== Background ==<br />
<br />
In machine learning research, it has been found that multiple-strategies and multiple modalities facilitate learning (Blum and Mitchell, 1998). However, the effectiveness of the properties of “co-training” theory have not been tested in human learners yet. We carried out this study to directly test two important properties of this theory in human learners. There are two results from the finished experiment and one non-result of interest. Most dramatic is the advantage of written over spoken input. This has nothing to do with co-training but is interesting and important for L2 word learning (translation). Second is the pairs effect, the advantage of spoken + written input presented during unlabelled training compared with either one separately. The independence of the surface features of these inputs (specific speaker, specific font) was not a factor.<br />
<br />
To understand the pairs effect, we have to know whether it is restricted to or larger for [[unlabeled examples|unlabeled trials]]. Experiment 1 did not manipulate pairing in labeled trials. In the fall of 2006, we tested the pairing property under both labeled and unlabeled trails.<br />
<br />
To understand the correlation feature better, we are testing the correlation feature in an in-vivo setup with more learning sessions.<br />
<br />
== Dependent variables ==<br />
<br />
[[Normal post-test]]: Accuracy of producing the English word under reading and/or listening situation.<br />
<br />
== Independent variables ==<br />
Labeling variable and correction variable.<br />
Four training conditions, between subject design. All subjects received 48 Labeled examples, then followed by<br />
A) none;<br />
B) 192 unpaired unlabeled examples;<br />
B) correlated paired unlabeled examples;<br />
D) uncorrelated paired unlabeled examples<br />
<br />
== Hypothesis ==<br />
<br />
Pairing of visual font and auditory sound of Chinese characters should enhance learning under both labeled and unlabeled trials, but the benefit is most significant when the trials are unlabeled.<br />
*<br />
[[Image:cotraining1.jpg]]<br />
<br />
== Findings ==<br />
<br />
*“Unlabelled paired” trials may aid learning. Learning meanings was facilitated by the addition of unlabeled paired trials that did not provide meaning.<br />
**However, this unlabeled-trials effect was restricted to cross-modal pairs (spoken syllable and written character); it was absent when only one (spoken syllable) or the other (written character) modality was presented.<br />
**Implication: Cross-modal inputs in this situation can establish multiple representations (speech-writing pairs) from which meaning links are more readily retrieved.<br />
*Written form learned better than spoken form Large advantage for the presentation of written characters compared with their corresponding spoken syllables for learning a form-meaning pair.<br />
*Benefits of uncorrelated examples was not observed. <br />
**Correlated examples: Given font and given speaker always co-occur (conditional dependent)<br />
**Uncorrelated examples: Given font occurs with all speakers; and given speaker occurs with all fonts (conditional independent)<br />
**This is still being assessed by using multiple learning sessions. <br />
<br />
[[Image:cotraining2.jpg]]<br />
<br />
== Explanation ==<br />
<br />
Learning meanings was facilitated by the addition of unlabeled paired trials that did not provide meaning implicates that predictions of the label are generated for unlabeled trials, so they serve as self-generated labeled trials and work as meaningful materials for learning. This effect is especially significant in multiple input situation (paired trials) because the establishment of multiple representations (speech-writing pairs) makes the “label prediction” more accurate.<br />
<br />
== Descendents ==<br />
<br />
None.<br />
<br />
== Further information ==</div>Liuyinghttps://learnlab.org/wiki/index.php?title=File:Learningresult.jpg&diff=8277File:Learningresult.jpg2008-09-29T00:15:55Z<p>Liuying: </p>
<hr />
<div></div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=8276Integrated Learning of Chinese2008-09-29T00:07:28Z<p>Liuying: </p>
<hr />
<div>--------<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Qun (Connie) Guan, Suemei Wu, Min Wang<br />
*PIs: Ying Liu, Charles Perfetti<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date July 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical [[knowledge components]] that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the [[integration]] by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
*How does [[integration]] of language constituents lead to [[robust learning]]?<br />
*Does writing Chinese lead to better integration and more robust Chinese reading?<br />
*Does the combination of writing and typing lead to more robust learning via better integration? <br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
*Accuracy rates of the lexical decision task<br />
*Accuracy rates on the partial-cue character recognition task<br />
*Learning gains (Pretest vs. Posttest) on meaning translation, pinyin and tone <br />
<br />
<br />
== Independent variables ==<br />
*Integration of reading and writing vs. reading only<br />
[[Image:LearningCondition2.jpg]]<br />
*Production with automated feedback, production only, vs. no production<br />
*Early integration vs. late integration<br />
<br />
== Hypothesis ==<br />
*General: Instructional Events that integrate receptive and productive components lead to robust representations of Chinese characters.<br />
*Specific: Writing characters provides a perceptual motor representation of graphic form that further supports the recognition of these forms. <br />
<br />
<br />
== Findings ==<br />
*The accuracy rates of the lexical decision task on the learned characters showed a significant main effect of learning condition [F (2, 56) = 4.79, MSE = .11, p = .01]. There was also a significant presentation order by learning condition interaction [F (2, 56) = 4.79, MSE = .11, p = .01]. The partial character recognition task did not show a learning condition main effect, but a significant presentation order by learning condition interaction. [F (2, 56) = 9.59, MSE = .05, p < .001]. When encountering the reading only condition first, the participants’ accuracy rate on the characters learned in the writing condition were 11% higher than reading only condition [t (13) = 3.75, p = .01, 2-tailed].<br />
<br />
[[Image:Learningresult.jpg]]<br />
<br />
<br />
*A second study is being designed at this moment to continue exploring the integration hypothesis. It will compare three methods: handwriting, Pinyin based computer typing, and both. Handwriting focuses on the semantic-orthography connections, whereas pinyin typing focuses on the semantic-phonology connection. We hypothesize that the combination of handwriting and pinyin typing can facilitate the integration of constituents. Theoretical framework and practical suggestions will be given on the learning of Chinese handwriting and typing in a modern technology rich learning environment.<br />
<br />
== Explanation ==<br />
*These studies test hypotheses about the effectiveness of targeted integrated instruction which are based theories from both Refinement and fluency cluster and Coordinative cluster. Each study has a specific rationale. (That is, integration is not a general virtue, but inherits effectiveness according to specific assumptions about the relation of component knowledge to specific tasks of reading, perception, and production.) The studies share a general approach in the use of the Integrated Chinese Tutor (ITC). In terms of the [[assistance dimension]], our assumption is the initial learning of a novel orthography along with a new phonological system places high demands on novice learners. The decomposition strategy represents a high level of [[assistance]] at this stage of learning. We are not testing the implication that, at advanced stages of learning, students might benefit from less assistance in the form of non-decomposed language units.<br />
*The current results suggested that writing conditions provided better learning than reading only conditions because of better orthography-semantic integration. The proposed project will continue to test the hypothesis of constituency integration by including both handwriting and pinyin typing in learning. <br />
<br />
== Descendants ==<br />
None.<br />
<br />
== Further information ==<br />
*Perfetti, C.A., Liu, Y., & Tan, L.H (2005). The Lexical Constituency Model: Some Implications of Research on Chinese for General Theories of Reading. Psychological Review, 112, 43-59.<br />
*Liu, Y., Wang, M., Perfetti, C.A. (2007) Threshold-Style Processing of Chinese Characters for Adult Second Language Learners. Memory and Cognition, .<br />
*Liu, Perfetti, C.A., & Wang, M. (2006) Visual Analysis and Lexical Access of Chinese characters by Chinese as Second Language Readers. Linguistic and Language, 7(3), 637-657.<br />
*Massaro, D. W., Liu, Y., Chen, T. H., & Perfetti, C. A. (2006). A Multilingual Embodied Conversational Agent for Tutoring Speech and Language Learning. Proceedings of the Ninth International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP, September, Pittsburgh, PA), 825-828.Universität Bonn, Bonn, Germany.<br />
<br />
Updated Sep 28, 2008</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=8275Integrated Learning of Chinese2008-09-28T23:18:27Z<p>Liuying: </p>
<hr />
<div>--------<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu<br />
*PIs: Ying Liu, Charles Perfetti, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date July 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical [[knowledge components]] that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the [[integration]] by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
*How does [[integration]] of language constituents lead to [[robust learning]]?<br />
*Does writing Chinese lead to better integration and more robust Chinese reading?<br />
*Does the combination of writing and typing lead to more robust learning via better integration? <br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
*Accuracy rates of the lexical decision task<br />
*Accuracy rates on the partial-cue character recognition task<br />
*Learning gains (Pretest vs. Posttest) on meaning translation, pinyin and tone <br />
<br />
<br />
== Independent variables ==<br />
*Integration of reading and writing vs. reading only<br />
[[Image:LearningCondition2.jpg]]<br />
*Production with automated feedback, production only, vs. no production<br />
*Early integration vs. late integration<br />
<br />
== Hypothesis ==<br />
*General: Instructional Events that integrate receptive and productive components lead to robust representations of Chinese characters.<br />
*Specific: Writing characters provides a perceptual motor representation of graphic form that further supports the recognition of these forms. <br />
<br />
<br />
== Findings ==<br />
The accuracy rates of the lexical decision task on the learned characters showed a significant main effect of learning condition [F (2, 56) = 4.79, MSE = .11, p = .01]. There was also a significant presentation order by learning condition interaction [F (2, 56) = 4.79, MSE = .11, p = .01]. The partial character recognition task did not show a learning condition main effect, but a significant presentation order by learning condition interaction. [F (2, 56) = 9.59, MSE = .05, p < .001]. When encountering the reading only condition first, the participants’ accuracy rate on the characters learned in the writing condition were 11% higher than reading only condition [t (13) = 3.75, p = .01, 2-tailed].<br />
<br />
== Explanation ==<br />
*These studies test hypotheses about the effectiveness of targeted integrated instruction which are based theories from both Refinement and fluency cluster and Coordinative cluster. Each study has a specific rationale. (That is, integration is not a general virtue, but inherits effectiveness according to specific assumptions about the relation of component knowledge to specific tasks of reading, perception, and production.) The studies share a general approach in the use of the Integrated Chinese Tutor (ITC). In terms of the [[assistance dimension]], our assumption is the initial learning of a novel orthography along with a new phonological system places high demands on novice learners. The decomposition strategy represents a high level of [[assistance]] at this stage of learning. We are not testing the implication that, at advanced stages of learning, students might benefit from less assistance in the form of non-decomposed language units.<br />
*The current results suggested that writing conditions provided better learning than reading only conditions because of better orthography-semantic integration. The proposed project will continue to test the hypothesis of constituency integration by including both handwriting and pinyin typing in learning. <br />
== Descendants ==<br />
None.<br />
== Further information ==<br />
*Perfetti, C.A., Liu, Y., & Tan, L.H (2005). The Lexical Constituency Model: Some Implications of Research on Chinese for General Theories of Reading. Psychological Review, 112, 43-59.<br />
*Liu, Y., Wang, M., Perfetti, C.A. (2007) Threshold-Style Processing of Chinese Characters for Adult Second Language Learners. Memory and Cognition, .<br />
*Liu, Perfetti, C.A., & Wang, M. (2006) Visual Analysis and Lexical Access of Chinese characters by Chinese as Second Language Readers. Linguistic and Language, 7(3), 637-657.<br />
*Massaro, D. W., Liu, Y., Chen, T. H., & Perfetti, C. A. (2006). A Multilingual Embodied Conversational Agent for Tutoring Speech and Language Learning. Proceedings of the Ninth International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP, September, Pittsburgh, PA), 825-828.Universität Bonn, Bonn, Germany.<br />
<br />
Updated Sep 28, 2008</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=8274Integrated Learning of Chinese2008-09-28T15:45:53Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu<br />
*PIs: Ying Liu, Charles Perfetti, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date July 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical [[knowledge components]] that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the [[integration]] by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
