https://learnlab.org/mediawiki-1.44.2/api.php?action=feedcontributions&feedformat=atom&user=AlevenTheory Wiki - User contributions [en]2026-05-01T20:33:28ZUser contributionsMediaWiki 1.44.2https://learnlab.org/mediawiki-1.44.2/index.php?title=PSLC_GradStudents&diff=10938PSLC GradStudents2010-08-25T13:42:29Z<p>Aleven: </p>
<hr />
<div>The purpose of this page is to serve as a repository of information relevant for grad students. We hope to maintain this page as a repository of current and relevant information for graduate students currently affiliated with the PSLC, as well as grad students who hope to be in the PSLC. <br />
<br />
== Announcements==<br />
<br />
== Meeting Notes==<br />
<br />
== FAQs==<br />
<br />
'''1. What does it take to be a PSLC grad student?'''<br />
<br />
Well, there are basically three ways you can be considered a PSLC grad student. <br />
a. You work on a project that receives funding from the PSLC.<br />
b. Your advisor or collaborator receives funding from the PSLC and asks you to be involved.<br />
c. You want to be a PSLC grad student.<br />
<br />
<br />
'''2. What types of opportunities does the PSLC have for a grad student like me?''' <br />
<br />
There are a variety of different levels of involvement and types of activities that the PSLC offers. <br />
<br />
For the casual grad student, the PSLC organizes a speaker series with talks that may be of interest to students interested in the learning sciences. These are open to whomever wishes to go. There are also monthly lunch meetings where people associated with the PSLC can give a talk on their work. <br />
<br />
The grad student community also hopes to organize events catered toward grad students, with topics like applying for grants, finding jobs, collaboration with people at other universities, etc. These are also open to the public. <br />
<br />
For those who wish to get more involved, the grad student community also has monthly meetings to discuss center-wide issues, read and discuss articles we believe are relevant, plan future events, etc. Again, these are open to the public. <br />
<br />
Finally, each thrust has regular or semi-regular meetings to discuss the thrust's theoretical framework, set the research agenda, and discuss the progress of projects within that thrust. While these are open to anyone, they're probably of limited interest unless you currently have or have had a project affiliated with the thrust. <br />
<br />
<br />
<br />
'''3. What is expected of me as a PSLC grad student?'''<br />
<br />
If you receive funding from the PSLC, you are expected, to the extent it is possible, to attend the thrust meetings for your relevant thrust, and attend the monthly PSLC lunches. The grad student community also encourages you to come to the grad student monthly meetings, of course.<br />
<br />
If you don't receive funding from the PSLC, but still wish to be a part of the grad student community, your level of involvement is up to you. <br />
<br />
<br />
'''How do I find out about upcoming talks/meetings/events?'''<br />
<br />
One option is to check the Announcements section of this page. A possibly better option would be to get on our mailing list. To do that, e-mail Jo Bodnar at jobodnar AT cs.cmu.edu and ask to be put on the PSLC general mailing list and grad student mailing list. <br />
<br />
There is also a regularly updated calendar at our main webpage (learnlab.org) that is updated regularly and gives a fairly complete account of most PSLC events.<br />
<br />
<br />
<br />
4. '''I already consider myself a PSLC grad, and want to be included on this page! What do I have to do?'''<br />
<br />
Well the great thing about the wiki page is that anybody can update it whenever they want! So, if you have an account here, and you know how to edit tables, you can just log in and add yourself! <br />
<br />
If you don't have an account already, you can easily request one (NOTE: I forget how to do it- I'll need to add that). Once you have an account, you can just click "Edit" above the table, and you can add yourself. <br />
<br />
<br />
<br />
5. '''But that's such a pain! Isn't there an easier way?!'''<br />
<br />
There sure is! If you don't want to make all that effort just to have your name and e-mail address on a page, just send your info to our Wikimaster (yep, we made that word up!), Ben Friedline, at bef25 AT pitt.edu, and he'll put it on here.<br />
<br />
== Who are the PSLC grads? ==<br />
<br />
{| border = "1"<br />
|-<br />
! Grad Student Name<br />
! University/Department<br />
! Advisor<br />
! E-mail<br />
! Bio<br />
! Personal Webpage<br />
! PSLC Projects<br />
! Other<br />
|-<br />
| Colleen Davy<br />
| Carnegie Mellon/Psychology<br />
| Brian MacWhinney<br />
| cdavy1@andrew.cmu.edu<br />
| I am interested in how adult second language learners develop fluent speaking skills in their second language.<br />
|<br />
| [http://www.learnlab.org/research/wiki/index.php/Davy_%26_MacWhinney_-_Spanish_Sentence_Production Spanish Sentence Production]<br />
|<br />
<br />
|}<br />
<br />
== Science of Learning Relevant Courses ==<br />
The PIER program offers three courses -- see the [www.cmu.edu/pier/ PIER web site].<br />
<br />
See also the courses taught be any of the PSLC faculty.<br />
<br />
(Please add the names of relevant courses and web pointers if possible!)<br />
<br />
<br />
<pre><br />
05832 / 05432 Cognitive Modeling & Intelligent Tutoring Systems<br />
3:00pm-4:20pm, Tuesdays and Thursdays, Fall 2010<br />
Room 3002, Newell-Simon Hall, Carnegie Mellon University<br />
9 units<br />
Dr. Vincent Aleven, aleven@cs.cmu.edu<br />
</pre><br />
<br />
Students in this course will learn about the Cognitive Tutor technology that has been demonstrated to dramatically enhance student learning in domains like math, science, and computer programming. This type of tutoring software is currently in use in 2,700 schools around the country and is used extensively as platform for learning sciences research. The technology is grounded in artificial intelligence, cognitive psychology, and cognitive task analysis. Students will learn data-driven and theoretical methods for analyzing human problem solving and will learn to use such data to inform the design of intelligent tutoring systems. Course projects will focus on the development of an intelligent tutor using CTAT, the Cognitive Tutor Authoring Tools (see http://ctat.pact.cs.cmu.edu). Some assignments will focus on creating cognitive models in the Jess production rule modeling language.<br />
<br />
Students should either have programming skills, or experience in the cognitive psychology of human problem solving, or HCI / design skills, or permission from the instructor.</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Metacognition_and_Motivation&diff=10134Metacognition and Motivation2009-11-25T00:51:38Z<p>Aleven: </p>
<hr />
<div>The Metacognition and Motivation thrust has two broad goals, 1) to develop a better understanding of how metacognitive processes and motivation interact with learner factors to influence robust student learning outcomes and 2) to test whether and how student learning environments can leverage improved metacognition and motivation to increase the robustness of student learning. Our research will focus on a small number of metacognitive abilities (e.g., help seeking, self-explanation, interpreting peer feedback, and interpreting textual descriptions of domain principles), and a broader range of affective and motivational variables including: challenge perception, boredom, frustration, performance goals, and off-task behavior.<br />
<br />
The Metacognition and Motivation thrust builds on the Coordinative Learning (CL) cluster, while bringing a significant shift of focus. The M&M thrust continues some of the work in the Coordinative Learning cluster that focused on the metacognitive aspects of coordinating multiple sources of information, such as studies on analogical comparison and self-explanation of examples by Nokes, and studies on diagrammatic self-explanation by Aleven and Butcher. It will also build on work done in other thrusts, for example the work done by Hausmann and VanLehn on scaffolding self-explanations in peer collaborative settings, the work on help seeking by Roll, Aleven, et al, and the work on studying [[gaming the system]] and [[off-task behavior]] by Baker et al. In addition, the M&M thrust aims to place greater emphasis on issues of motivation within learning sciences research than has been done so far within the PSLC.<br />
<br />
We have recruited three senior consultants who are helping to increase both the quality of the Metacognition and Motivation research and its visibility within broader communities of metacognition and motivation researchers. They are: Dr. Barry J. Zimmerman, a pre-eminent scholar in metacognition and motivation (e.g., Schunk & Zimmerman, 2008), Dr. Josh Aronson, a distinguished expert in stereotypes, self-esteem, motivation, and attitudes, and Dr. Andrew Elliott, a well-known expert in achievement motivation and social motivation. <br />
<br />
We will pursue the following three broad research directions:<br />
''Create and validate automated detectors for affect, motivation, and meta-cognition''. We will start by enhancing the LearnLab infrastructure with technology for automatically monitoring metacognitive and affective variables, at a much finer grain-size, over longer durations, and for more students, than has been previously possible. <br />
<br />
