Theory Wiki - User contributions [en]

Category:Glossary

2011-08-29T07:16:23Z

Maloneypark:

The [http://www.pitt.edu/~vanlehn/PSLC/PSLC%20Theory%20Framework%20no%20projects%207Aug2006.doc current glossary] has not yet been completely updated to reflect recent work by the clusters on their glossaries.

Definitions of dependent measures for normal and robust learning are given in the this [http://www.learnlab.org/attributes/media/robust-learning-measures.ppt PowerPoint file]

NOTE: Ideal glossary entries should include an example as well as the general text description.

[[Glossary Instructions | Instructions for creating glossary entries.]]

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Bridging Principles and Examples through Analogy and Explanation

2011-08-29T07:16:12Z

Maloneypark:

==Bridging Principles and Examples through Analogy and Explanation==

Timothy J. Nokes and Kurt VanLehn

===Summary Table===

====Study 1 (In Vivo)====
{| border="1" cellpadding="5" cellspacing="0" style="text-align: left;"
| '''PIs''' || Timothy Nokes and Kurt VanLehn
|-
| '''Study Start Date''' || October, 2007
|-
| '''Study End Date''' || December, 2007
|-
| '''LearnLab Site''' || United States Naval Academy
|-
| '''Number of Students''' || 78
|-
| '''Total Participant Hours''' || 312
|-
| '''Data Shop''' || Expected Spring, 2008; Analysis on-going
|}
<br>
====Study 2 (Laboratory)====
{| border="1" cellpadding="5" cellspacing="0" style="text-align: left;"
| '''PIs''' || Timothy Nokes and Kurt VanLehn
|-
| '''Study Start Date''' || June, 2008
|-
| '''Study End Date''' || August, 2008
|-
| '''LearnLab Site''' || University of Pittsburgh
|-
| '''Number of Students''' || anticipated 60
|-
| '''Total Participant Hours''' || anticipated 240
|-
| '''Data Shop''' || Expected Fall, 2008
|}
<br>

===Abstract===
The purpose of the current work is to test the hypothesis that learning the relations between principles and examples is critical to deep understanding and [[transfer]]. It is proposed that there are at least two paths to acquiring these relations. The first path is through [[self-explanation]] of how [[worked examples]] are related to the principles. The second path is learning a schema through [[analogical comparison]] of two examples and then relating that schema to the principle. These hypotheses are tested in both a [[in vivo experiment]] in the [[Physics]] LearnLab as well as laboratory studies.

===Research Question===
The central problem addressed in this work is how to facilitate students’ deep learning of new concepts. Of particular interest is to determine what learning paths lead to a deep understanding of new concepts that enables [[robust learning]] including [[long-term retention]], [[transfer]], and [[accelerated future learning]].

===Background and Significance===
Much research in cognitive science has shown that when students first learn a new domain such as statistics or physics they rely heavily on prior examples to solve new problems (Anderson, Greeno, Kline, & Neves, 1981; Ross, 1984; VanLehn, 1998). Furthermore, laboratory studies indicate that students prefer to use examples even when they have access to written instructions or principles (LeFerve & Dixon, 1986; Ross, 1987). For example, LeFerve and Dixon (1986) showed that when learning to solve induction problems, students preferred to use the solution procedure illustrated in the example over the one described in the written instructions. Although using examples enables novices to make progress when solving new problems they are often only able to apply such knowledge to near transfer problems with similar surface features (see Reeves & Weissberg, 1994 for a review). It is principally through extended practice in the domain that students begin to develop more ‘expert-like’ abilities such as being able to ‘perceive’ and use the deep structural features of the problem (Chi, Feltovich, & Glaser, 1981) or use a forwards-working problem solving strategy (Sweller, Mawer, & Ward, 1983).

One reason that students may rely so heavily on prior examples to solve new problems is that they lack a deep understanding for how the principles are instantiated in the examples. That is, they may lack the knowledge and skills required for relating the principle components to the problem features. Some prior research by Nisbett and colleagues (Fong, Krantz, & Nisbett, 1986; Fong & Nisbett, 1991) has shown that when students are given brief training on an abstract rule (the statistical principle for the Law of Large Numbers) with illustrating examples they perform better than students trained on the rule or examples alone. This result was shown in a domain where the students were hypothesized to have an intuitive understanding of the principle prior to training. One plausible interpretation of this result is that the students used their intuitive understanding of the principle to relate the abstract rule to the illustrating examples. This possibility is intriguing and suggests that a training procedure designed to facilitate understanding of the relations between principles and examples may result in deep learning.