*How does [[integration]] of language constituents lead to [[robust learning]]?<br />
*Does writing Chinese lead to better integration and more robust Chinese reading?<br />
*Does the combination of writing and typing lead to more robust learning via better integration? <br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
*[[Normal post-test]]: measures of <i> partial character recognition </i> (i.e., the accuracy and response time on recognizing the partial characters, the whole of which the students have learned in training), <i> lexical decision </i> (i.e., the accuracy and response time on deciding whether the character is a real character or not) and <i> dictation task </i> (i.e. the quality of character-writing given the English meaning cues or the sound cues of the characters)<br />
*[[Transfer]]: measure of <i> lexical decision </i> (see the definition above) on the novel characters<br />
<br />
== Independent variables ==<br />
*Integration of reading and writing vs. reading only<br />
[[Image:LearningCondition2.jpg]]<br />
*Production with automated feedback, production only, vs. no production<br />
*Early integration vs. late integration<br />
<br />
== Hypothesis ==<br />
*Integration is crucial for robust learning; <br />
*Integration is most robust when the involved knowledge components are already refined.<br />
<br />
== Findings ==<br />
The first experiment is under programming. Testing of students will start in late March of 2008.<br />
<br />
== Explanation ==<br />
These studies test hypotheses about the effectiveness of targeted integrated instruction which are based theories from both Refinement and fluency cluster and Coordinative cluster. Each study has a specific rationale. (That is, integration is not a general virtue, but inherits effectiveness according to specific assumptions about the relation of component knowledge to specific tasks of reading, perception, and production.) The studies share a general approach in the use of the Integrated Chinese Tutor (ITC). In terms of the [[assistance dimension]], our assumption is the initial learning of a novel orthography along with a new phonological system places high demands on novice learners. The decomposition strategy represents a high level of [[assistance]] at this stage of learning. We are not testing the implication that, at advanced stages of learning, students might benefit from less assistance in the form of non-decomposed language units. <br />
== Descendants ==<br />
None.<br />
== Further information ==<br />
*Perfetti, C.A., Liu, Y., & Tan, L.H (2005). The Lexical Constituency Model: Some Implications of Research on Chinese for General Theories of Reading. Psychological Review, 112, 43-59.<br />
*Liu, Y., Wang, M., Perfetti, C.A. (2007) Threshold-Style Processing of Chinese Characters for Adult Second Language Learners. Memory and Cognition, .<br />
*Liu, Perfetti, C.A., & Wang, M. (2006) Visual Analysis and Lexical Access of Chinese characters by Chinese as Second Language Readers. Linguistic and Language, 7(3), 637-657.<br />
*Massaro, D. W., Liu, Y., Chen, T. H., & Perfetti, C. A. (2006). A Multilingual Embodied Conversational Agent for Tutoring Speech and Language Learning. Proceedings of the Ninth International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP, September, Pittsburgh, PA), 825-828.Universität Bonn, Bonn, Germany.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=8273Integrated Learning of Chinese2008-09-28T15:45:16Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu<br />
*PIs: Ying Liu, Charles Perfetti, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date July 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical [[knowledge components]] that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the [[integration]] by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
How does [[integration]] of language constituents lead to [[robust learning]]?<br />
Does writing Chinese lead to better integration and more robust Chinese reading?<br />
Does the combination of writing and typing lead to more robust learning via better integration? <br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
*[[Normal post-test]]: measures of <i> partial character recognition </i> (i.e., the accuracy and response time on recognizing the partial characters, the whole of which the students have learned in training), <i> lexical decision </i> (i.e., the accuracy and response time on deciding whether the character is a real character or not) and <i> dictation task </i> (i.e. the quality of character-writing given the English meaning cues or the sound cues of the characters)<br />
*[[Transfer]]: measure of <i> lexical decision </i> (see the definition above) on the novel characters<br />
<br />
== Independent variables ==<br />
*Integration of reading and writing vs. reading only<br />
[[Image:LearningCondition2.jpg]]<br />
*Production with automated feedback, production only, vs. no production<br />
*Early integration vs. late integration<br />
<br />
== Hypothesis ==<br />
*Integration is crucial for robust learning; <br />
*Integration is most robust when the involved knowledge components are already refined.<br />
<br />
== Findings ==<br />
The first experiment is under programming. Testing of students will start in late March of 2008.<br />
<br />
== Explanation ==<br />
These studies test hypotheses about the effectiveness of targeted integrated instruction which are based theories from both Refinement and fluency cluster and Coordinative cluster. Each study has a specific rationale. (That is, integration is not a general virtue, but inherits effectiveness according to specific assumptions about the relation of component knowledge to specific tasks of reading, perception, and production.) The studies share a general approach in the use of the Integrated Chinese Tutor (ITC). In terms of the [[assistance dimension]], our assumption is the initial learning of a novel orthography along with a new phonological system places high demands on novice learners. The decomposition strategy represents a high level of [[assistance]] at this stage of learning. We are not testing the implication that, at advanced stages of learning, students might benefit from less assistance in the form of non-decomposed language units. <br />
== Descendants ==<br />
None.<br />
== Further information ==<br />
*Perfetti, C.A., Liu, Y., & Tan, L.H (2005). The Lexical Constituency Model: Some Implications of Research on Chinese for General Theories of Reading. Psychological Review, 112, 43-59.<br />
*Liu, Y., Wang, M., Perfetti, C.A. (2007) Threshold-Style Processing of Chinese Characters for Adult Second Language Learners. Memory and Cognition, .<br />
*Liu, Perfetti, C.A., & Wang, M. (2006) Visual Analysis and Lexical Access of Chinese characters by Chinese as Second Language Readers. Linguistic and Language, 7(3), 637-657.<br />
*Massaro, D. W., Liu, Y., Chen, T. H., & Perfetti, C. A. (2006). A Multilingual Embodied Conversational Agent for Tutoring Speech and Language Learning. Proceedings of the Ninth International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP, September, Pittsburgh, PA), 825-828.Universität Bonn, Bonn, Germany.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=6738Integrated Learning of Chinese2008-01-11T14:35:24Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu<br />
*PIs: Ying Liu, Charles Perfetti, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date Dec 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical [[knowledge components]] that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the [[integration]] by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
How does [[integration]] of language constituents lead to [[robust learning]]?<br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
*[[Normal post-test]]: measures of partial character recognition, lexical decision and translation<br />
*[[Transfer]]: measures of novel character decision and translation<br />
<br />
== Independent variables ==<br />
*Integration of reading and writing vs. reading only<br />
*Production with automated feedback, production only, vs. no production<br />
*Early integration vs. late integration<br />
<br />
== Hypothesis ==<br />
*Integration is crucial for robust learning; <br />
*Integration is most robust when the involved knowledge components are already refined.<br />
<br />
== Findings ==<br />
The first experiment is under programming. Testing of students will start in early March of 2008.<br />
== Explanation ==<br />
These studies test hypotheses about the effectiveness of targeted integrated instruction which are based theories from both Refinement and fluency cluster and Coordinative cluster. Each study has a specific rationale. (That is, integration is not a general virtue, but inherits effectiveness according to specific assumptions about the relation of component knowledge to specific tasks of reading, perception, and production.) The studies share a general approach in the use of the Integrated Chinese Tutor (ITC). In terms of the [[assistance dimension]], our assumption is the initial learning of a novel orthography along with a new phonological system places high demands on novice learners. The decomposition strategy represents a high level of [[assistance]] at this stage of learning. We are not testing the implication that, at advanced stages of learning, students might benefit from less assistance in the form of non-decomposed language units. <br />
== Descendants ==<br />
None.<br />
== Further information ==<br />
*Perfetti, C.A., Liu, Y., & Tan, L.H (2005). The Lexical Constituency Model: Some Implications of Research on Chinese for General Theories of Reading. Psychological Review, 112, 43-59.<br />
*Liu, Y., Wang, M., Perfetti, C.A. (2007) Threshold-Style Processing of Chinese Characters for Adult Second Language Learners. Memory and Cognition, .<br />
*Liu, Perfetti, C.A., & Wang, M. (2006) Visual Analysis and Lexical Access of Chinese characters by Chinese as Second Language Readers. Linguistic and Language, 7(3), 637-657.<br />
*Massaro, D. W., Liu, Y., Chen, T. H., & Perfetti, C. A. (2006). A Multilingual Embodied Conversational Agent for Tutoring Speech and Language Learning. Proceedings of the Ninth International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP, September, Pittsburgh, PA), 825-828.Universität Bonn, Bonn, Germany.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=6737Integrated Learning of Chinese2008-01-11T14:31:08Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu<br />
*PIs: Ying Liu, Charles Perfetti, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date Dec 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical [[knowledge components]] that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the [[integration]] by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
How does [[integration]] of language constituents lead to [[robust learning]]?<br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
*[[Normal post-test]]: measures of partial character recognition, lexical decision and translation tasks<br />
*[[Transfer]]: novel character decision and translation<br />
== Independent variables ==<br />
*Integration of reading and writing vs. reading only<br />
*Production with automated feedback, production only, vs. no production<br />
<br />
== Hypothesis ==<br />
*Integration is crucial for robust learning; <br />
*Integration is most robust when the involved knowledge components are already refined.<br />
<br />
== Findings ==<br />
The first experiment is under programming. Testing of students will start in early March of 2008.<br />
== Explanation ==<br />
These studies test hypotheses about the effectiveness of targeted integrated instruction which are based theories from both Refinement and fluency cluster and Coordinative cluster. Each study has a specific rationale. (That is, integration is not a general virtue, but inherits effectiveness according to specific assumptions about the relation of component knowledge to specific tasks of reading, perception, and production.) The studies share a general approach in the use of the Integrated Chinese Tutor (ITC). In terms of the [[assistance dimension]], our assumption is the initial learning of a novel orthography along with a new phonological system places high demands on novice learners. The decomposition strategy represents a high level of [[assistance]] at this stage of learning. We are not testing the implication that, at advanced stages of learning, students might benefit from less assistance in the form of non-decomposed language units. <br />
== Descendants ==<br />
None.<br />
== Further information ==<br />
*Perfetti, C.A., Liu, Y., & Tan, L.H (2005). The Lexical Constituency Model: Some Implications of Research on Chinese for General Theories of Reading. Psychological Review, 112, 43-59.<br />
*Liu, Y., Wang, M., Perfetti, C.A. (2007) Threshold-Style Processing of Chinese Characters for Adult Second Language Learners. Memory and Cognition, .<br />
*Liu, Perfetti, C.A., & Wang, M. (2006) Visual Analysis and Lexical Access of Chinese characters by Chinese as Second Language Readers. Linguistic and Language, 7(3), 637-657.<br />
*Massaro, D. W., Liu, Y., Chen, T. H., & Perfetti, C. A. (2006). A Multilingual Embodied Conversational Agent for Tutoring Speech and Language Learning. Proceedings of the Ninth International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP, September, Pittsburgh, PA), 825-828.Universität Bonn, Bonn, Germany.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=6736Integrated Learning of Chinese2008-01-11T14:30:45Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu<br />
*PIs: Ying Liu, Charles Perfetti, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date Dec 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: experiments have not started yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical [[knowledge components]] that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the [[integration]] by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
How does [[integration]] of language constituents lead to [[robust learning]]?<br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, 2007; Liu, Perfetti, and Wang, 2006), tone perception (Wang et al, under review), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