Specifically, we combine observational and questionnaire data with student log data (e.g. response times, patterns of activity), to develop machine-learned models that monitor, in real-time and moment-by-moment, affective, motivational, and metacognitive variables in interactive learning environments. In particular, we will develop detectors of such constructs as [[gaming the system]], [[off-task behavior]], [[help-seeking]], boredom, frustration, flow, perception of challenge, self-efficacy, and performance goals. Once created, these detectors will only draw on information that is available to the learning environment in the normal course of its operation (student log data at the keystroke level, timing, and semantic levels), without requiring extra sensors, enabling these detectors to be used in authentic, unmodified learning settings. The combination of machine learning with observational and questionnaire data has already been successful at detecting a limited set of relevant constructs such as perception of challenge (de Vicente & Pain, 2002), [[gaming the system]] (Baker et al., 2008), and [[off-task behavior]] (Baker, 2007). <br />
<br />
These detectors will be implemented in learning software used in multiple LearnLabs, and will enable PSLC researchers across thrusts to study motivation and metacognition as mediating variables when evaluating interventions aimed at enhancing the robustness of student learning.<br />
<br />
''Evaluate interventions aimed at supporting different metacognitive abilities''. The PSLC’s particular strength in this area is studying metacognition within the context of interactive learning environments. A key question is whether such environments can be as effective in fostering or supporting metacognitive skills as they have been in improving domain-specific learning. A number of recent in vivo experiments have revealed significantly improved domain learning among students given metacognitive tutoring support for self-explanation (Aleven & Koedinger, 2002), error-correction (Mathan & Koedinger, 2005), video-based prompting and peer collaborative scaffolding of self-explanation (Craig et al. 2007, 2008, submitted; Hausmann & Vanlehn, 2007a, 2007b), and remedial instruction for material missed through meta-cognitive errors (Baker et al, 2006). We will build on this earlier work, with interventions that attempt to support four metacognitive abilities: help seeking, self-explanation, interpreting peer feedback, and interpreting textual descriptions of domain principles. An important goal in the Metacognition and Motivation thrust is to develop interactive learning environments that can help students internalize this support, solidifying and generalizing their metacognitive skills so they will no longer need external support in future learning situations, and can approach a new domain with a general set of skills that can facilitate learning.<br />
<br />
To evaluate the effectiveness of the interventions aimed at enhancing metacognition, we will look not only at the normal indicators of robust domain-level learning (i.e., transfer and retention), but also (and in particular) at whether future learning is accelerated. As appropriate, the detectors for metacognitive behaviors developed under goal 1 will be used to evaluate whether the targeted metacognitive behavior is enhanced both while the intervention is in place (as a manipulation check) as well as in future learning situations (to evaluate its role as a potential cause of accelerated future learning).<br />
<br />
''Evaluate interventions aimed at inducing positive affect and motivation to persist''. As a complement to investigating how cognitive learning principles can improve and support metacognitive ability, we will also study the effect of interventions aimed at enhancing motivation, as a way of uncovering relationships between motivation, affect, and metacognition in interactive learning environments. We will focus on two types of interventions. First, inspired by the motivational impact of computer games, we will (a) identify features of games that could be adopted for use in interactive learning environments, and (b) evaluate the effect of adding these features. Our initial investigations will focus on trivial choice, “boss problems,” (challenge problems – e.g. Siegler & Jenkins, 1981, designed to look like end-of-level bosses in video games) student control over challenge level, and rewards. In addition, we will evaluate the effect of putting the student in a care-taking role where they need to tutor a synthetic student. (The synthetic student will be driven by the PSLC’s SimStudent learning agent technology.) Second, we will evaluate social interventions aimed at enhancing motivation: peer pressure and comparison with peers, including competition with simulated students.<br />
<br />
<br />
<br />
== Descendants ==<br />
<br />
*[[Nokes - Questionnaires]]<br />
*[[Baker - Building Generalizable Fine-grained Detectors]]<br />
*[[Roll - Inquiry]]<br />
*[[Pavlik - Dificulty and Strategy]]<br />
*[[Nokes - Dialectical Interaction and Robust Learning]]<br />
*[[Math Game Elements]]<br />
*[[Geometry Greatest Hits]]<br />
*[[Aleven - Causal Argumentation Game]]</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Metacognition_and_Motivation&diff=10133Metacognition and Motivation2009-11-25T00:48:48Z<p>Aleven: </p>
<hr />
<div>The Metacognition and Motivation thrust builds on the Coordinative Learning (CL) cluster, while bringing a significant shift of focus. Work within the CL cluster addressed specific versions of the general cluster question: “When and how does coordinating multiple sources of information or lines of reasoning increase robust learning?” <br />
<br />
''Examples and Explanations.'' One of these specific questions is “when and how does the use of worked examples in problem-solving instruction enhance robust learning?” Studies in geometry (c.f., Salden, Aleven, Renkl, & Schwonke, 2008; Schwonke, Wittwer, Aleven, Salden, Krieg, & Renkl, in press) showed that (interactive) worked examples are an effective supplement to tutored problem solving (supported by a Cognitive Tutor), leading to more robust learning. Studies in the Chemistry LearnLab course (c.f., McLaren, Lim, & Koedinger, 2008) found that the combination of worked examples and tutored problem solving improved the efficiency of robust learning. Studies in the Physics LearnLab course found that analogical comparisons and self-explanations of worked examples facilitate conceptual learning (Nokes & VanLehn, 2008).<br />
<br />
''Visualization and multi-modal sources.'' The second sub-question is, “When does adding visualizations or other multi-modal input enhance robust learning and how do we best support students in coordinating these sources?” A study by Liu, Perfetti, and Mitchell in the Chinese LearnLab took inspiration from the machine learning theory of co-training (Blum & Mitchell, 1998) to explore whether students might benefit from the combination of multiple sources of information. The study confirmed the general prediction (derived from machine learning research) that co-training improves learning. However, it failed to confirm a more specific prediction, that sources need to be non-correlated to enhance learning – it may be that lack of correlation is necessary for machines, but not for humans. Another series of studies in this sub-cluster has explored the potential benefits of a tighter integration of text and diagrams in the Geometry LearnLab. Butcher and Aleven (2007; 2008) found benefits for robust learning of more contiguous text and diagram presentation. Other studies exploring research questions within the [[Coordinative Learning]] cluster can be found at the [[Coordinative Learning]] page.<br />
<br />
The M&M thrust will continue some of the work in the Coordinative Learning cluster that focused on the metacognitive aspects of coordinating multiple sources of information, such as studies on analogical comparison and self-explanation of examples by Nokes, and studies on diagrammatic self-explanation by Aleven and Butcher. It will also build on work done in other thrusts, for example the work done by Hausmann and VanLehn on scaffolding self-explanations in peer collaborative settings, the work on help seeking by Roll, Aleven, et al, and the work on studying [[gaming the system]] and [[off-task behavior]] by Baker et al. In addition, the M&M thrust aims to place greater emphasis on issues of motivation within learning sciences research than has been done so far within the PSLC.<br />
<br />
Specifically, the Metacognition and Motivation thrust has two broad goals, 1) to develop a better understanding of how metacognitive processes and motivation interact with learner factors to influence robust student learning outcomes and 2) to test whether and how student learning environments can leverage improved metacognition and motivation to increase the robustness of student learning. Our research will focus on a small number of metacognitive abilities (e.g., help seeking, self-explanation, interpreting peer feedback, and interpreting textual descriptions of domain principles), and a broader range of affective and motivational variables including: challenge perception, boredom, frustration, performance goals, and off-task behavior.<br />
<br />
During the upcoming year (year 5 of the PSLC), we will focus on planning and preparing for the Metacognition and Motivation thrust’s activities. The thrust will meet regularly, replacing the regular meetings of the Coordinative Learning cluster. We will conduct research that begins to address the main research questions outlined above, through, for example, projects to create tutors with game features as a way of enhancing student motivation. The thrust’s main research activities will occur subsequent to the PSLC renewal, which we hope will start in October 2009. <br />
<br />
We have recruited three senior and highly distinguished consultants who will help to increase both the quality of the Metacognition and Motivation research and its visibility within broader communities of metacognition and motivation researchers. They are: Dr. Barry J. Zimmerman, a pre-eminent scholar in metacognition and motivation (e.g., Schunk & Zimmerman, 2008), Dr. Josh Aronson, a distinguished expert in stereotypes, self-esteem, motivation, and attitudes, and Dr. Andrew Elliott, a well-known expert in achievement motivation and social motivation. <br />