The current research builds on this result by postulating that learning activities designed to focus students on learning the relations between examples and principles should improve their conceptual understanding and lead to [[robust learning]]. We examine two learning paths to acquiring these relations: [[self-explanation]] and [[analogical comparison]]. [[Self-explanation]] has been shown to facilitate both procedural and conceptual learning and [[transfer]] of that knowledge to new contexts. Prior work by Chi, Bassok, Lewis, Reimann, and Glaser (1989) showed that good learners were more likely than poor learners to generate inferences relating the worked examples to the principles and concepts of the problem. This result suggests that ''prompting'' students to self-explain the relations between principles and [[worked examples]] will further facilitate learning. Of central interest to the current work is to understand how students learn to coordinate the knowledge representations of principles and examples through explanation. The second path is learning a schema through [[analogical comparison]]. Prior work has shown that [[analogical comparison]] can facilitate schema abstraction and [[transfer]] to new problems (Gentner, Lowenstein, & Thompson, 2003; Kurtz, Miao, & Gentner, 2001). However, this work has not examined how learning from problem comparison impacts understanding of an abstract principle. The current work examines how analogical comparison may help bridge students’ learning of the relations between principles and examples.

===Independent Variables===
'''Type of instruction'''
All three groups receive principle booklets providing textual descriptions of physics principles (rules) for rotational kinematics (e.g., angular velocity, angular displacement, etc.), pairs of [[worked examples]], as well as isomorphic problem solving tasks. The primary manipulation is the activity engaged in during learning.
*Control - Reading
**Participants first read through the principle booklets. Next they read through the two [[worked examples]] one at a time. Each example includes an explicit explanation/justification for each step. Next, they solve two isomorphic problems^.
*Self-Explain
**Participants first read through the principle booklets. Next they are given the first of the [[worked examples]] and are instructed to self-explain each solution step. After self-explaining they read through explanations for each step (same as control). After completing the first example they perform the same task for the second example. Next they solve one isomorphic problem^.
*Analogy
**Participants first read through the principle booklets. Next they read through the two [[worked examples]] one at a time. Each example includes an explicit explanation/justification for each step (same as control). Then they are instructed to compare each part of the examples writing a summary of the similarities and differences between the two (e.g., goals, concepts, and solution procedures). Next, they solve one isomorphic problem^.

^The control group solves two problem isomorphs whereas the self-explanation and analogy groups only solve one to control for time on task.

===Dependent Variables===
'''Learning Measures''' (manipulation check)
*Control group: Performance on practice problems
*Self-explanation group: Content of explanations
*Analogy group: Comparison summaries and content of explanations
'''Test Measures'''
*[[Normal post-test]]
**Problem solving
***Solving a problem requiring the application of the same principles, concepts, and equations but asks the student to find a different sought value (almost identical to learning problem)
***Solving a problem requiring the application of the same principles, concepts, and equations but includes additional IRRELEVANT information in the problem statement. To solve this problem correctly a student must have deeper understanding of the meaning of the variables. One cannot rely on superficial surface strategies.
*[[Transfer]]
**Multiple choice
***A novel test that assesses qualitative understanding of the concepts. Students are asked to reason about concepts and principles.

*Performance on [[Andes]] problems
**Learning curves
**Solution times
**Error rates

*[[Long-term retention]]
**Homework and Final exam performance

*[[Accelerated future learning]]
**Performance on subsequent topics (e.g., rotational dynamics) as measured by [[Andes]] performance

===Hypotheses===
*Learning the ''relations'' between principles and examples is critical to deep understanding and [[transfer]].
**[[Self-explanation]] can serve as one mechanism to facilitate this learning.
**Problem schemas may help bridge the student's understanding between principles and examples.
**[[Analogical comparison]] can serve as one mechanism to facilitate schema acquisition.

===Expected Findings===
*If learning the relations is critical for deep understanding and transfer then the groups prompted to explain relations should perform better on the test tasks than the unprompted group.
*If schema acquisition helps bridge this understanding then the Analogy+explanation group should perform best.