*[[Normal post-test]]: measures of partial character recognition, lexical decision and translation tasks<br />
*[[Transfer]]: novel character decision and translation<br />
== Independent variables ==<br />
*Integration of reading and writing vs. reading only<br />
*Production with automated feedback, production only, vs. no production<br />
<br />
== Hypothesis ==<br />
*Integration is crucial for robust learning; <br />
*Integration is most robust when the involved knowledge components are already refined.<br />
<br />
== Findings ==<br />
The first experiment is under programming. Testing of students will start in early March of 2008.<br />
== Explanation ==<br />
These studies test hypotheses about the effectiveness of targeted integrated instruction which are based theories from both Refinement and fluency cluster and Coordinative cluster. Each study has a specific rationale. (That is, integration is not a general virtue, but inherits effectiveness according to specific assumptions about the relation of component knowledge to specific tasks of reading, perception, and production.) The studies share a general approach in the use of the Integrated Chinese Tutor (ITC). In terms of the [[assistance dimension]], our assumption is the initial learning of a novel orthography along with a new phonological system places high demands on novice learners. The decomposition strategy represents a high level of [[assistance]] at this stage of learning. We are not testing the implication that, at advanced stages of learning, students might benefit from less assistance in the form of non-decomposed language units. <br />
== Descendants ==<br />
None.<br />
== Further information ==<br />
Perfetti, C.A., Liu, Y., & Tan, L.H (2005). The Lexical Constituency Model: Some Implications of Research on Chinese for General Theories of Reading. Psychological Review, 112, 43-59.<br />
Liu, Y., Wang, M., Perfetti, C.A. (2007) Threshold-Style Processing of Chinese Characters for Adult Second Language Learners. Memory and Cognition, .<br />
Liu, Perfetti, C.A., & Wang, M. (2006) Visual Analysis and Lexical Access of Chinese characters by Chinese as Second Language Readers. Linguistic and Language, 7(3), 637-657.<br />
Massaro, D. W., Liu, Y., Chen, T. H., & Perfetti, C. A. (2006). A Multilingual Embodied Conversational Agent for Tutoring Speech and Language Learning. Proceedings of the Ninth International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP, September, Pittsburgh, PA), 825-828.Universität Bonn, Bonn, Germany.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=6735Integrated Learning of Chinese2008-01-11T04:43:57Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu<br />
*PIs: Ying Liu, Charles Perfetti, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date Dec 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: not yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical [[knowledge components]] that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the [[integration]] by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
How does [[integration]] of language constituents lead to [[robust learning]]?<br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, in press), tone perception (Wang et al, in preparation), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and [[cotraining]] of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
*[[Normal post-test]]: measures of partial character recognition, lexical decision and translation tasks<br />
*[[Transfer]]: novel character decision and translation<br />
== Independent variables ==<br />
*Integration of reading and writing vs. reading only<br />
*Production with automated feedback, production only, vs. no production<br />
<br />
== Hypothesis ==<br />
*Integration is crucial for robust learning; <br />
*Integration is most robust when the involved knowledge components are already refined.<br />
<br />
== Findings ==<br />
The first experiment is under programming. Testing of students will start in early March of 2008.<br />
== Explanation ==<br />
These studies test hypotheses about the effectiveness of targeted integrated instruction which are based theories from both Refinement and fluency cluster and Coordinative cluster. Each study has a specific rationale. (That is, integration is not a general virtue, but inherits effectiveness according to specific assumptions about the relation of component knowledge to specific tasks of reading, perception, and production.) The studies share a general approach in the use of the Integrated Chinese Tutor (ITC). In terms of the [[assistance dimension]], our assumption is the initial learning of a novel orthography along with a new phonological system places high demands on novice learners. The decomposition strategy represents a high level of [[assistance]] at this stage of learning. We are not testing the implication that, at advanced stages of learning, students might benefit from less assistance in the form of non-decomposed language units. <br />
== Descendants ==<br />
None.<br />
== Further information ==</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=6734Integrated Learning of Chinese2008-01-11T04:40:06Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu<br />
*PIs: Ying Liu, Charles Perfetti, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date Dec 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: not yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical [[knowledge components]] that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the [[integration]] by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
How does [[integration]] of language constituents lead to [[robust learning]]?<br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, in press), tone perception (Wang et al, in preparation), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and cotraining of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
*[[Normal post-test]]: measures of partial character recognition, lexical decision and translation tasks<br />
*[[Transfer]]: novel character decision and translation<br />
== Independent variables ==<br />
*Integration of reading and writing vs. reading only<br />
*Production with automated feedback, production only, vs. no production<br />
<br />
== Hypothesis ==<br />
*Integration is crucial for robust learning; <br />
*Integration is most robust when the involved knowledge components are already refined.<br />
<br />
== Findings ==<br />
The first experiment is under programming. Testing of students will start in early March of 2008.<br />
== Explanation ==<br />
These studies test hypotheses about the effectiveness of targeted integrated instruction which are based theories from both Refinement and fluency cluster and Coordinative cluster. Each study has a specific rationale. (That is, integration is not a general virtue, but inherits effectiveness according to specific assumptions about the relation of component knowledge to specific tasks of reading, perception, and production.) The studies share a general approach in the use of the ICT. In terms of the assistance dimension, our assumption is the initial learning of a novel orthography along with a new phonological system places high demands on novice learners. The decomposition strategy represents a high level of assistance at this stage of learning. We are not testing the implication that, at advanced stages of learning, students might benefit from less assistance in the form of non-decomposed language units. <br />
== Descendents ==<br />
None.<br />
== Further information ==</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=6733Integrated Learning of Chinese2008-01-11T04:05:49Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu<br />
*PIs: Ying Liu, Charles Perfetti, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date Dec 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: not yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical [[knowledge components]] that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the [[integration]] by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
How does [[integration]] of language constituents lead to [[robust learning]]?<br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, in press), tone perception (Wang et al, in preparation), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and cotraining of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
*[[Normal post-test]]: measures of partial character recognition, lexical decision and translation tasks<br />
*[[Transfer]]: novel character decision and translation<br />
== Independent variables ==<br />
*Integration of reading and writing vs. reading only<br />
*Production with automated feedback, production only, vs. no production<br />
*<br />
== Hypothesis ==<br />
*Integration is crucial for robust learning; <br />
*Integration is most robust when the involved knowledge components are already refined.<br />
<br />
== Findings ==<br />
<br />
== Explanation ==<br />
<br />
== Descendents ==<br />
<br />
== Further information ==</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=6732Integrated Learning of Chinese2008-01-11T03:48:09Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu<br />
*PIs: Ying Liu, Charles Perfetti, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date Dec 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: not yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical [[knowledge components]] that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the [[integration]] by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
How does [[integration]] of language constituents lead to [[robust learning]]?<br />
<br />
== Background ==<br />
* Our previous work on Chinese learning has focused separately on character reading (Liu, Wang, and Perfetti, in press), tone perception (Wang et al, in preparation), syllable production with “talking head” (Massaro, Liu, Chen, & Perfetti, 2006), and cotraining of characters (Liu, Perfetti, and Mitchell, in preparation). Most of above studies were implemented through PSLC Chinese online course, and we will continue to do so for all studies in the present project plan.<br />
*There have been various findings from above studies. The character reading study found that explicit learning of radicals facilitates the learning of character meaning. Tone perception study found that visual contour plus pinyin provided the best learning curve over one semester. Syllable production study suggested that the synthetic talking head “Bao” provided larger improvement on vowel production than audio only. The [[cotraining]] study showed significant advantage for “paired” learning, in which both visual font and auditory sound of a character were presented sequentially in one trial.<br />
<br />
<br />
== Dependent variables ==<br />
<br />
[[Normal post-test]]: measures of partial character recognition, lexical decision and translation tasks<br />
[[Transfer]]: novel character decision and translation<br />
== Independent variables ==<br />
*Integration of reading and writing vs. reading only<br />
*<br />
== Hypothesis ==<br />
*Integration is crucial for robust learning; <br />
*Integration is most robust when the involved knowledge components are already refined.<br />
<br />
== Findings ==<br />
<br />
== Explanation ==<br />
<br />
== Descendents ==<br />
<br />
== Further information ==</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=6727Integrated Learning of Chinese2008-01-11T03:34:02Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu<br />
*PIs: Ying Liu, Charles Perfetti, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date Dec 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: not yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical [[knowledge components]] that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the [[integration]] by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
How does [[integration]] of language constituents lead to [[robust learning]]?<br />
<br />
== Background ==<br />
* <br />
*<br />
*<br />
<br />
== Dependent variables ==<br />
<br />
[[Normal post-test]]: <br />
[[Transfer]]: <br />
== Independent variables ==<br />
<br />
<br />
== Hypothesis ==<br />
<br />
<br />
== Findings ==<br />
<br />
== Explanation ==<br />
<br />
== Descendents ==<br />
<br />
== Further information ==</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Integrated_Learning_of_Chinese&diff=6724Integrated Learning of Chinese2008-01-11T00:49:27Z<p>Liuying: New page: ---- Summary table *Node Title: Integrated Learning of Chinese: reading, perception and production *Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu *PIs: Ying Liu, Charles Per...</p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Integrated Learning of Chinese: reading, perception and production<br />
*Researchers: Ying Liu, Charles Perfetti, Min Wang, Suemei Wu<br />
*PIs: Ying Liu, Charles Perfetti, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Connie Guan<br />
*Graduate Students: Derek Chan<br />
*Study Start Date Jan 1, 2008<br />
*Study End Date Dec 31, 2009<br />
*LearnLab Site and Courses , CMU Chinese (Classroom and Online)<br />
*Number of Students: 100<br />
*Planned Participant Hours for the study: 300<br />
*Data in the Data Shop: not yet<br />
----<br />
== Abstract ==<br />
*Learning second language is a challenge to learners. It is more so for English speakers to learn Chinese. The unique Chinese character writing system and tonal features are fundamentally different from English and thus presents a unique obstacle to learning by English speakers. In our model of reading Chinese, orthography, phonology and meaning are universal constituents and critical knowledge components that should be learned and integrated (Perfetti, Liu, and Tan, 2005). Working together with the CMU Chinese online course, the present studies will explore how to facilitate the integration by training both perception and production skills. The specific methods to be tested will be using multiple learning systems including learning orthography, pronunciation and meaning together through complementary visual, auditory and motor modalities. Integration factors affect the learning curve are examined in three studies. <br />
<br />
<br />
== Glossary ==<br />
Integration; Constituents; Orthography; Phonology; Meaning<br />
<br />
<br />
== Research question ==<br />