<br />
We will pursue the following three broad research directions:<br />
''Create and validate automated detectors for affect, motivation, and meta-cognition''. We will start by enhancing the LearnLab infrastructure with technology for automatically monitoring metacognitive and affective variables, at a much finer grain-size, over longer durations, and for more students, than has been previously possible. <br />
<br />
Specifically, we combine observational and questionnaire data with student log data (e.g. response times, patterns of activity), to develop machine-learned models that monitor, in real-time and moment-by-moment, affective, motivational, and metacognitive variables in interactive learning environments. In particular, we will develop detectors of such constructs as [[gaming the system]], [[off-task behavior]], [[help-seeking]], boredom, frustration, flow, perception of challenge, self-efficacy, and performance goals. Once created, these detectors will only draw on information that is available to the learning environment in the normal course of its operation (student log data at the keystroke level, timing, and semantic levels), without requiring extra sensors, enabling these detectors to be used in authentic, unmodified learning settings. The combination of machine learning with observational and questionnaire data has already been successful at detecting a limited set of relevant constructs such as perception of challenge (de Vicente & Pain, 2002), [[gaming the system]] (Baker et al., 2008), and [[off-task behavior]] (Baker, 2007). <br />
<br />
These detectors will be implemented in learning software used in multiple LearnLabs, and will enable PSLC researchers across thrusts to study motivation and metacognition as mediating variables when evaluating interventions aimed at enhancing the robustness of student learning.<br />
<br />
''Evaluate interventions aimed at supporting different metacognitive abilities''. The PSLC’s particular strength in this area is studying metacognition within the context of interactive learning environments. A key question is whether such environments can be as effective in fostering or supporting metacognitive skills as they have been in improving domain-specific learning. A number of recent in vivo experiments have revealed significantly improved domain learning among students given metacognitive tutoring support for self-explanation (Aleven & Koedinger, 2002), error-correction (Mathan & Koedinger, 2005), video-based prompting and peer collaborative scaffolding of self-explanation (Craig et al. 2007, 2008, submitted; Hausmann & Vanlehn, 2007a, 2007b), and remedial instruction for material missed through meta-cognitive errors (Baker et al, 2006). We will build on this earlier work, with interventions that attempt to support four metacognitive abilities: help seeking, self-explanation, interpreting peer feedback, and interpreting textual descriptions of domain principles. An important goal in the Metacognition and Motivation thrust is to develop interactive learning environments that can help students internalize this support, solidifying and generalizing their metacognitive skills so they will no longer need external support in future learning situations, and can approach a new domain with a general set of skills that can facilitate learning.<br />
<br />
To evaluate the effectiveness of the interventions aimed at enhancing metacognition, we will look not only at the normal indicators of robust domain-level learning (i.e., transfer and retention), but also (and in particular) at whether future learning is accelerated. As appropriate, the detectors for metacognitive behaviors developed under goal 1 will be used to evaluate whether the targeted metacognitive behavior is enhanced both while the intervention is in place (as a manipulation check) as well as in future learning situations (to evaluate its role as a potential cause of accelerated future learning).<br />
<br />
''Evaluate interventions aimed at inducing positive affect and motivation to persist''. As a complement to investigating how cognitive learning principles can improve and support metacognitive ability, we will also study the effect of interventions aimed at enhancing motivation, as a way of uncovering relationships between motivation, affect, and metacognition in interactive learning environments. We will focus on two types of interventions. First, inspired by the motivational impact of computer games, we will (a) identify features of games that could be adopted for use in interactive learning environments, and (b) evaluate the effect of adding these features. Our initial investigations will focus on trivial choice, “boss problems,” (challenge problems – e.g. Siegler & Jenkins, 1981, designed to look like end-of-level bosses in video games) student control over challenge level, and rewards. In addition, we will evaluate the effect of putting the student in a care-taking role where they need to tutor a synthetic student. (The synthetic student will be driven by the PSLC’s SimStudent learning agent technology.) Second, we will evaluate social interventions aimed at enhancing motivation: peer pressure and comparison with peers, including competition with simulated students.<br />
<br />
<br />
<br />
== Descendants ==<br />
<br />
*[[Nokes - Questionnaires]]<br />
*[[Baker - Building Generalizable Fine-grained Detectors]]<br />
*[[Roll - Inquiry]]<br />
*[[Pavlik - Dificulty and Strategy]]<br />
*[[Nokes - Dialectical Interaction and Robust Learning]]<br />
*[[Math Game Elements]]<br />
*[[Geometry Greatest Hits]]<br />
*[[Aleven - Causal Argumentation Game]]</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Metacognition_and_Motivation&diff=10132Metacognition and Motivation2009-11-25T00:47:38Z<p>Aleven: </p>
<hr />
<div>The Metacognition and Motivation thrust builds on the Coordinative Learning (CL) cluster, while bringing a significant shift of focus. Work within the CL cluster addressed specific versions of the general cluster question: “When and how does coordinating multiple sources of information or lines of reasoning increase robust learning?” <br />
<br />
''Examples and Explanations.'' One of these specific questions is “when and how does the use of worked examples in problem-solving instruction enhance robust learning?” Studies in geometry (c.f., Salden, Aleven, Renkl, & Schwonke, 2008; Schwonke, Wittwer, Aleven, Salden, Krieg, & Renkl, in press) showed that (interactive) worked examples are an effective supplement to tutored problem solving (supported by a Cognitive Tutor), leading to more robust learning. Studies in the Chemistry LearnLab course (c.f., McLaren, Lim, & Koedinger, 2008) found that the combination of worked examples and tutored problem solving improved the efficiency of robust learning. Studies in the Physics LearnLab course found that analogical comparisons and self-explanations of worked examples facilitate conceptual learning (Nokes & VanLehn, 2008).<br />
<br />
''Visualization and multi-modal sources.'' The second sub-question is, “When does adding visualizations or other multi-modal input enhance robust learning and how do we best support students in coordinating these sources?” A study by Liu, Perfetti, and Mitchell in the Chinese LearnLab took inspiration from the machine learning theory of co-training (Blum & Mitchell, 1998) to explore whether students might benefit from the combination of multiple sources of information. The study confirmed the general prediction (derived from machine learning research) that co-training improves learning. However, it failed to confirm a more specific prediction, that sources need to be non-correlated to enhance learning – it may be that lack of correlation is necessary for machines, but not for humans. Another series of studies in this sub-cluster has explored the potential benefits of a tighter integration of text and diagrams in the Geometry LearnLab. Butcher and Aleven (2007; 2008) found benefits for robust learning of more contiguous text and diagram presentation. Other studies exploring research questions within the [[Coordinative Learning]] cluster can be found at the [[Coordinative Learning]] page.<br />
<br />
The M&M thrust will continue some of the work in the Coordinative Learning cluster that focused on the metacognitive aspects of coordinating multiple sources of information, such as studies on analogical comparison and self-explanation of examples by Nokes, and studies on diagrammatic self-explanation by Aleven and Butcher. It will also build on work done in other thrusts, for example the work done by Hausmann and VanLehn on scaffolding self-explanations in peer collaborative settings, the work on help seeking by Roll, Aleven, et al, and the work on studying [[gaming the system]] and [[off-task behavior]] by Baker et al. In addition, the M&M thrust aims to place greater emphasis on issues of motivation within learning sciences research than has been done so far within the PSLC.<br />
<br />
Specifically, the Metacognition and Motivation thrust has two broad goals, 1) to develop a better understanding of how metacognitive processes and motivation interact with learner factors to influence robust student learning outcomes and 2) to test whether and how student learning environments can leverage improved metacognition and motivation to increase the robustness of student learning. Our research will focus on a small number of metacognitive abilities (e.g., help seeking, self-explanation, interpreting peer feedback, and interpreting textual descriptions of domain principles), and a broader range of affective and motivational variables including: challenge perception, boredom, frustration, performance goals, and off-task behavior.<br />
<br />