*Variety of test tasks will help identify what knowledge components are learned:
**Problem solving: different sought: Analogy = Self-explanation = Control; accuracy
**Problem solving: irrelevant info: Analogy = Self-explanation > Control; accuracy
**Multiple choice: Analogy = Self-explanation > Control; more likely to get understand the concepts facilitating qualitative reasoning.

*Andes performance: Analogy = Self-explanation > Control; errors rates

===Explanation===
Prompting students to explain how each step of a worked example is related to the principles facilitates the generation of inferences connecting the physics principles and concepts to the procedures and equations in the problem. These inferences serve to highlight the importance of the concepts in problem solving and increase the likelihood of future activation when solving novel problems. Furthermore, they serve as the critical links integrating and coordinating the principle [[knowledge components]] with the problem [[features]].

By comparing similarities and differences of worked examples students have an opportunity to identify the important [[features]] of the problems. After having identified the important features they can be related to the principle description through explanation.

===Descendents===
None
=== Annotated Bibliography ===
*Anderson, J. R., Greeno, J. G., Kline, P. J., & Neves, D. M. (1981). Acquisition of problem-solving skill. In J. R. Anderson (Ed.), ''Cognitive skills and their acquisition'' (pp. 191-230). Hillsdale, NJ: Erlbaum.
*Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. ''Cognitive Science, 13'', 145-182.
*Chi, M. T. H., De Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. ''Cognitive Science, 18'', 439-477.
*Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. ''Cognitive Science, 5'', 121-152.
*Dufresne, R. J., Gerace, W. J., Hardiman, P. T., & Mestre, J. P. (1992). Constraining novices to perform expertlike analyses: effects on schema acquisition. ''Journal of the Learning Sciences, 2'', 307-331.
*Fong, G. T., & Nisbett, R. E. (1991). Immediate and delayed transfer of training effects in statistical reasoning. ''Journal of Experimental Psychology: General, 120'', 34-45.
*Fong, G. T., Krantz, D. H., & Nisbett, R. E. (1986). The effects of statistical training on thinking about everyday problems. ''Cognitive Psychology, 18'', 253-292.
*Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. ''Journal of Educational Psychology, 95'', 393-408.
*Kurtz, K. J., Miao, C. H., & Gentner, D. (2001). Learning by analogical bootstrapping. ''Journal of the Learning Sciences, 10'', 417-446.
*LeFerve, J., & Dixon, P. (1986). Do written instructions need examples? Cognition and Instruction, 3, 1-30.
*Mestre, J. P. (2002). Probing adults’ conceptual understanding and transfer of learning via problem posing. ''Applied Developmental Psychology, 23'', 9-50.
*Reeves, L. M., & Weissberg, W. R. (1994). The role of content and abstract information in analogical transfer. ''Psychological Bulletin, 115'', 381-400.
*Ross, B. H. (1984). Remindings and their effects in learning a cognitive skill. ''Cognitive Psychology, 16'', 371-416.
*Sweller, Mawer, & Ward (1983). Development of expertise in mathematical problem solving. ''Journal of Experimental Psychological: General, 112'', 639-661.
*VanLehn, K. (1998). Analogy events: How examples are used during problem solving. ''Cognitive Science, 22'', 347-388.

===Further Information===

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Basic skills training

2011-08-29T07:16:00Z

Maloneypark:

==French dictation training==

==Abstract==

The goal of this project is to improve the ability of students of Elementary French to write down sentences as they hear them.
Mastering this ability involves improvements in vocabulary recognition, auditory range and precision, and control of spelling rules.
Like other studies conducted by MacWhinney and Pavlik, this work emphasizes the role of scheduling in attaining mastery. This practice
is now an in vivo component of French Online.

==Glossary==
*[[optimal spacing interval]]
*[[mastery]]
*[[spelling rules]]
*[[cue reliability]]
*[[cue availability]]
*[[transfer]]

==Research question==
This research is designed to discover the best method of producing robust learning of French dictation. Toward this end, there is an
emphasis on developing materials based on error analysis to configure correct stimulus sequencing. The specific online method examines the role
of sequencing, explicit feedback, and lexical familiarity in promoting dictation skill.

An important component of this online tutor is its ability to provide direct feedback on student errors. This is done by placing asterisks over those segments of the student's dictation that fail to match the target.