How does integration of language constituents lead to robust learning?<br />
<br />
== Background ==<br />
* <br />
*<br />
*<br />
<br />
== Dependent variables ==<br />
<br />
[[Normal post-test]]: <br />
[[Transfer]]: <br />
== Independent variables ==<br />
<br />
<br />
== Hypothesis ==<br />
<br />
<br />
== Findings ==<br />
<br />
== Explanation ==<br />
<br />
== Descendents ==<br />
<br />
== Further information ==</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Refinement_and_Fluency&diff=6723Refinement and Fluency2008-01-11T00:36:03Z<p>Liuying: /* Knowledge accessibility */</p>
<hr />
<div>= The PSLC Refinement and Fluency cluster =<br />
<br />
== Abstract ==<br />
The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br />
<br />
1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br />
<br />
2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br />
<br />
3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br />
<br />
4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information ("facts") or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br />
<br />
5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br />
<br />
6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br />
<br />
7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br />
<br />
8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br />
<br />
9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br />
<br />
10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br />
<br />
The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br />
<br />
This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br />
<br />
<br><center>[[Image:rf-theory.jpg]]</center><br />
<br />
==Significance==<br />
A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br />
<br />
== Glossary ==<br />
[[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br />
<br />
== Research question ==<br />
The overall research question is how can instruction optimally support the acquisition, refinement, and fluent use of complex targeted knowledge, taking into account the learner’s existing knowledge in relation to the knowledge demands of the target domain? In examining this general question, the studies focus on features of the learning situation, including the following: the cognitive demands of targeted knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br />
<br />
== Independent variables ==<br />
At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br />
<br />
== Dependent variables ==<br />
The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br />
<br />
== Hypotheses ==<br />
Instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. More specifically, the initial acquisition of knowledge and its refinement benefit from instructional activities that require the learner to encode the relevant knowledge components by attending to [[valid features]] of the learning content. The fluency corollary: <br />
<br />
<br />
Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br />
<br />
1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br />
<br />
2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby "divergent coding systems" here may be the same as "multiple input sources" in co-training.)<br />
<br />
3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br />
<br />
4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br />
<br />
5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br />
<br />
6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br />
<br />
7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br />
<br />
8. Learning to learn hypothesis: The acquisition of certain skills in one context support future learning in other contexts. Such skills include problem analysis, help-seeking, or advance organizers. <br />
<br />
9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br />
<br />
10. Active learning hypothesis: Even in simple tasks, learning is more robust when the learner actively engages in the learning material.<br />
<br />
== Explanation ==<br />
All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br />
<br />
== Descendents ==<br />
<br />
=== Explicit instruction ===<br />
'''A. Explicit vs Implicit.''' These projects typically compare a more explict form of instruction with a more implict form <br />
* [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br />
* [[Basic skills training|French dictation training]] (MacWhinney)<br />
* [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br />
<br />
'''B. Explicit attention manipulations''' studies typically vary features available to learner<br />
* [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br />
* [[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]<br />
* [[Learning French gender cues with prototypes]] (Presson, MacWhinney)<br />
<br />
'''C. Explicit instruction: Practice and Scheduling''' Typical studies control practice events and provide feedback<br />
* [[Optimizing the practice schedule]] (Pavlik et al.) [[Applying optimal scheduling of practice in the Chinese Learnlab|1]]<br />
* [[French gender cues | French grammatical gender cue learning]] (Presson, MacWhinney)<br />
* [[Japanese fluency]] (Yoshimura-MacWhinney)<br />
* [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br />
* [[Using learning curves to optimize problem assignment]] (Cen & Koedinger)<br />
<br />
=== Knowledge accessibility ===<br />
'''A. Background knowledge''' These projects directly study effects of learners' background knowledge<br />
* [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br />
* [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in Interactive Communication]<br />
* [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br />
<br />
'''B. Availability of knowledge during learning'''<br />
* [[Optimizing the practice schedule]] (Pavlik et al.) [[Understanding paired associate transfer effects based on shared stimulus components|2]], [[Applying optimal scheduling of practice in the Chinese Learnlab|1]], [[Understanding encoding inhibition, retrieval inhibition and destructive interference effects of errors during practice|3]]<br />
* [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br />
* [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br />
* [[Arithmetical fluency project]] (Fiez)<br />
* [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, & Liu)<br />
* [[Integrated Learning of Chinese]] (Liu, Perfetti, Wang, Wu)<br />
<br />
=== Active processing ===<br />
These projects also include some addressing issues of learner control<br />
* [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br />
*[[Note-Taking_Technologies | Note-taking Project Page (Bauer & Koedinger)]] [Also in Coordinative Learning]<br />
**[[Note-Taking: Restriction and Selection]] (completed)<br />
**[[Note-Taking: Focusing On Concepts]] (planned)<br />
**[[Note-Taking: Focusing On Quantity]] (planned)<br />
*[[Handwriting Algebra Tutor]] (Anthony, Yang & Koedinger) [Also in Coordinative Learning]<br />
**[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) <br />
**[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress)<br />
<br />
===Other===<br />
<br />
* [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br />
<br />
== Annotated bibliography ==<br />
Forthcoming<br />
<br />
[[Category:Cluster]]</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Refinement_and_Fluency&diff=6722Refinement and Fluency2008-01-11T00:34:54Z<p>Liuying: /* Active processing */</p>
<hr />
<div>= The PSLC Refinement and Fluency cluster =<br />
<br />
== Abstract ==<br />
The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br />
<br />
1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br />
<br />
2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br />
<br />
3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br />
<br />
4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information ("facts") or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br />
<br />
5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br />
<br />
6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br />
<br />
7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br />
<br />
8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br />
<br />
9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br />
<br />
10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br />
<br />
The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br />
<br />
This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br />
<br />
<br><center>[[Image:rf-theory.jpg]]</center><br />
<br />
==Significance==<br />
A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br />
<br />
== Glossary ==<br />
[[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br />
<br />
== Research question ==<br />
The overall research question is how can instruction optimally support the acquisition, refinement, and fluent use of complex targeted knowledge, taking into account the learner’s existing knowledge in relation to the knowledge demands of the target domain? In examining this general question, the studies focus on features of the learning situation, including the following: the cognitive demands of targeted knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br />
<br />
== Independent variables ==<br />
At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br />
<br />
== Dependent variables ==<br />
The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br />
<br />
== Hypotheses ==<br />
Instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. More specifically, the initial acquisition of knowledge and its refinement benefit from instructional activities that require the learner to encode the relevant knowledge components by attending to [[valid features]] of the learning content. The fluency corollary: <br />
<br />
<br />
Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br />
<br />
1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br />
<br />
2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby "divergent coding systems" here may be the same as "multiple input sources" in co-training.)<br />
<br />
3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br />
<br />
4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br />
<br />
5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br />
<br />
6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br />
<br />
7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br />
<br />
8. Learning to learn hypothesis: The acquisition of certain skills in one context support future learning in other contexts. Such skills include problem analysis, help-seeking, or advance organizers. <br />
<br />
9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br />
<br />
10. Active learning hypothesis: Even in simple tasks, learning is more robust when the learner actively engages in the learning material.<br />
<br />
== Explanation ==<br />
All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br />
<br />
== Descendents ==<br />
<br />
=== Explicit instruction ===<br />
'''A. Explicit vs Implicit.''' These projects typically compare a more explict form of instruction with a more implict form <br />
* [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br />
* [[Basic skills training|French dictation training]] (MacWhinney)<br />
* [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br />
<br />
'''B. Explicit attention manipulations''' studies typically vary features available to learner<br />
* [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br />
* [[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]<br />
* [[Learning French gender cues with prototypes]] (Presson, MacWhinney)<br />
<br />
'''C. Explicit instruction: Practice and Scheduling''' Typical studies control practice events and provide feedback<br />
* [[Optimizing the practice schedule]] (Pavlik et al.) [[Applying optimal scheduling of practice in the Chinese Learnlab|1]]<br />
* [[French gender cues | French grammatical gender cue learning]] (Presson, MacWhinney)<br />
* [[Japanese fluency]] (Yoshimura-MacWhinney)<br />
* [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br />
* [[Using learning curves to optimize problem assignment]] (Cen & Koedinger)<br />
<br />
=== Knowledge accessibility ===<br />
'''A. Background knowledge''' These projects directly study effects of learners' background knowledge<br />
* [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br />
* [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in Interactive Communication]<br />
* [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br />
<br />
'''B. Availability of knowledge during learning'''<br />
* [[Optimizing the practice schedule]] (Pavlik et al.) [[Understanding paired associate transfer effects based on shared stimulus components|2]], [[Applying optimal scheduling of practice in the Chinese Learnlab|1]], [[Understanding encoding inhibition, retrieval inhibition and destructive interference effects of errors during practice|3]]<br />
* [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br />
* [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br />
* [[Arithmetical fluency project]] (Fiez)<br />
* [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, & Liu)<br />
<br />
=== Active processing ===<br />
These projects also include some addressing issues of learner control<br />
* [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br />
*[[Note-Taking_Technologies | Note-taking Project Page (Bauer & Koedinger)]] [Also in Coordinative Learning]<br />
**[[Note-Taking: Restriction and Selection]] (completed)<br />
**[[Note-Taking: Focusing On Concepts]] (planned)<br />
**[[Note-Taking: Focusing On Quantity]] (planned)<br />
*[[Handwriting Algebra Tutor]] (Anthony, Yang & Koedinger) [Also in Coordinative Learning]<br />
**[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) <br />
**[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress)<br />
<br />
===Other===<br />
<br />
* [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br />
<br />
== Annotated bibliography ==<br />
Forthcoming<br />
<br />
[[Category:Cluster]]</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Refinement_and_Fluency&diff=6721Refinement and Fluency2008-01-11T00:34:26Z<p>Liuying: /* Active processing */</p>
<hr />
<div>= The PSLC Refinement and Fluency cluster =<br />
<br />
== Abstract ==<br />