During the upcoming year (year 5 of the PSLC), we will focus on planning and preparing for the Metacognition and Motivation thrust’s activities. The thrust will meet regularly, replacing the regular meetings of the Coordinative Learning cluster. We will conduct research that begins to address the main research questions outlined above, through, for example, projects to create tutors with game features as a way of enhancing student motivation. The thrust’s main research activities will occur subsequent to the PSLC renewal, which we hope will start in October 2009. <br />
<br />
We have recruited three senior and highly distinguished consultants who will help to increase both the quality of the Metacognition and Motivation research and its visibility within broader communities of metacognition and motivation researchers. They are: Dr. Barry J. Zimmerman, a pre-eminent scholar in metacognition and motivation (e.g., Schunk & Zimmerman, 2008), Dr. Josh Aronson, a distinguished expert in stereotypes, self-esteem, motivation, and attitudes, and Dr. Andrew Elliott, a well-known expert in achievement motivation and social motivation. <br />
<br />
We will pursue the following three broad research directions:<br />
''Create and validate automated detectors for affect, motivation, and meta-cognition''. We will start by enhancing the LearnLab infrastructure with technology for automatically monitoring metacognitive and affective variables, at a much finer grain-size, over longer durations, and for more students, than has been previously possible. <br />
<br />
Specifically, we combine observational and questionnaire data with student log data (e.g. response times, patterns of activity), to develop machine-learned models that monitor, in real-time and moment-by-moment, affective, motivational, and metacognitive variables in interactive learning environments. In particular, we will develop detectors of such constructs as [[gaming the system]], [[off-task behavior]], [[help-seeking]], boredom, frustration, flow, perception of challenge, self-efficacy, and performance goals. Once created, these detectors will only draw on information that is available to the learning environment in the normal course of its operation (student log data at the keystroke level, timing, and semantic levels), without requiring extra sensors, enabling these detectors to be used in authentic, unmodified learning settings. The combination of machine learning with observational and questionnaire data has already been successful at detecting a limited set of relevant constructs such as perception of challenge (de Vicente & Pain, 2002), [[gaming the system]] (Baker et al., 2008), and [[off-task behavior]] (Baker, 2007). <br />
<br />
These detectors will be implemented in learning software used in multiple LearnLabs, and will enable PSLC researchers across thrusts to study motivation and metacognition as mediating variables when evaluating interventions aimed at enhancing the robustness of student learning.<br />
<br />
''Evaluate interventions aimed at supporting different metacognitive abilities''. The PSLC’s particular strength in this area is studying metacognition within the context of interactive learning environments. A key question is whether such environments can be as effective in fostering or supporting metacognitive skills as they have been in improving domain-specific learning. A number of recent in vivo experiments have revealed significantly improved domain learning among students given metacognitive tutoring support for self-explanation (Aleven & Koedinger, 2002), error-correction (Mathan & Koedinger, 2005), video-based prompting and peer collaborative scaffolding of self-explanation (Craig et al. 2007, 2008, submitted; Hausmann & Vanlehn, 2007a, 2007b), and remedial instruction for material missed through meta-cognitive errors (Baker et al, 2006). We will build on this earlier work, with interventions that attempt to support four metacognitive abilities: help seeking, self-explanation, interpreting peer feedback, and interpreting textual descriptions of domain principles. An important goal in the Metacognition and Motivation thrust is to develop interactive learning environments that can help students internalize this support, solidifying and generalizing their metacognitive skills so they will no longer need external support in future learning situations, and can approach a new domain with a general set of skills that can facilitate learning.<br />
<br />
To evaluate the effectiveness of the interventions aimed at enhancing metacognition, we will look not only at the normal indicators of robust domain-level learning (i.e., transfer and retention), but also (and in particular) at whether future learning is accelerated. As appropriate, the detectors for metacognitive behaviors developed under goal 1 will be used to evaluate whether the targeted metacognitive behavior is enhanced both while the intervention is in place (as a manipulation check) as well as in future learning situations (to evaluate its role as a potential cause of accelerated future learning).<br />
<br />
''Evaluate interventions aimed at inducing positive affect and motivation to persist''. As a complement to investigating how cognitive learning principles can improve and support metacognitive ability, we will also study the effect of interventions aimed at enhancing motivation, as a way of uncovering relationships between motivation, affect, and metacognition in interactive learning environments. We will focus on two types of interventions. First, inspired by the motivational impact of computer games, we will (a) identify features of games that could be adopted for use in interactive learning environments, and (b) evaluate the effect of adding these features. Our initial investigations will focus on trivial choice, “boss problems,” (challenge problems – e.g. Siegler & Jenkins, 1981, designed to look like end-of-level bosses in video games) student control over challenge level, and rewards. In addition, we will evaluate the effect of putting the student in a care-taking role where they need to tutor a synthetic student. (The synthetic student will be driven by the PSLC’s SimStudent learning agent technology.) Second, we will evaluate social interventions aimed at enhancing motivation: peer pressure and comparison with peers, including competition with simulated students.<br />
<br />
<br />
<br />
== Descendants ==<br />
<br />
*[[Nokes - Questionnaires]]<br />
*[[Baker - Building Generalizable Fine-grained Detectors]]<br />
*[[Roll - Inquiry]]<br />
*[[Pavlik - Dificulty and Strategy]]<br />
*[[Nokes - Dialectical Interaction and Robust Learning]]<br />
*[[Aleven - Fractions Tutor With Game Elements]]<br />
*[[Geometry Greatest Hits]]<br />
*[[Aleven - Causal Argumentation Game]]</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Talk:Does_learning_from_examples_improved_tutored_problem_solving%3F&diff=9946Talk:Does learning from examples improved tutored problem solving?2009-10-24T00:02:39Z<p>Aleven: </p>
<hr />
<div>[VA, October, 2009] Authors listed are Renkl, Aleven, Salden, but clearly this is a Hausman study. The title may actually be from the Renkl et al. project.</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Talk:Does_learning_from_examples_improved_tutored_problem_solving%3F&diff=9945Talk:Does learning from examples improved tutored problem solving?2009-10-24T00:01:40Z<p>Aleven: New page: Authors listed are Renkl, Aleven, Salden, but clearly this is a Hausman study. The title may actually be from the Renkl et al. project.</p>
<hr />
<div>Authors listed are Renkl, Aleven, Salden, but clearly this is a Hausman study. The title may actually be from the Renkl et al. project.</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9351Geometry Greatest Hits2009-05-15T01:36:29Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || 98<br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
* [[Visual-verbal integration]] principle<br />
* [[Worked example principle]]<br />
* [[Prompted self-explanation principle]]<br />
* Accurate knowledge decomposition principle (part of the complete and efficient practice principle)<br />
* Accurate knowledge estimates principle (part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
PSLC’s in vivo methodology generally focuses on testing one principle at a time.<br />
This approach is useful for understanding which principles work, how they work, and what their boundary conditions are.<br />
Testing combinations of principles is useful also, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic.<br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
=== Completed experiments===<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
<br />
=== Planned experiments===<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Independent variables ===<br />
<br />
The Greatest Hits version of the tutor had the following features, which are supported by prior PSLC research<br />
<br />
* integrated problem format (symbolic information integrated in the diagram; all interaction happens in the diagram)<br />
* non-interactive conceptual example sets at the beginning of each curricular unit<br />
* interactive worked examples at the beginning of each curricular, faded in an individualized manner<br />
* diagrammatic self-explanations of incorrect steps<br />
* tuned knowledge-tracing parameters to achieve more better individualized problem sequences (avoiding over-practice and under-practice)<br />
* employed new knowledge-tracing algorithm that estimated the probability of guesses and slips in a contextual manner (to improve the accuracy of student modeling, which in turn better individualized problem sequences)<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Math_Game_Elements&diff=9350Math Game Elements2009-05-15T01:24:21Z<p>Aleven: </p>
<hr />
<div>== Improving student affect through adding game elements to mathematics LearnLabs ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Ryan Baker, Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || Adriana de Carvalho (Research Associate, CMU HCII); Owen Durni, and Matt Morrill (Undergraduate research assistant, and former PSLC summer intern)<br />