==Background and significance==
Both L1 and L2 classroom instruction in French often uses a system of dictation that seeks to teach clear recognition of words, phrases, and sentences along with proper spelling. In classroom dictatin, the teacher will pronounce a series of phrases or sentences and ask the students to write them out. The sentences are typically designed to provide information regarding orthographic ambiguities such as the contrast between the masculine adjective intelligent, the feminine form intelligente, or the plural form intelligentes. This single classroom activity incorporates four types of learning at once:
#Students must be able to recognize the words between produced by the teacher. For L1 learners, this part of dictation is relatively easy, but L2 learners may find two types of challenges here. First, L2 learners often find that French is difficult to segment into words because of frequent elision patterns and the reduced, monosyllabic nature of high frequency forms.
#Second, word recognition may be difficult in some cases for L2 learners because they have not yet learned the relevant vocabulary.
#Once words are recognized, learners must be able to map them to standard orthographic patterns. For learners with English as L1, the use of diacritics and the proper encoding of vowel patterns is a particular challenge.
#Although French spelling is largely regular, many spelling patterns depend on grammatical [[features]] such as gender, number, and verb conjugation. In order to achieve proper dictation, learners must be sensitive to these features.

After pilot testing in Fall 2005, the FOL (French Online) course now incorporates a Java-based dictation template that logs directly to DataShop. The program gives immediate feedback regarding correctness, but we have not yet provided feedback regarding the technicalities or principles of spelling.

==Dependent variables==

[[Normal post-test]] measures:
#The dependent variable is percentage correct. Correctness is scored on the word level. There is no penalty for incorrect words. Scoring is done by a Perl program.
#In addition to the overall analysis for percentage correct, we are also closely tracking error types within words. We are interested in specifying closely the spellings rules and auditory patterns that are most difficult for students.

There are no explicit [[robust learning]] measures in this study. However, a major finding was that improvements from dictation training were across-the-board and not confined to sentences used in the training. Thus, it appears that dictation trains a totally general skill.

==Independent variables==
#We are using a pretest-posttest design to measure the overall effects of the online training. We compare gain scores from students in the traditional course with no dictation training with gain scores for students in the online course with dictation training.
#We are also tracking the effects of exposure to particular sentences. In each lesson, half of the students study one list and half study another. We then test generalization across lists.

==Hypothesis==
#Our initial hypothesis was that exposure to a particular set of sentence would markedly improve the ability to spell these sentences in comparison with a parallel set of unexposed sentences. In the first round of testing, we found that this was wrong, indicating that the skill of dictation is not pegged to sentence level memories, but rather to the level of the phoneme and word.
#We have found a clear increase from pretest to posttest in dictation ability across the course.
#We further hypothesize that dictation training in French 1 will markedly improve dictation ability in French 2.
#We hypothesize that the most difficult dictation patterns will be those marked by use of diacritics, unreliable spelling patterns, the apostrophe, and difficult auditory sequences.
#When French words match up closely with English words in sound and meaning, but differ in spelling, we will expect some transfer errors.

These predictions derive from the Competition Model (MacWhinney, in press).

Errors in dictation often produce alternative sentences that are still fully meaningful. For example "Je ne nage pas, moi" (I don't swim) can be misproduced as "Je ne nage pas mal" (I don't swim poorly). Or "Il faut faire tes devoirs" (It is necessary to do your duties) is produced as "Il forte de duboise" (It strong of Dubois).

==Explanation==
The [[Competition]] Model explanation for these effects emphasizes the role of L1 transfer, cue [[reliability]], cue availability, and lexical learning as determinants of dictation learning. Availability and reliability are measured across the vocabulary. L1 transfer effects are predicted on the basis of a comparative analysis of French and English.

==Descendents==

==Annotated bibliography==
*Bonin, P., Fayol, M., & Pacton, S. (2001). La production verbale écrite: évidences en faveur d'une (relative) autonomie de l'écrit. Psychologie Francaise, 46, 77-88.
*Bonin, P., Fayol, M., & Gombert, J. (1998). An experimental study of lexical access in the writing and naming of isolated words. Inernational Journal of Psychology, 33, 269-286.
*Content, A., Mousty, P., & Radeau, M. (1990). Brulex: Une base de données lexicales informatisée pour le français écrit et parlé. L'Année Psychologique, 90, 551-566.
*MacWhinney, B. (2006). A unified model. In N. Ellis & P. Robinson (Eds.), Handbook of Cognitive Linguistics and Second Language Acquisition. Mahwah, NJ: Lawrence Erlbaum Press.

[[Category:Study]]

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