The studies in this cluster concern the design and organization of instructional activities to facilitate the acquisition, [[refinement]], and fluent control of critical [[knowledge components]]. The research of the cluster addresses a series of core propositions, including but not limited to the following.<br />
<br />
1. cognitive task analysis or knowledge component analysis: Complex knowledge consists of smaller components that can be identified through analysis of knowledge-based task performance and tested in experiments. To design effective instruction, learning tasks are anlayzed into simpler task components. <br />
<br />
2. fluency from basics: For true fluency, higher level skills must be grounded on well-practiced lower level skills.<br />
<br />
3. scheduling of practice: [[Optimized scheduling]] of [[practice]] uses principles of memory to maximize robust learning and achieve mastery.<br />
<br />
4. [[explicit instruction]]: Explicit instruction, i.e. instruction that either directly asserts information ("facts") or provides rules, facilitates the acquisition and refinement of specific skills. Rules are effective only when they are relatively simple.<br />
<br />
5. [[implicit instruction]]: Implicit instruction, i.e. exposure to to-be-learned patterns, can foster the development of pattern familiarity and strengthen connections of these patterns to other patterns. <br />
<br />
6. immediacy of feedback: A corollary of the scheduling and explicit instruction propositions is that immediate feedback facilitates learning.<br />
<br />
7. [[cue validity]]: In both explicit and implicit instruction, the validity of a cue for a knowledge component affects the learning of that knowledge component. (Cue validity is related to [[feature validity]].)<br />
<br />
8. [[focusing]]: Instruction that directs (focuses) the learner's attention to valid cues leads to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br />
<br />
9. learning to learn: The acquisition of skills and strategies that can generalize across learning tasks can promote new learning. Examples may be deep analysis, help-seeking, use of advance organizers, and, most generally, meta-cognitive strategies. <br />
<br />
10. [[transfer]]: A learner's earlier knowledge places strong constraints on new learning, promoting some forms of learning, while inhibiting others.<br />
<br />
The overall hypothesis is that instruction that systematically reflects the complex [[features]] of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. The principle is that the gap between targeted knowledge and existing knowledge needs to be directly reflected in the organization of instructional events. This organization includes the structure of knowledge components selected for instruction, the scheduling of learning events, practice, recall opportunities, explicit and implicit presentations, and other activities.<br />
<br />
This hypothesis can be rephrased in terms of the PSLC general hypothesis, which is that [[robust learning]] occurs when the [[learning event space]] is designed to include appropriate target paths, and when students are encouraged to take those paths. The studies in this cluster focus on the formulation of well specified target paths with highly predictable learning outcomes.<br />
<br />
<br><center>[[Image:rf-theory.jpg]]</center><br />
<br />
==Significance==<br />
A core theme in this cluster is that instruction in basic skills can facilitate the acquisition and refinement of knowledge and prepare the learner for [[fluency]]-enhancing practice. Instruction that provides practice and feedback for basic skills on a schedule that closely matches observed student abilities is important for this goal, and can be effectively delivered by computer. In the area of second language learning, the strengths of computerized instruction are matched by certain weaknesses. In particular, computerized tutors are not yet good at speech recognition, making it difficult to assess student production. Moreover, contact with a human teacher can increase the breadth of language usage, as well as motivation. Therefore, an optimal environment for language learning would combine the strengths of computerized instruction with those of classroom instruction. It is possible that a similar analysis will apply to science and math.<br />
<br />
== Glossary ==<br />
[[:Category:Refinement and Fluency|Refinement and Fluency]] glossary.<br />
<br />
== Research question ==<br />
The overall research question is how can instruction optimally support the acquisition, refinement, and fluent use of complex targeted knowledge, taking into account the learner’s existing knowledge in relation to the knowledge demands of the target domain? In examining this general question, the studies focus on features of the learning situation, including the following: the cognitive demands of targeted knowledge components, the scheduling of practice, the timing and extent of explicit [[instructional method|instructional events]] relative to implicit learning opportunities, and the role of feedback.<br />
<br />
== Independent variables ==<br />
At a general level, the research varies the organization of instructional events. This organization variable is typically based on alternative analyses of task demands, relevant knowledge components, and learner background.<br />
<br />
== Dependent variables ==<br />
The dependent variables in these studies assess learner performance during learning events and following learning. Typical measures are percentage correct and number of learning trials or time to reach a given standard of performance. Response times are also measured in some cases.<br />
<br />
== Hypotheses ==<br />
Instruction that systematically reflects the complex features of targeted knowledge in relation to the learner’s existing knowledge leads to more robust learning than instruction that does not. More specifically, the initial acquisition of knowledge and its refinement benefit from instructional activities that require the learner to encode the relevant knowledge components by attending to [[valid features]] of the learning content. The fluency corollary: <br />
<br />
<br />
Specific hypotheses about the organization of instruction derive from task analyzes of specific domain knowledge and the existing knowledge of the learner. A background assumption for most studies is that fluency is grounded in well-practiced lower level skills. A few examples of specific hypotheses are as follows:<br />
<br />
1. Scheduling of practice hypothesis: The optimal scheduling of practice uses principles of memory consolidation to maximize robust learning and achieve mastery.<br />
<br />
2. Resonance hypothesis: The acquisition of knowledge components can be facilitated by evoking associations between divergent coding systems. (This hypothesis is similar or perhaps the same as [[Coordinative Learning]] hypothesis or [[co-training]] more specifically whereby "divergent coding systems" here may be the same as "multiple input sources" in co-training.)<br />
<br />
3. [[Explicit instruction]] hypothesis: Explicit rule-based instruction facilitates the acquisition of specific skills, but only if the rules are simple.<br />
<br />
4. [[Implicit instruction]] hypothesis: Implicit instruction or exposure serves to foster the development of initial familiarity with larger patterns.<br />
<br />
5. Feedback hypothesis: Instruction that provides immediate, diagnostic feedback will be superior to instruction that does not.<br />
<br />
6. Cue validity hypothesis: In both explicit and implicit instruction, cue validity plays a central role in determining ease of learning of knowledge components. See also [[feature validity]].<br />
<br />
7. [[Focusing]] hypothesis: Instruction that focuses the learner's attention on valid cues will lead to more robust learning than unfocused instruction or instruction that focuses on less valid cues.<br />
<br />
8. Learning to learn hypothesis: The acquisition of certain skills in one context support future learning in other contexts. Such skills include problem analysis, help-seeking, or advance organizers. <br />
<br />
9. Learner knowledge hypothesis: A learner's existing knowledge places strong constraints on new learning, promoting some forms of learning, while blocking others.<br />
<br />
10. Active learning hypothesis: Even in simple tasks, learning is more robust when the learner actively engages in the learning material.<br />
<br />
== Explanation ==<br />
All knowledge involves content and procedures that are specific to a domain. An analysis of the domain reveals the complexities that a learner of a given background will face and the knowledge components that are part of the overall complexity. Accordingly, the organization of instruction is critical in allowing the learner to attend to the critical valid features of knowledge components and to integrated them in authentic performance. Acquiring valid features and strengthening their associations facilitates retrieval during subsequent assessment and instruction, leading to more robust learning. Additionally, robust learning is increased by the scheduling of learning events that promotes the [[long-term retention]] of the associations.<br />
<br />
== Descendents ==<br />
<br />
=== Explicit instruction ===<br />
'''A. Explicit vs Implicit.''' These projects typically compare a more explict form of instruction with a more implict form <br />
* [[Learning the role of radicals in reading Chinese]] (Liu et al.)<br />
* [[Basic skills training|French dictation training]] (MacWhinney)<br />
* [[Providing optimal support for robust learning of syntactic constructions in ESL]] (Levin, Frishkoff, De Jong, Pavlik)<br />
<br />
'''B. Explicit attention manipulations''' studies typically vary features available to learner<br />
* [[Chinese pinyin dictation]] (Zhang-MacWhinney)<br />
* [[Learning a tonal language: Chinese]] (Wang, Perfetti, Liu) [Also Coordinative learning]<br />
* [[Learning French gender cues with prototypes]] (Presson, MacWhinney)<br />
<br />
'''C. Explicit instruction: Practice and Scheduling''' Typical studies control practice events and provide feedback<br />
* [[Optimizing the practice schedule]] (Pavlik et al.) [[Applying optimal scheduling of practice in the Chinese Learnlab|1]]<br />
* [[French gender cues | French grammatical gender cue learning]] (Presson, MacWhinney)<br />
* [[Japanese fluency]] (Yoshimura-MacWhinney)<br />
* [[Fostering fluency in second language learning]] (De Jong, Perfetti)<br />
* [[Using learning curves to optimize problem assignment]] (Cen & Koedinger)<br />
<br />
=== Knowledge accessibility ===<br />
'''A. Background knowledge''' These projects directly study effects of learners' background knowledge<br />
* [[Intelligent_Writing_Tutor | First language effects on second language grammar acquisition]] (Mitamura-Wylie)<br />
* [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]] [Also in Interactive Communication]<br />
* [[The Impact of Native Writing Systems on 2nd Language Reading]] (Einikis, Ben-Yehudah, Fiez)<br />
<br />
'''B. Availability of knowledge during learning'''<br />
* [[Optimizing the practice schedule]] (Pavlik et al.) [[Understanding paired associate transfer effects based on shared stimulus components|2]], [[Applying optimal scheduling of practice in the Chinese Learnlab|1]], [[Understanding encoding inhibition, retrieval inhibition and destructive interference effects of errors during practice|3]]<br />
* [[Using syntactic priming to increase robust learning]] (De Jong, Perfetti, DeKeyser)<br />
* [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br />
* [[Arithmetical fluency project]] (Fiez)<br />
* [[A word-experience model of Chinese character learning]] (Reichle, Perfetti, & Liu)<br />
<br />
=== Active processing ===<br />
These projects also include some addressing issues of learner control<br />
* [[Mental rotations during vocabulary training]] (Tokowicz-Degani)<br />
*[[Note-Taking_Technologies | Note-taking Project Page (Bauer & Koedinger)]] [Also in Coordinative Learning]<br />
**[[Note-Taking: Restriction and Selection]] (completed)<br />
**[[Note-Taking: Focusing On Concepts]] (planned)<br />
**[[Note-Taking: Focusing On Quantity]] (planned)<br />
*[[Handwriting Algebra Tutor]] (Anthony, Yang & Koedinger) [Also in Coordinative Learning]<br />
**[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed) <br />
**[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress)<br />
**[[Integrated Chinese Learning ...]] (in progress)<br />
<br />
===Other===<br />
<br />
* [[Development of a Novel Writing System]] (Greene, Durisko, Ciuca, Fiez)<br />
<br />
== Annotated bibliography ==<br />
Forthcoming<br />
<br />
[[Category:Cluster]]</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Learning_a_tonal_language:_Chinese&diff=5393Learning a tonal language: Chinese2007-06-13T11:38:58Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Learning a tonal language: Chinese<br />
*Researchers: Min Wang, Ying Liu, Suemei Wu, Derek Chan, Charles Perfetti<br />
*PIs: Min Wang, Charles Perfetti, Ying Liu<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Baoguo Chen<br />
*Graduate Students: Derek Chan, Brian Brubaker<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2006<br />
*LearnLab Site and Courses , CMU Chinese Online<br />
*Number of Students: 150<br />
*Total Participant Hours for the study: 300<br />
*Data in the Data Shop: Yes<br />
----<br />
== Abstract ==<br />
*The tonal feature of Chinese language poses a particular challenge for a beginning learner of Chinese as a second language. In this project, we test learning hypotheses based on the assumption that attending to the critical [[features]] of the tonal pitch contour facilitates learning.<br />
*This study consists of experiments on both tone perception and production tasks. In tone perception task, three training conditions were tested: 1) visual pitch contours that depict the acoustic information of the tones, together with Pinyin spelling of the spoken syllable; 2) numerical numbers that represent the tones in traditional classroom instruction, together with Pinyin spelling of the spoken syllable; 3) visual pitch contours, without Pinyin spelling. By comparing these three training conditions, we will test two hypotheses: 1) using visual information of the tone waveform facilitates students’ perception of auditory tones; 2) providing Pinyin spelling allows the students to focus on the tone, therefore yields more [[robust learning]], which was measured by [[transfer]] and [[long-term retention]] tasks.<br />