<br />
|-<br />
| '''Study Start Date''' || Fall, 2009<br />
|-<br />
| '''Study End Date''' || <br />
|-<br />
| '''LearnLab Site''' || <br />
|-<br />
| '''LearnLab Course''' || Middle-School Mathematics<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || <br />
|}<br />
=== Abstract ===<br />
There is much evidence to believe that games are fun. Can we incorporate some of the features that make games fun into intelligent tutors, in a way that improves motivation, generates positive affect, and improves the robustness of student learning? Specifically, what happens if we take game elements known to be effective such as fantasy, competition, and trivial choice, and embed into tutors already known to promote learning, using principles in PSLC theoretical framework? In the current project (which started in Year 5 of the PSLC), we are investigating the effect of adding game elements to an existing set of fractions tutors developed by Martina Rau (Rau, Aleven, & Rummel, in press) for a different PSLC project. The game elements comprise a fantasy soccer game, where success in the soccer game depends on learning progress in the factions tutors.<br />
<br />
=== Background & Significance ===<br />
Games and tutors appear to have complementary strengths. The current project is an attempt to have the best of both worlds. We investigate one particular way of integrating game elements and learning content.<br />
<br />
How best to integrate learning content and game elements has been the subject of much theorizing, with some authors arguing that optimal learning requires that the learning content and game world be mutually dependent (Lepper & Malone, 1987), and others arguing that the learning should be embedded in the game’s core mechanic (Habgood, 2007). However, such theories ignore the real-world success (at least in anecdotal reports from teachers and students) of environments that feature a much looser integration between learning and motivational embellishments (e.g., FirstInMath). Given that a loose coupling between game elements and learning activities is far easier to implement (since it avoids the difficult problem of embedding math problems in a storyline or game context, hard to do especially if the learning content is to be adapted to individual students’ learning results), it stands to reason to investigate this option first. It may well be that as long as the game features are “cool,” the degree of integration is not really a strong factor. As mentioned, the success of for example the motivational embellishments in FirstInMath certainly suggest so.<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
=== Independent Variables ===<br />
We are investigating the effect of loosely coupling the fractions tutors to a parallel fantasy world. This fantasy world was designed in a participatory design process involving 6th graders (the target population). The main theme (soccer) was chosen because it will appeal to the taste of both girls and boys.<br />
<br />
In the fantasy world, a soccer game takes place on a tropical beach between a motley crew of friendly animal characters and a group of invading pirates. This unfriendly bunch is intent on taking over the beach, a disaster that can be averted only by beating the pirates at the soccer game. The student regularly switches between the tutors and the fantasy world (in a manner controlled largely by the system). During each visit to the fantasy world, the student selects a game move (e.g., a deep pass, or risky dribble). The outcome of that move will not be known until the student’s next visit to the fantasy world, and the probability of success of that move depends on the student’s performance on the intervening fractions problems. (It may be clear that this coupling between game elements and learning is loose: different learning content could be slotted in without changing the game.) <br />
<br />
=== Dependent Variables ===<br />
We will evaluate whether this game leads to increased learning, compared to a standard tutor without the game elements, and whether any improvement in learning we may observe is mediated by increased motivation and more positive affect. Affect will be assessed by means of field observations, motivation (interest, self-efficacy) by means of questionnaires. We will evaluate both whether the game leads to greater persistence (when student have the option to do something else), and whether it leads to greater learning even when the set of learning activities (but not time) is held constant (which if true would mean that the game elements lead to deeper processing during the same set of learning activities).<br />
<br />
=== Planned Experiments ===<br />
We will conduct an in-vivo experiment comparing the tutor with game features against the regular tutor.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Math_Game_Elements&diff=9349Math Game Elements2009-05-15T01:23:12Z<p>Aleven: </p>
<hr />
<div>== Improving student affect through adding game elements to mathematics LearnLabs ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Ryan Baker, Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || Adriana de Carvalho (Research Associate, CMU HCII); Owen Durni, and Matt Morrill (Undergraduate research assistant, and former PSLC summer intern)<br />
<br />
|-<br />
| '''Study Start Date''' || Fall, 2009<br />
|-<br />
| '''Study End Date''' || <br />
|-<br />
| '''LearnLab Site''' || <br />
|-<br />
| '''LearnLab Course''' || Middle-School Mathematics<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || <br />
|}<br />
=== Abstract ===<br />
There is much evidence to believe that games are fun. Can we incorporate some of the features that make games fun into intelligent tutors, in a way that improves motivation, generates positive affect, and improves the robustness of student learning? Specifically, what happens if we take game elements known to be effective such as fantasy, competition, and trivial choice, and embed into tutors already known to promote learning, using principles in PSLC theoretical framework? In the current project (which started in Year 5 of the PSLC), we are investigating the effect of adding game elements to an existing set of fractions tutors developed by Martina Rau (Rau, Aleven, & Rummel, in press) for a different PSLC project. The game elements comprise a fantasy soccer game, where success in the soccer game depends on learning progress in the factions tutors.<br />
<br />
=== Background & Significance ===<br />
Games and tutors appear to have complementary strengths. The current project is an attempt to have the best of both worlds.<br />
<br />
How best to integrate learning content and game elements has been the subject of much theorizing, with some authors arguing that optimal learning requires that the learning content and game world be mutually dependent (Lepper & Malone, 1987), and others arguing that the learning should be embedded in the game’s core mechanic (Habgood, 2007). However, such theories ignore the real-world success (at least in anecdotal reports from teachers and students) of environments that feature a much looser integration between learning and motivational embellishments (e.g., FirstInMath). Given that a loose coupling between game elements and learning activities is far easier to implement (since it avoids the difficult problem of embedding math problems in a storyline or game context, hard to do especially if the learning content is to be adapted to individual students’ learning results), it stands to reason to investigate this option first. It may well be that as long as the game features are “cool,” the degree of integration is not really a strong factor. As mentioned, the success of for example the motivational embellishments in FirstInMath certainly suggest so.<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
=== Independent Variables ===<br />
We are investigating the effect of loosely coupling the fractions tutors to a parallel fantasy world. This fantasy world was designed in a participatory design process involving 6th graders (the target population). The main theme (soccer) was chosen because it will appeal to the taste of both girls and boys.<br />
<br />
In the fantasy world, a soccer game takes place on a tropical beach between a motley crew of friendly animal characters and a group of invading pirates. This unfriendly bunch is intent on taking over the beach, a disaster that can be averted only by beating the pirates at the soccer game. The student regularly switches between the tutors and the fantasy world (in a manner controlled largely by the system). During each visit to the fantasy world, the student selects a game move (e.g., a deep pass, or risky dribble). The outcome of that move will not be known until the student’s next visit to the fantasy world, and the probability of success of that move depends on the student’s performance on the intervening fractions problems. (It may be clear that this coupling between game elements and learning is loose: different learning content could be slotted in without changing the game.) <br />
<br />
=== Dependent Variables ===<br />
We will evaluate whether this game leads to increased learning, compared to a standard tutor without the game elements, and whether any improvement in learning we may observe is mediated by increased motivation and more positive affect. Affect will be assessed by means of field observations, motivation (interest, self-efficacy) by means of questionnaires. We will evaluate both whether the game leads to greater persistence (when student have the option to do something else), and whether it leads to greater learning even when the set of learning activities (but not time) is held constant (which if true would mean that the game elements lead to deeper processing during the same set of learning activities).<br />