*In tone production task, we used a frequency analyzer to extract the fundamental frequency of student’s sound production. The pitch contour of production will be displayed to the student in real time during their production practice. By comparing the group which receives this individualized pitch contour with a group which does not, we predict the former will show more [[robust learning]] on tone production, which was shown as pronunciation [[refinement]]. <br />
<br />
<br />
== Glossary ==<br />
Tone; pitch contour; visual feedback<br />
<br />
<br />
== Research question ==<br />
How to optimally use crucial tonal information to facilitate Chinese tone learning.<br />
<br />
== Background ==<br />
*The basic speech unit of Chinese is the syllable, and each syllable is divided into two parts: onset and rime. The onset of a Chinese syllable is always a single consonant. In most syllables the rime segment consists of mainly vowels. As a result, Chinese has a much smaller number of syllables than does spoken English (Hanely, Tzeng, & Huang, 1999). This leads to a large number of homophones in Chinese. However, because of the existence of tone in Chinese syllables, the number of homophones is reduced. There are about 1,300 tone syllables in spoken Chinese (Taylor & Taylor, 1995). <br />
*The tonal feature of the Chinese language forms a sharp contrast to many alphabetic languages such as English. American college students learning Chinese language may encounter great difficulty in acquiring the tone skill. Wang, Perfetti, and Liu (2003) used a onset-rime-tone matching task to test beginning Chinese learners’ phonological processing skills. We found that these beginning Chinese learners showed poorer performance in tone matching compared to their performance in onset and rime matching.<br />
*There is very limited research on studying tone learning. Three-year-old Chinese native-speaking children have been shown to be able to detect when rime and tone are combined but they cannot detect rime and tone separately. Five-year-olds, on the other hand, can independently process rime and tone (Ho & Bryant, 1997). Wang, Spence, Jongman and Sereno (1999) trained American listeners to perceive Chinese tones. They found a significant increase of identification accuracy from pretest to posttest. <br />
<br />
== Dependent variables ==<br />
<br />
[[Normal post-test]]: Accuracy of making tone selection and decision tasks, and evaluations of productions.<br />
<br />
== Independent variables ==<br />
<br />
''Tone perception study'': 1) visual pitch contours that depict the acoustic information of the tones, together with Pinyin spelling of the spoken syllable; 2) numerical numbers that represent the tones in traditional classroom instruction, together with Pinyin spelling of the spoken syllable; 3) visual pitch contours, without Pinyin spelling.<br />
<br />
''Tone production study'': 1) visual feedback based on tone analyzer of student’s pronunciation; 2) no visual feedback.<br />
<br />
== Hypothesis ==<br />
Having student focusing on tonal feature by providing visual pitch contour plus segmental information facilitates tonal perception and production.<br />
<br />
== Findings ==<br />
Current results from two terms of tone perception experiment showed providing segmental information (Pinyin) provides a better learning curve. The learning curve of term 1 (lesson 1 to 8) showed Pinyin+contour and Pinyin+number conditions are better than contour only condition which is shown in Figure 1 that the former two conditions have more negative slope (faster learning rate). The learning curves of term 2 were “flat” and did not show difference between the three conditions.<br />
The post-test result of term 1 showed significantly higher improvement of performance in Pinyin+contour than pinyin+number conditions in item analysis. In term 2, the post-test did not show any significant difference between the three conditions.<br />
<br />
== Explanation ==<br />
Learning Chinese tone was facilitated by having students [[focusing]] on the tonal features. Proving segmental information (Pinyin) before learning to a syllable sound provides more [[assistance]] to beginners, which makes it easier for them to pay more attention to the tone. <br />
Furthremore, the visual pitch contour and auditory tone are [[complementary]] information for learning tones. The mental representation of tones are more complete when visual pintch contour are provided together with Pinyin.<br />
<br />
== Descendents ==<br />
Tone perception (the present page)<br />
Tone production (under construction)<br />
<br />
== Further information ==<br />
www.pitt.edu/~liuying/pslc_tone.doc</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Learning_a_tonal_language:_Chinese&diff=5392Learning a tonal language: Chinese2007-06-13T11:37:15Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary table<br />
*Node Title: Learning a tonal language: Chinese<br />
*Researchers: Min Wang, Ying Liu, Suemei Wu, Derek Chan, Charles Perfetti<br />
*PIs: Min Wang, Charles Perfetti, Ying Liu<br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs: Baoguo Chen<br />
*Graduate Students: Derek Chan, Brian Brubaker<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2006<br />
*LearnLab Site and Courses , CMU Chinese Online<br />
*Number of Students: 150<br />
*Total Participant Hours for the study: 300<br />
*Data in the Data Shop: Yes<br />
----<br />
== Abstract ==<br />
*The tonal feature of Chinese language poses a particular challenge for a beginning learner of Chinese as a second language. In this project, we test learning hypotheses based on the assumption that attending to the critical [[features]] of the tonal pitch contour facilitates learning.<br />
*This study consists of experiments on both tone perception and production tasks. In tone perception task, three training conditions were tested: 1) visual pitch contours that depict the acoustic information of the tones, together with Pinyin spelling of the spoken syllable; 2) numerical numbers that represent the tones in traditional classroom instruction, together with Pinyin spelling of the spoken syllable; 3) visual pitch contours, without Pinyin spelling. By comparing these three training conditions, we will test two hypotheses: 1) using visual information of the tone waveform facilitates students’ perception of auditory tones; 2) providing Pinyin spelling allows the students to focus on the tone, therefore yields more [[robust learning]], which was measured by [[transfer]] and [[long-term retention]] tasks.<br />
*In tone production task, we used a frequency analyzer to extract the fundamental frequency of student’s sound production. The pitch contour of production will be displayed to the student in real time during their production practice. By comparing the group which receives this individualized pitch contour with a group which does not, we predict the former will show more [[robust learning]] on tone production, which was shown as pronunciation [[refinement]]. <br />
<br />
<br />
== Glossary ==<br />
A glossary that defines terms used elsewhere in this node but not defined in the nodes that are parents, grandparents, etc. of this node; <br />
tone; pitch contour; visual feedback<br />
<br />
<br />
== Research question ==<br />
<br />
The research question stated as concisely as possible, usually in a single sentence; <br />
How to optimally use crucial tonal information to facilitate Chinese tone learning.<br />
<br />
== Background ==<br />
A background and significance section that briefly summarizes prior work on the research question and why it is important to answer it;<br />
[[Link title]] The basic speech unit of Chinese is the syllable, and each syllable is divided into two parts: onset and rime. The onset of a Chinese syllable is always a single consonant. In most syllables the rime segment consists of mainly vowels. As a result, Chinese has a much smaller number of syllables than does spoken English (Hanely, Tzeng, & Huang, 1999). This leads to a large number of homophones in Chinese. However, because of the existence of tone in Chinese syllables, the number of homophones is reduced. There are about 1,300 tone syllables in spoken Chinese (Taylor & Taylor, 1995). <br />
The tonal feature of the Chinese language forms a sharp contrast to many alphabetic languages such as English. American college students learning Chinese language may encounter great difficulty in acquiring the tone skill. Wang, Perfetti, and Liu (2003) used a onset-rime-tone matching task to test beginning Chinese learners’ phonological processing skills. We found that these beginning Chinese learners showed poorer performance in tone matching compared to their performance in onset and rime matching.<br />
There is very limited research on studying tone learning. Three-year-old Chinese native-speaking children have been shown to be able to detect when rime and tone are combined but they cannot detect rime and tone separately. Five-year-olds, on the other hand, can independently process rime and tone (Ho & Bryant, 1997). Wang, Spence, Jongman and Sereno (1999) trained American listeners to perceive Chinese tones. They found a significant increase of identification accuracy from pretest to posttest. <br />
<br />
== Dependent variables ==<br />
<br />
[[Normal post-test]]: Accuracy of making tone selection and decision tasks, and evaluations of productions.<br />
<br />
== Independent variables ==<br />
<br />
''Tone perception study'': 1) visual pitch contours that depict the acoustic information of the tones, together with Pinyin spelling of the spoken syllable; 2) numerical numbers that represent the tones in traditional classroom instruction, together with Pinyin spelling of the spoken syllable; 3) visual pitch contours, without Pinyin spelling.<br />
<br />
''Tone production study'': 1) visual feedback based on tone analyzer of student’s pronunciation; 2) no visual feedback.<br />
<br />
== Hypothesis ==<br />
The hypothesis, which is a concise statement of the relationship among the variables that answers the research question; <br />
Having student focusing on tonal feature by providing visual pitch contour plus segmental information facilitates tonal perception and production.<br />
<br />
== Findings ==<br />
Current results from two terms of tone perception experiment showed providing segmental information (Pinyin) provides a better learning curve. The learning curve of term 1 (lesson 1 to 8) showed Pinyin+contour and Pinyin+number conditions are better than contour only condition which is shown in Figure 1 that the former two conditions have more negative slope (faster learning rate). The learning curves of term 2 were “flat” and did not show difference between the three conditions.<br />
The post-test result of term 1 showed significantly higher improvement of performance in Pinyin+contour than pinyin+number conditions in item analysis. In term 2, the post-test did not show any significant difference between the three conditions.<br />
<br />
== Explanation ==<br />
Learning Chinese tone was facilitated by having students [[focusing]] on the tonal features. Proving segmental information (Pinyin) before learning to a syllable sound provides more [[assistance]] to beginners, which makes it easier for them to pay more attention to the tone. <br />
Furthremore, the visual pitch contour and auditory tone are [[complementary]] information for learning tones. The mental representation of tones are more complete when visual pintch contour are provided together with Pinyin.<br />
<br />
== Descendents ==<br />
The descendents, which lists links to descendent nodes of this one, if there are any; <br />
Tone perception (the present page)<br />
Tone production (under construction)<br />
<br />
== Further information ==<br />
A further information section that points to documents using hyper links and/or references in APA format. Each indicates briefly the document's relationship to the node (e.g., whether the document is a paper reporting the node in full detail, a proposal describing the motivation and design of the study in more detail, the node for a similar PSLC research study, etc.). <br />
www.pitt.edu/~liuying/pslc_tone.doc</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Learning_the_role_of_radicals_in_reading_Chinese&diff=5391Learning the role of radicals in reading Chinese2007-06-13T11:24:52Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary Table<br />
*Node Title: Semantic Radicals Study<br />
*Researchers: Susan Dunlap, Ying Liu, Charles Perfetti, Sue-mei Wu<br />
*PIs: Charles Perfetti, Ying Liu, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Graduate Students: Susan Dunlap<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2006<br />
*LearnLab Site and Courses , CMU Chinese Online<br />
*Number of Students: 20<br />
*Total Participant Hours for the study: 60<br />
*Data in the Data Shop: in progress<br />
----<br />
<br />
== Abstract ==<br />
<br />
Does providing reliable semantic information help second language learners acquire new words? Two experiments investigated whether adult learners of Chinese benefited from explicit instruction of semantic information when learning new characters. We manipulated whether semantic information was a reliable cue to word meaning and whether predictability was taught [[explicit instruction|explicitly]]. We measured learning outcomes with translation and semantic judgment tasks.<br />
<br />
== Glossary ==<br />
<br />
Semantic radical; [[Explicit instruction]]; [[Implicit instruction]]; [[Cue validity]]<br />
<br />
== Research Question ==<br />
<br />
Does providing reliable semantic information help second language learners acquire new words?<br />
<br />
== Background ==<br />
A '''background''' and significance section that briefly summarizes prior work on the research question and why it is important to answer it<br />
<br />