<br />
=== Planned Experiments ===<br />
We will conduct an in-vivo experiment comparing the tutor with game features against the regular tutor.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Math_Game_Elements&diff=9348Math Game Elements2009-05-15T01:21:26Z<p>Aleven: </p>
<hr />
<div>== Improving student affect through adding game elements to mathematics LearnLabs ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Ryan Baker, Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || Adriana de Carvalho (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || Fall, 2009<br />
|-<br />
| '''Study End Date''' || <br />
|-<br />
| '''LearnLab Site''' || <br />
|-<br />
| '''LearnLab Course''' || Middle-School Mathematics<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || <br />
|}<br />
=== Abstract ===<br />
There is much evidence to believe that games are fun. Can we incorporate some of the features that make games fun into intelligent tutors, in a way that improves motivation, generates positive affect, and improves the robustness of student learning? Specifically, what happens if we take game elements known to be effective such as fantasy, competition, and trivial choice, and embed into tutors already known to promote learning, using principles in PSLC theoretical framework? In the current project (which started in Year 5 of the PSLC), we are investigating the effect of adding game elements to an existing set of fractions tutors developed by Martina Rau (Rau, Aleven, & Rummel, in press) for a different PSLC project. The game elements comprise a fantasy soccer game, where success in the soccer game depends on learning progress in the factions tutors.<br />
<br />
=== Background & Significance ===<br />
Games and tutors appear to have complementary strengths. The current project is an attempt to have the best of both worlds.<br />
<br />
How best to integrate learning content and game elements has been the subject of much theorizing, with some authors arguing that optimal learning requires that the learning content and game world be mutually dependent (Lepper & Malone, 1987), and others arguing that the learning should be embedded in the game’s core mechanic (Habgood, 2007). However, such theories ignore the real-world success (at least in anecdotal reports from teachers and students) of environments that feature a much looser integration between learning and motivational embellishments (e.g., FirstInMath). Given that a loose coupling between game elements and learning activities is far easier to implement (since it avoids the difficult problem of embedding math problems in a storyline or game context, hard to do especially if the learning content is to be adapted to individual students’ learning results), it stands to reason to investigate this option first. It may well be that as long as the game features are “cool,” the degree of integration is not really a strong factor. As mentioned, the success of for example the motivational embellishments in FirstInMath certainly suggest so.<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
=== Independent Variables ===<br />
We are investigating the effect of loosely coupling the fractions tutors to a parallel fantasy world. This fantasy world was designed in a participatory design process involving 6th graders (the target population). The main theme (soccer) was chosen because it will appeal to the taste of both girls and boys.<br />
<br />
In the fantasy world, a soccer game takes place on a tropical beach between a motley crew of friendly animal characters and a group of invading pirates. This unfriendly bunch is intent on taking over the beach, a disaster that can be averted only by beating the pirates at the soccer game. The student regularly switches between the tutors and the fantasy world (in a manner controlled largely by the system). During each visit to the fantasy world, the student selects a game move (e.g., a deep pass, or risky dribble). The outcome of that move will not be known until the student’s next visit to the fantasy world, and the probability of success of that move depends on the student’s performance on the intervening fractions problems. (It may be clear that this coupling between game elements and learning is loose: different learning content could be slotted in without changing the game.) <br />
<br />
=== Dependent Variables ===<br />
We will evaluate whether this game leads to increased learning, compared to a standard tutor without the game elements, and whether any improvement in learning we may observe is mediated by increased motivation and more positive affect. Affect will be assessed by means of field observations, motivation (interest, self-efficacy) by means of questionnaires. We will evaluate both whether the game leads to greater persistence (when student have the option to do something else), and whether it leads to greater learning even when the set of learning activities (but not time) is held constant (which if true would mean that the game elements lead to deeper processing during the same set of learning activities).<br />
<br />
=== Planned Experiments ===<br />
We will conduct an in-vivo experiment comparing the tutor with game features against the regular tutor.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9347Geometry Greatest Hits2009-05-15T00:36:29Z<p>Aleven: /* Independent variables */</p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
* [[Visual-verbal integration]] principle<br />
* [[Worked example principle]]<br />
* [[Prompted self-explanation principle]]<br />
* Accurate knowledge decomposition principle (part of the complete and efficient practice principle)<br />
* Accurate knowledge estimates principle (part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
PSLC’s in vivo methodology generally focuses on testing one principle at a time.<br />
This approach is useful for understanding which principles work, how they work, and what their boundary conditions are.<br />
Testing combinations of principles is useful also, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic.<br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
=== Completed experiments===<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
<br />
=== Planned experiments===<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Independent variables ===<br />
<br />
The Greatest Hits version of the tutor had the following features, which are supported by prior PSLC research<br />
<br />
* integrated problem format (symbolic information integrated in the diagram; all interaction happens in the diagram)<br />
* non-interactive conceptual example sets at the beginning of each curricular unit<br />
* interactive worked examples at the beginning of each curricular, faded in an individualized manner<br />
* diagrammatic self-explanations of incorrect steps<br />
* tuned knowledge-tracing parameters to achieve more better individualized problem sequences (avoiding over-practice and under-practice)<br />
* employed new knowledge-tracing algorithm that estimated the probability of guesses and slips in a contextual manner (to improve the accuracy of student modeling, which in turn better individualized problem sequences)<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9346Geometry Greatest Hits2009-05-15T00:28:58Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
* [[Visual-verbal integration]] principle<br />
* [[Worked example principle]]<br />
* [[Prompted self-explanation principle]]<br />
* Accurate knowledge decomposition principle (part of the complete and efficient practice principle)<br />
* Accurate knowledge estimates principle (part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
PSLC’s in vivo methodology generally focuses on testing one principle at a time.<br />
This approach is useful for understanding which principles work, how they work, and what their boundary conditions are.<br />
Testing combinations of principles is useful also, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic.<br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
=== Completed experiments===<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
<br />
=== Planned experiments===<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Independent variables ===<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9345Geometry Greatest Hits2009-05-15T00:26:41Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
* [[Visual-verbal integration]] principle<br />
* [[Worked example principle]]<br />
* [[Prompted self-explanation principle]]<br />
* Accurate knowledge decomposition principle (part of the complete and efficient practice principle)<br />
* Accurate knowledge estimates principle (part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
PSLC’s in vivo methodology generally focuses on testing one principle at a time.<br />
This approach is useful for understanding which principles work, how they work, and what their boundary conditions are.<br />
Testing combinations of principles is useful also, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic.<br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
===Planned experiments===<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9344Geometry Greatest Hits2009-05-15T00:23:40Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
* [[Visual-verbal integration]] principle<br />
* [[Worked example principle]]<br />
* [[Prompted self-explanation principle]]<br />
* Accurate knowledge decomposition principle (part of the complete and efficient practice principle)<br />
* Accurate knowledge estimates principle (part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
PSLC’s in vivo methodology generally focuses on testing one principle at a time.<br />
This approach is useful for understanding which principles work, how they work, and what their boundary conditions are.<br />
Testing combinations of principles is useful also, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic.<br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