Previous research has shown that non-native learners of Chinese do not discern the presence of [[cue validity|helpful cues]] in the orthography unless such relationships are taught explicitly (Taft & Chung, 1999). But because semantic cues in Chinese are not always reliable predictors of word meaning (Hanley, 2005; Shu, Chen, Anderson, Wu, & Xuan, 2003), it may actually be more confusing for a beginning learner to be taught these relationships. The aim of this study was to determine how [[reliability]] of cues can affect learning. As in every language, Chinese has rules and exceptions to those rules. The written form of Chinese contains a high percentage of compound characters, which are single, one-syllable words made up of semantic and phonetic radicals. These radicals, or linguistic subcomponents, often provide cues to the character’s meaning and pronunciation. However, a reader cannot rely solely on using this strategy to decode new words in Chinese. Therefore, we wanted to ascertain whether it is helpful to teach the sometimes ambiguous relationship between linguistic subcomponents and whole word definitions.<br />
<br />
== Dependent variables ==<br />
The '''dependent variables''', which are observable and typically measure competence, motivation, interaction, meta-learning, or some other pedagogically desirable outcome<br />
<br />
[[Normal post-test]] measures:<br />
- accuracy and response time on a semantic category judgment task with previously learned items (Experiment 1)<br />
<br />
- accuracy of translating previously learned Chinese characters into English (Experiment 2)<br />
<br />
[[Transfer]] measure:<br />
- accuracy on a multiple-choice translation task with new Characters (Experiments 1 and 2)<br />
<br />
== Independent variables ==<br />
The '''independent variables''', which are typically include instructional environment, activity or method, and perhaps some student characteristics, such as gender or first language<br />
<br />
Training condition was either explicit (information was provided about the semantic radical’s meaning in relation to meaning of the character) or implicit (no additional information was provided). Being explicit about the radical is an instance of [[feature focusing]] [[instructional method]]. Each semantic radical was either reliable (its meaning was associated with the meaning of the characters) or unreliable (its meaning was unrelated to the meaning of the character in which it appeared).<br />
<br />
== Hypothesis<br />
The '''hypothesis''', which is a concise statement of the relationship among the variables that answers the research question<br />
<br />
We predict an interaction between [[reliability]] and [[explicit instruction|explicitness]], such that learners will perform better on items studied in the explicit condition compared to the implicit condition, and this effect will be greater for characters with reliable semantic radicals than characters with unreliable semantic radicals.<br />
<br />
== Findings ==<br />
The '''findings''', which are the results of the study if any are currently available<br />
<br />
Preliminary analyses show that providing semantic cues promoted retention of target characters and aided in transferring knowledge to new characters. Reliability of cues had no additional effect on retention or transfer.<br />
<br />
== Explanation ==<br />
An '''explanation''', which is short (a paragraph or two) and typically mentions unobservable, hypothetical attributes of the students (e.g., the students’ knowledge or motivation) and cognitive or social processes that affect them<br />
<br />
We theorize that learners benefit from being taught the connection between semantic subcomponents of words and the meanings of words, and they adopt this strategy in learning new vocabulary.<br />
<br />
== Descendents ==<br />
The '''descendents''', which lists links to descendent nodes of this one, if there are any<br />
<br />
None yet.<br />
<br />
== Further information ==<br />
A '''further information''' section that points to documents using hyper links and/or references in APA format. Each indicates briefly the document's relationship to the node (e.g., whether the document is a paper reporting the node in full detail, a proposal describing the motivation and design of the study in more detail, the node for a similar PSLC research study, etc.)<br />
<br />
None yet.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Learning_the_role_of_radicals_in_reading_Chinese&diff=5390Learning the role of radicals in reading Chinese2007-06-13T11:21:19Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary Table<br />
*Node Title: Semantic Radicals Study<br />
*Researchers: Susan Dunlap, Ying Liu, Charles Perfetti, Sue-mei Wu<br />
*PIs: Charles Perfetti, Ying Liu, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Graduate Students: Susan Dunlap<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2006<br />
*LearnLab Site and Courses , CMU Chinese Online<br />
*Number of Students: 20<br />
*Total Participant Hours for the study: 60<br />
*Data in the Data Shop: in progress<br />
----<br />
<br />
== Abstract ==<br />
<br />
Does providing reliable semantic information help second language learners acquire new words? Two experiments investigated whether adult learners of Chinese benefited from explicit instruction of semantic information when learning new characters. We manipulated whether semantic information was a reliable cue to word meaning and whether predictability was taught [[explicit instruction|explicitly]]. We measured learning outcomes with translation and semantic judgment tasks.<br />
<br />
== Glossary ==<br />
<br />
Semantic radical; explicit; implicit; cue reliability<br />
<br />
== Research Question ==<br />
<br />
Does providing reliable semantic information help second language learners acquire new words?<br />
<br />
== Background ==<br />
A '''background''' and significance section that briefly summarizes prior work on the research question and why it is important to answer it<br />
<br />
Previous research has shown that non-native learners of Chinese do not discern the presence of helpful cues in the orthography unless such relationships are taught explicitly (Taft & Chung, 1999). But because semantic cues in Chinese are not always reliable predictors of word meaning (Hanley, 2005; Shu, Chen, Anderson, Wu, & Xuan, 2003), it may actually be more confusing for a beginning learner to be taught these relationships. The aim of this study was to determine how [[reliability]] of cues can affect learning. As in every language, Chinese has rules and exceptions to those rules. The written form of Chinese contains a high percentage of compound characters, which are single, one-syllable words made up of semantic and phonetic radicals. These radicals, or linguistic subcomponents, often provide cues to the character’s meaning and pronunciation. However, a reader cannot rely solely on using this strategy to decode new words in Chinese. Therefore, we wanted to ascertain whether it is helpful to teach the sometimes ambiguous relationship between linguistic subcomponents and whole word definitions.<br />
<br />
== Dependent variables ==<br />
The '''dependent variables''', which are observable and typically measure competence, motivation, interaction, meta-learning, or some other pedagogically desirable outcome<br />
<br />
[[Normal post-test]] measures:<br />
- accuracy and response time on a semantic category judgment task with previously learned items (Experiment 1)<br />
<br />
- accuracy of translating previously learned Chinese characters into English (Experiment 2)<br />
<br />
[[Transfer]] measure:<br />
- accuracy on a multiple-choice translation task with new Characters (Experiments 1 and 2)<br />
<br />
== Independent variables ==<br />
The '''independent variables''', which are typically include instructional environment, activity or method, and perhaps some student characteristics, such as gender or first language<br />
<br />
Training condition was either explicit (information was provided about the semantic radical’s meaning in relation to meaning of the character) or implicit (no additional information was provided). Being explicit about the radical is an instance of [[feature focusing]] [[instructional method]]. Each semantic radical was either reliable (its meaning was associated with the meaning of the characters) or unreliable (its meaning was unrelated to the meaning of the character in which it appeared).<br />
<br />
== Hypothesis<br />
The '''hypothesis''', which is a concise statement of the relationship among the variables that answers the research question<br />
<br />
We predict an interaction between reliability and explicitness, such that learners will perform better on items studied in the explicit condition compared to the implicit condition, and this effect will be greater for characters with reliable semantic radicals than characters with unreliable semantic radicals.<br />
<br />
== Findings ==<br />
The '''findings''', which are the results of the study if any are currently available<br />
<br />
Preliminary analyses show that providing semantic cues promoted retention of target characters and aided in transferring knowledge to new characters. Reliability of cues had no additional effect on retention or transfer.<br />
<br />
== Explanation ==<br />
An '''explanation''', which is short (a paragraph or two) and typically mentions unobservable, hypothetical attributes of the students (e.g., the students’ knowledge or motivation) and cognitive or social processes that affect them<br />
<br />
We theorize that learners benefit from being taught the connection between semantic subcomponents of words and the meanings of words, and they adopt this strategy in learning new vocabulary.<br />
<br />
== Descendents ==<br />
The '''descendents''', which lists links to descendent nodes of this one, if there are any<br />
<br />
None yet.<br />
<br />
== Further information ==<br />
A '''further information''' section that points to documents using hyper links and/or references in APA format. Each indicates briefly the document's relationship to the node (e.g., whether the document is a paper reporting the node in full detail, a proposal describing the motivation and design of the study in more detail, the node for a similar PSLC research study, etc.)<br />
<br />
None yet.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Learning_the_role_of_radicals_in_reading_Chinese&diff=5389Learning the role of radicals in reading Chinese2007-06-13T11:20:45Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary Table<br />
*Node Title: Semantic Radicals Study<br />
*Researchers: Susan Dunlap, Ying Liu, Charles Perfetti, Sue-mei Wu<br />
*PIs: Charles Perfetti, Ying Liu, Min Wang<br />
*Others who have contributed 160 hours or more:<br />
*Graduate Students: Susan Dunlap<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2006<br />
*LearnLab Site and Courses , CMU Chinese Online<br />
*Number of Students: 20<br />
*Total Participant Hours for the study: 60<br />
*Data in the Data Shop: in progress<br />
----<br />
<br />
== Abstract ==<br />
<br />
Does providing reliable semantic information help second language learners acquire new words? Two experiments investigated whether adult learners of Chinese benefited from explicit instruction of semantic information when learning new characters. We manipulated whether semantic information was a reliable cue to word meaning and whether predictability was taught [[explicitly|explicit instruction]]. We measured learning outcomes with translation and semantic judgment tasks.<br />
<br />
== Glossary ==<br />
<br />
Semantic radical; explicit; implicit; cue reliability<br />
<br />
== Research Question ==<br />
<br />
Does providing reliable semantic information help second language learners acquire new words?<br />
<br />
== Background ==<br />
A '''background''' and significance section that briefly summarizes prior work on the research question and why it is important to answer it<br />
<br />
Previous research has shown that non-native learners of Chinese do not discern the presence of helpful cues in the orthography unless such relationships are taught explicitly (Taft & Chung, 1999). But because semantic cues in Chinese are not always reliable predictors of word meaning (Hanley, 2005; Shu, Chen, Anderson, Wu, & Xuan, 2003), it may actually be more confusing for a beginning learner to be taught these relationships. The aim of this study was to determine how [[reliability]] of cues can affect learning. As in every language, Chinese has rules and exceptions to those rules. The written form of Chinese contains a high percentage of compound characters, which are single, one-syllable words made up of semantic and phonetic radicals. These radicals, or linguistic subcomponents, often provide cues to the character’s meaning and pronunciation. However, a reader cannot rely solely on using this strategy to decode new words in Chinese. Therefore, we wanted to ascertain whether it is helpful to teach the sometimes ambiguous relationship between linguistic subcomponents and whole word definitions.<br />
<br />
== Dependent variables ==<br />
The '''dependent variables''', which are observable and typically measure competence, motivation, interaction, meta-learning, or some other pedagogically desirable outcome<br />
<br />
[[Normal post-test]] measures:<br />
- accuracy and response time on a semantic category judgment task with previously learned items (Experiment 1)<br />
<br />
- accuracy of translating previously learned Chinese characters into English (Experiment 2)<br />
<br />
[[Transfer]] measure:<br />
- accuracy on a multiple-choice translation task with new Characters (Experiments 1 and 2)<br />
<br />
== Independent variables ==<br />
The '''independent variables''', which are typically include instructional environment, activity or method, and perhaps some student characteristics, such as gender or first language<br />
<br />
Training condition was either explicit (information was provided about the semantic radical’s meaning in relation to meaning of the character) or implicit (no additional information was provided). Being explicit about the radical is an instance of [[feature focusing]] [[instructional method]]. Each semantic radical was either reliable (its meaning was associated with the meaning of the characters) or unreliable (its meaning was unrelated to the meaning of the character in which it appeared).<br />
<br />
== Hypothesis<br />
The '''hypothesis''', which is a concise statement of the relationship among the variables that answers the research question<br />
<br />