===Planned experiments===<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9343Geometry Greatest Hits2009-05-15T00:21:33Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
* Visual-verbal integration principle<br />
* [[Worked example principle]]<br />
* Prompted self-explanation principle<br />
* Accurate knowledge decomposition principle (part of the complete and efficient practice principle)<br />
* Accurate knowledge estimates principle (part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
PSLC’s in vivo methodology generally focuses on testing one principle at a time.<br />
This approach is useful for understanding which principles work, how they work, and what their boundary conditions are.<br />
Testing combinations of principles is useful also, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic.<br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
===Planned experiments===<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9342Geometry Greatest Hits2009-05-15T00:20:07Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
* Visual-verbal integration principle<br />
* Worked example principle<br />
* Prompted self-explanation principle<br />
* Accurate knowledge decomposition principle<br />
(part of the complete and efficient practice principle)<br />
* Accurate knowledge estimates principle<br />
(part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
PSLC’s in vivo methodology generally focuses on testing one principle at a time.<br />
This approach is useful for understanding which principles work, how they work, and what their boundary conditions are.<br />
Testing combinations of principles is useful also, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic.<br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
===Planned experiments===<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9341Geometry Greatest Hits2009-05-15T00:18:06Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
• Visual-verbal integration principle<br />
• Worked example principle<br />
• Prompted self-explanation principle<br />
• Accurate knowledge decomposition principle<br />
(part of the complete and efficient practice principle)<br />
• Accurate knowledge estimates principle<br />
(part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
PSLC’s in vivo methodology generally focuses on testing one principle at a time.<br />
This approach is useful for understanding which principles work, how they work, and what their boundary conditions are.<br />
Testing combinations of principles is useful also, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic.<br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
===Planned experiments===<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9340Geometry Greatest Hits2009-05-15T00:17:49Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
• Visual-verbal integration principle<br />
• Worked example principle<br />
• Prompted self-explanation principle<br />
• Accurate knowledge decomposition principle<br />
(part of the complete and efficient practice principle)<br />
• Accurate knowledge estimates principle<br />
(part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
PSLC’s in vivo methodology generally focuses on testing one principle at a time.<br />
This approach is useful for understanding which principles work, how they work, and what their boundary conditions are.<br />
Testing combinations of principles is useful also, because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic.<br />
<br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical goal within the learning sciences. Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
===Planned experiments===<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9339Geometry Greatest Hits2009-05-15T00:15:57Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
• Visual-verbal integration principle<br />
• Worked example principle<br />
• Prompted self-explanation principle<br />
• Accurate knowledge decomposition principle<br />
(part of the complete and efficient practice principle)<br />
• Accurate knowledge estimates principle<br />
(part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
PSLC’s in vivo methodology generally focuses on testing one principle at a time.<br />
Useful for understanding how that principle works and what its boundary conditions are.<br />
Testing combinations of principles is useful because it elucidates boundary conditions and explores the degree to which principles are complementary or synergistic.<br />
<br />
Intelligent Tutoring Systems have been proven to be more effective than typical classroom instruction.<br />
Can principle-oriented research make them even more effective?<br />
Can demonstrable impact in the classroom be strengthened by combining principles from successful in vivo studies?<br />
And will such a combination lead to a large effect size?<br />
<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is also an important practical and theoretical goal within the learning sciences. This project will contribute new knowledge to our understanding of the relationship between instructional principles for robust learning. From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
===Planned experiments===<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9338Geometry Greatest Hits2009-05-15T00:13:38Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
• Visual-verbal integration principle<br />
• Worked example principle<br />
• Prompted self-explanation principle<br />
• Accurate knowledge decomposition principle<br />
(part of the complete and efficient practice principle)<br />
• Accurate knowledge estimates principle<br />
(part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is an important practical and theoretical goal within the learning sciences. This project will contribute new knowledge to our understanding of the relationship between instructional principles for robust learning. From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
===Planned experiments===<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9337Geometry Greatest Hits2009-05-15T00:11:02Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
• Visual-verbal integration principle<br />
• Worked example principle<br />
• Prompted self-explanation principle<br />
• Accurate knowledge decomposition principle<br />
(part of the complete and efficient practice principle)<br />
• Accurate knowledge estimates principle<br />
(part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is an important practical and theoretical goal within the learning sciences. This project will contribute new knowledge to our understanding of the relationship between instructional principles for robust learning. From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
===Planned experiments===<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9336Geometry Greatest Hits2009-05-15T00:10:16Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
• Visual-verbal integration principle<br />
• Worked example principle<br />
• Prompted self-explanation principle<br />
• Accurate knowledge decomposition principle<br />
(part of the complete and efficient practice principle)<br />
• Accurate knowledge estimates principle<br />
(part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is an important practical and theoretical goal within the learning sciences. This project will contribute new knowledge to our understanding of the relationship between instructional principles for robust learning. From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
===Planned experiments===<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
<br />
=== Results ===<br />
Not yet available.<br />
<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9335Geometry Greatest Hits2009-05-15T00:09:53Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
• Visual-verbal integration principle<br />
• Worked example principle<br />
• Prompted self-explanation principle<br />
• Accurate knowledge decomposition principle<br />
(part of the complete and efficient practice principle)<br />
• Accurate knowledge estimates principle<br />
(part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is an important practical and theoretical goal within the learning sciences. This project will contribute new knowledge to our understanding of the relationship between instructional principles for robust learning. From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
=== Glossary ===<br />
=== Hypotheses ===<br />
<br />
===Planned experiments===<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
<br />
=== Results ===<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9334Geometry Greatest Hits2009-05-15T00:09:09Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
• Visual-verbal integration principle<br />
• Worked example principle<br />
• Prompted self-explanation principle<br />
• Accurate knowledge decomposition principle<br />
(part of the complete and efficient practice principle)<br />
• Accurate knowledge estimates principle<br />
(part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is an important practical and theoretical goal within the learning sciences. This project will contribute new knowledge to our understanding of the relationship between instructional principles for robust learning. From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. <br />
<br />
=== Glossary ===<br />
=== Research questions ===<br />
<br />
===Planned experiments===<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
<br />
=== Hypotheses ===<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9333Geometry Greatest Hits2009-05-15T00:08:09Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
• Visual-verbal integration principle<br />
• Worked example principle<br />
• Prompted self-explanation principle<br />
• Accurate knowledge decomposition principle<br />