We predict an interaction between reliability and explicitness, such that learners will perform better on items studied in the explicit condition compared to the implicit condition, and this effect will be greater for characters with reliable semantic radicals than characters with unreliable semantic radicals.<br />
<br />
== Findings ==<br />
The '''findings''', which are the results of the study if any are currently available<br />
<br />
Preliminary analyses show that providing semantic cues promoted retention of target characters and aided in transferring knowledge to new characters. Reliability of cues had no additional effect on retention or transfer.<br />
<br />
== Explanation ==<br />
An '''explanation''', which is short (a paragraph or two) and typically mentions unobservable, hypothetical attributes of the students (e.g., the students’ knowledge or motivation) and cognitive or social processes that affect them<br />
<br />
We theorize that learners benefit from being taught the connection between semantic subcomponents of words and the meanings of words, and they adopt this strategy in learning new vocabulary.<br />
<br />
== Descendents ==<br />
The '''descendents''', which lists links to descendent nodes of this one, if there are any<br />
<br />
None yet.<br />
<br />
== Further information ==<br />
A '''further information''' section that points to documents using hyper links and/or references in APA format. Each indicates briefly the document's relationship to the node (e.g., whether the document is a paper reporting the node in full detail, a proposal describing the motivation and design of the study in more detail, the node for a similar PSLC research study, etc.)<br />
<br />
None yet.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Learning_Chinese_pronunciation_from_a_%C3%83%C2%A2%C3%A2%E2%80%9A%C2%AC%C3%85%E2%80%9Ctalking_head%C3%83%C2%A2%C3%A2%E2%80%9A%C2%AC%C3%82%C2%9D&diff=5388Learning Chinese pronunciation from a “talking headâ€Â2007-06-13T11:17:09Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary Table<br />
*Learning Chinese pronunciation from a “talking head”<br />
*Researchers: Ying Liu, Dominic Massaro, Susan Dunlap, Suemei Wu, Trevor Chen, Derek Chan, Charles Perfetti<br />
*PIs: Ying Liu, Dominic Massaro, Charles Perfetti, <br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs:<br />
*Graduate Students: Trevor Chen<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2006<br />
*LearnLab Site and Courses , CMU Chinese Online<br />
*Number of Students: 20<br />
*Total Participant Hours for the study: 40<br />
*Data in the Data Shop: Yes<br />
----<br />
<br />
== Abstract ==<br />
In this study, we compared the learning of Chinese pronunciation under three different online instruction methods: audio only, human “talking head”, and computer generated synthetic “talking head”. The learning took place through a web site developed specifically for students learning Chinese in the Chinese Learnlab[http://learnlab.org/learnlabs/chinese/]. Under both “talking head” conditions, the face of the speaker occupied 2/3 of the video screen. When student viewed the human “talking head”, major information came from the shape of the mouth and lip movement accompanied by audio sound. Whereas the synthetic “talking head” is transparent to reveal the internal articulators, which was accompanied by a slower than normal sound to match the “talking head” articulation. We predict [[multimedia sources]] can lead to [[robust learning]] when the [[cognitive load]] is within limit.<br />
<br />
== Glossary ==<br />
<br />
Visual; audio; video<br />
<br />
== Research question ==<br />
<br />
Does visual input of a “talking head” enhance the learning of Chinese pronunciation? <br />
<br />
== Background ==<br />
<br />
Multimedia technology has been used in second language learning for many years. The current available technology makes it possible to deliver not only text information, but also auditory and visual information through the Internet. It has been found that multiple-strategies and multiple modalities facilitate learning (Blum and Mitchell, 1998). For example, research in English showed that visual information on the vertical separation between the lips and the degree of lip spreading/rounding help the understanding of spoken language (Massaro and Cohen, 1990; Cohen and Massaro, 1994). So, does a visually presented “talking head” contains both auditory and visual information help Chinese character learning? Especially the robust learning of Chinese pronunciations which contain difficult consonants and tones? The method has not been tested by any well-designed experiment yet. However, based on a study in which we used a real person “talking head” to train true beginners on Chinese character, we believe it is a very effective learning method. Dr. Massaro’s research group is currently working on developing a animated 3D Chinese virtual speaker: Bao (Massaro, Ouni, Cohen, and Clark, In press). They found both the animated video (Baldi) and natural video were perceived better than voice only condition in a perceptual recognition experiment. The above two video conditions performed equally. We will do a comparison study between audio only, Bao and real person talking heads on our Chinese learners. <br />
<br />
<br />
<br />
== Dependent variables == which are observable and typically measure competence, motivation, interaction, meta-learning, or some other pedagogically desirable outcome; <br />
<br />
Accuracy of pronouncing Chinese syllables (initials and finals).<br />
<br />
== Independent variables ==<br />
<br />
Three learning methods: audio only (control), human “talking head”, computer synthesized “talking head”.<br />
Different Chinese syllables listed in Table 1.<br />
Table 1. The syllables are all tone-1 Mandarin words (pin-yin) except those with the tones indicated in parentheses. UC = unique consonants; NUC = Non-unique consonants; NUS = Non-unique syllables; US = unique syllables; UV = unique vowels<br />
*UC NUC NUS US UV<br />
*ji Pi bao Ju Ge<br />
*qie Nie dao qu He<br />
*xian Tian gao xu Ke<br />
*zhen Fen e(2)<br />
*chuan kuan U(3)<br />
*sha La <br />
<br />
== Hypothesis ==<br />
<br />
We predict that visual input can provide more robust learning of pronouncing Chinese sound when using appropriately.<br />
<br />
== Findings ==<br />
<br />
The analysis on finals showed significant condition effect (χ2 (2)=7.39, p=0.025). Further pairwise comparisons showed that synthetic Baldi is significantly better than audio only condition (χ2(1)=7.36, p=0.0067). Least square mean were listed in Table 2.<br />
<br />
Table 2. Least square mean percentages of improvement based on logistic model<br />
*Condition Initials Finals<br />
*Audio only 53.1 34.2<br />
*Human face 54.5 39.6<br />
*Synthetic Baldi 51.5 46.4<br />
<br />
<br />
== Explanation ==<br />
<br />
It is difficult to learn to speak a language by just listening to it, especially for a second language learner at beginner’s level. Visual cues provide extra information for reach the goal of speaking “natively”. Imitation is best achieved by understanding how the organs produce the sound. Current findings support that Bao (Chinese Baldi) has significant advantage in teaching Chinese vowel pronunciation than audio alone. The human face falls between the above two methods, because it provides some useful facial information but the internal organs are not transparent. We conclude that visual speech provides significant benefit for learners to improve their pronunciation.<br />
<br />
As a node under [[coordinative learning]] cluster, [[coordination]] of visual and audio inputs is the cognitive process leads to more [[robust learning]]. <br />
<br />
== Descendents ==<br />
None.<br />
<br />
== Further information ==<br />
Massaro, D. W., Liu, Y., Chen, T. H., & Perfetti, C. A. (2006). A Multilingual Embodied Conversational Agent for Tutoring Speech and Language Learning. Proceedings of the Ninth International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP, September, Pittsburgh, PA), 825-828.Universität Bonn, Bonn, Germany.</div>Liuyinghttps://learnlab.org/wiki/index.php?title=Learning_Chinese_pronunciation_from_a_%C3%83%C2%A2%C3%A2%E2%80%9A%C2%AC%C3%85%E2%80%9Ctalking_head%C3%83%C2%A2%C3%A2%E2%80%9A%C2%AC%C3%82%C2%9D&diff=5387Learning Chinese pronunciation from a “talking headâ€Â2007-06-13T11:15:50Z<p>Liuying: </p>
<hr />
<div>----<br />
Summary Table<br />
*Learning Chinese pronunciation from a “talking head”<br />
*Researchers: Ying Liu, Dominic Massaro, Susan Dunlap, Suemei Wu, Trevor Chen, Derek Chan, Charles Perfetti<br />
*PIs: Ying Liu, Dominic Massaro, Charles Perfetti, <br />
*Others who have contributed 160 hours or more:<br />
*Post-Docs:<br />
*Graduate Students: Trevor Chen<br />
*Study Start Date Sep 1, 2005<br />
*Study End Date Dec 31, 2006<br />
*LearnLab Site and Courses , CMU Chinese Online<br />
*Number of Students: 20<br />
*Total Participant Hours for the study: 40<br />
*Data in the Data Shop: Yes<br />
----<br />
<br />
== Abstract ==<br />
In this study, we compared the learning of Chinese pronunciation under three different online instruction methods: audio only, human “talking head”, and computer generated synthetic “talking head”. The learning took place through a web site developed specifically for students learning Chinese in the Chinese Learnlab[http://learnlab.org/learnlabs/chinese/]. Under both “talking head” conditions, the face of the speaker occupied 2/3 of the video screen. When student viewed the human “talking head”, major information came from the shape of the mouth and lip movement accompanied by audio sound. Whereas the synthetic “talking head” is transparent to reveal the internal articulators, which was accompanied by a slower than normal sound to match the “talking head” articulation. We predict [[multimedia sources]] can lead to [[robust learning]] when the [[cognitive load]] is within limit.<br />
<br />
== Glossary ==<br />
<br />
Visual; audio; video<br />
<br />
== Research question ==<br />
<br />
Does visual input of a “talking head” enhance the learning of Chinese pronunciation? <br />
<br />
== Background ==<br />
<br />
Multimedia technology has been used in second language learning for many years. The current available technology makes it possible to deliver not only text information, but also auditory and visual information through the Internet. It has been found that multiple-strategies and multiple modalities facilitate learning (Blum and Mitchell, 1998). For example, research in English showed that visual information on the vertical separation between the lips and the degree of lip spreading/rounding help the understanding of spoken language (Massaro and Cohen, 1990; Cohen and Massaro, 1994). So, does a visually presented “talking head” contains both auditory and visual information help Chinese character learning? Especially the robust learning of Chinese pronunciations which contain difficult consonants and tones? The method has not been tested by any well-designed experiment yet. However, based on a study in which we used a real person “talking head” to train true beginners on Chinese character, we believe it is a very effective learning method. Dr. Massaro’s research group is currently working on developing a animated 3D Chinese virtual speaker: Bao (Massaro, Ouni, Cohen, and Clark, In press). They found both the animated video (Baldi) and natural video were perceived better than voice only condition in a perceptual recognition experiment. The above two video conditions performed equally. We will do a comparison study between audio only, Bao and real person talking heads on our Chinese learners. <br />
<br />
<br />
<br />
== Dependent variables == which are observable and typically measure competence, motivation, interaction, meta-learning, or some other pedagogically desirable outcome; <br />
<br />
Accuracy of pronouncing Chinese syllables (initials and finals).<br />
<br />
== Independent variables ==<br />
<br />
Three learning methods: audio only (control), human “talking head”, computer synthesized “talking head”.<br />
Different Chinese syllables listed in Table 1.<br />
Table 1. The syllables are all tone-1 Mandarin words (pin-yin) except those with the tones indicated in parentheses. UC = unique consonants; NUC = Non-unique consonants; NUS = Non-unique syllables; US = unique syllables; UV = unique vowels<br />
*UC NUC NUS US UV<br />
*ji Pi bao Ju Ge<br />
*qie Nie dao qu He<br />
*xian Tian gao xu Ke<br />
*zhen Fen e(2)<br />
*chuan kuan U(3)<br />
*sha La <br />
<br />
== Hypothesis ==<br />
<br />
We predict that visual input can provide more robust learning of pronouncing Chinese sound when using appropriately.<br />
<br />
== Findings ==<br />
<br />
The analysis on finals showed significant condition effect (χ2 (2)=7.39, p=0.025). Further pairwise comparisons showed that synthetic Baldi is significantly better than audio only condition (χ2(1)=7.36, p=0.0067). Least square mean were listed in Table 2.<br />
<br />
Table 2. Least square mean percentages of improvement based on logistic model<br />
* Initials Finals<br />
*Audio only 53.1 34.2<br />
*Human face 54.5 39.6<br />
*Synthetic Baldi 51.5 46.4<br />
<br />
<br />
== Explanation ==<br />
<br />
It is difficult to learn to speak a language by just listening to it, especially for a second language learner at beginner’s level. Visual cues provide extra information for reach the goal of speaking “natively”. Imitation is best achieved by understanding how the organs produce the sound. Current findings support that Bao (Chinese Baldi) has significant advantage in teaching Chinese vowel pronunciation than audio alone. The human face falls between the above two methods, because it provides some useful facial information but the internal organs are not transparent. We conclude that visual speech provides significant benefit for learners to improve their pronunciation.<br />
<br />
As a node under [[coordinative learning]] cluster, [[coordination]] of visual and audio inputs is the cognitive process leads to more [[robust learning]]. <br />
<br />
== Descendents ==<br />
None.<br />
<br />
== Further information ==<br />
Massaro, D. W., Liu, Y., Chen, T. H., & Perfetti, C. A. (2006). A Multilingual Embodied Conversational Agent for Tutoring Speech and Language Learning. Proceedings of the Ninth International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP, September, Pittsburgh, PA), 825-828.Universität Bonn, Bonn, Germany.</div>Liuying