(part of the complete and efficient practice principle)<br />
• Accurate knowledge estimates principle<br />
(part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we have created a new version of the Geometry Cognitive Tutor that implements these five principles. We have conducted an in-vivo experiment, and will conduct a lab experiment, to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor, which does not support any of these principles, or supports them less strongly.<br />
<br />
=== Background & Significance ===<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is an important practical and theoretical goal within the learning sciences. This project will contribute new knowledge to our understanding of the relationship between instructional principles for robust learning. From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. Further, the project, if successful, will demonstrate that the studied combination of principles leads to dramatically greater effectiveness of one particular intelligent tutoring system. Since these principles are drawn from PSLC theory and research evidence, the successful combination of principles has important implications for the development of PSLC theory, and inference about its eventual impact on learning outcomes. From a theoretical perspective, individual instructional design principles are a convenient way of stating theory; findings related to synergy (or lack thereof) of individual principles impose constraints on the theoretical rationale of each. Thus, this project contributes to both the applied and the theoretical missions of the PSLC.<br />
<br />
=== Glossary ===<br />
=== Research questions ===<br />
<br />
===Planned experiments===<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
<br />
=== Hypotheses ===<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9332Geometry Greatest Hits2009-05-15T00:06:30Z<p>Aleven: /* Abstract */</p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
The main idea in the current project is to combine instructional interventions derived from five instructional principles. Each of these interventions has been shown to be effective in separate (PSLC) studies, and can be expected on theoretical grounds to be synergistic (or complementary). We hypothesize that instruction that simultaneously implements several principles will be dramatically more effective than instruction that does not implement any of the targeted principles (e.g. current common practice), especially if the principles are tied to different learning mechanisms. This project will test this hypothesis, focusing on the following five principles:<br />
<br />
• Visual-verbal integration principle<br />
• Worked example principle<br />
• Prompted self-explanation principle<br />
• Accurate knowledge decomposition principle<br />
(part of the complete and efficient practice principle)<br />
• Accurate knowledge estimates principle<br />
(part of the complete and efficient practice principle) <br />
<br />
Building on our prior work that tested these principles individually, we will create a new version of the Geometry Cognitive Tutor that implements these five principles. We will use this new version to conduct both a lab experiment and an in vivo experiment to test the hypothesis that the combination of these principles produces a large effect size compared to the standard Cognitive Tutor.<br />
Knowing which instructional interventions and principles are synergistic (as well as when interventions and principles do not have any additive effects) is an important practical and theoretical goal within the learning sciences. This project will contribute new knowledge to our understanding of the relationship between instructional principles for robust learning. From a practical perspective, instructional designers often use principles in combination (e.g. Anderson et al, 1995; Quintana et al, 2004); knowing which combinations are effective in concert is therefore pragmatically useful. Further, the project, if successful, will demonstrate that the studied combination of principles leads to dramatically greater effectiveness of one particular intelligent tutoring system. Since these principles are drawn from PSLC theory and research evidence, the successful combination of principles has important implications for the development of PSLC theory, and inference about its eventual impact on learning outcomes. From a theoretical perspective, individual instructional design principles are a convenient way of stating theory; findings related to synergy (or lack thereof) of individual principles impose constraints on the theoretical rationale of each. Thus, this project contributes to both the applied and the theoretical missions of the PSLC.<br />
<br />
=== Background & Significance ===<br />
<br />
=== Glossary ===<br />
=== Research questions ===<br />
<br />
===Planned experiments===<br />
* Lab study (2 phases): <br />
**(1) A two-condition study (comparing the baseline tutor to the modified tutor with all five improvements) testing overall student learning (including measures of robust learning) and efficiency in one tutor unit (Angles). <br />
**(2) Think-aloud (lab) research to determine if worked-examples and visual interaction have the hypothesized, complementary process effects.<br />
* In vivo study: A two-condition in-vivo study (comparing the baseline tutor to the modified tutor with all four improvements). Measures of learning gains and learning efficiency (time taken to complete tutor) will be utilized.<br />
<br />
=== Hypotheses ===<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Math_Game_Elements&diff=9231Math Game Elements2009-05-14T02:29:51Z<p>Aleven: New page: == Improving student affect through adding game elements to mathematics LearnLabs == === Summary Table === ====Study 1==== {| border="1" cellspacing="0" cellpadding="5" style="text-align: ...</p>
<hr />
<div>== Improving student affect through adding game elements to mathematics LearnLabs ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Ryan Baker, Vincent Aleven<br />
|-<br />
| '''Other Contributers''' || Adriana de Carvalho (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || Fall, 2009<br />
|-<br />
| '''Study End Date''' || <br />
|-<br />
| '''LearnLab Site''' || <br />
|-<br />
| '''LearnLab Course''' || Middle-School Mathematics<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || <br />
|}<br />
=== Abstract ===<br />
=== Background & Significance ===<br />
=== Glossary ===<br />
=== Research questions ===<br />
=== Hypotheses ===<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Algebra&diff=9230Algebra2009-05-14T02:26:37Z<p>Aleven: </p>
<hr />
<div>===Algebra LearnLab course description===<br />
The Algebra LearnLab course is described [http://learnlab.org/learnlabs/algebra/ here].<br />
<br />
The Algebra LearnLab course involves teaching high school Algebra I using the textbook and tutoring system of Carnegie Learning. The curriculum combines software-based, individualized computer lessons with collaborative, real-world problem-solving activities. Students spend about 40% of their class time using the software, and the balance of their time engaged in classroom problem-solving activities. Three Pittsburgh-area high schools are currently participating as PSLC Algebra LearnLab sites, offering a diverse student population, and additional high schools around the country are anticipated for future participation. <br />
<br />
See a list of course topics below.<br />
<br />
===Algebra & Geometry LearnLab Course Committee===<br />
The Math Course Committee meets monthly and is led by Albert Corbett (corbett@andrew.cmu.edu).<br />
<br />
Teachers (and perhaps researchers) can find answers to questions on the [[FAQ for teachers]].<br />
<br />
===Algebra Learnlab Studies===<br />
<br />
*[[Booth | Improving skill at solving equations through better encoding of algebraic concepts (Booth, Siegler, Koedinger & Rittle-Johnson)]]<br />
*[[Handwriting Algebra Tutor]] (Anthony, Yang & Koedinger)<br />
**[[Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving]] (completed)<br />
**[[Effect of adding simple worked examples to problem-solving in algebra learning]] (completed, analysis in progress)<br />
**[[In vivo comparison of Cognitive Tutor Algebra using handwriting vs typing input]] (in progress)<br />
*[[Rummel_Scripted_Collaborative_Problem_Solving|Collaborative Extensions to the Cognitive Tutor Algebra: Scripted Collaborative Problem Solving (Rummel, Diziol, McLaren, & Spada)]]<br />
*[[Walker_A_Peer_Tutoring_Addition|Collaborative Extensions to the Cognitive Tutor Algebra: A Peer Tutoring Addition (Walker, McLaren, Koedinger, & Rummel)]]<br />
*[[DiBiano_Personally_Relevant_Algebra_Problems|Robust Learning in Culturally and Personally Relevant Algebra Problem Scenarios (DiBiano, Petrosino, Greeno, & Sherman)]]<br />
*[[Math_Game_Elements|Improving student affect through adding game elements to mathematics LearnLabs (Baker, Aleven)]]<br />
<br />
These studies are also organized within research clusters that address common issues across a variety of academic content domains: [[Coordinative Learning]], [[Interactive Communication]], and [[Refinement and Fluency]].<br />
<br />
===Algebra I topics===<br />
*Organizing Single Variable Data <br />
*Simplifying Linear Expressions <br />
*Finding Linear Equations from Graphs <br />
*Solving Linear Equations and Inequalities <br />
*Standard Form <br />
*Slope Intercept Form <br />
*Mathematical Modeling <br />
*Linear Expressions and Equations <br />
*Quadratic Expressions and Equations <br />
*Solving Systems of Linear Equations Algebraically and Graphically <br />
*Solving and Graphing Equations Involving Absolute Values <br />
*Problem Solving using Proportional Reasoning <br />
*Analyzing Data and Making Predictions <br />
*Powers and Exponents</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9229Geometry Greatest Hits2009-05-14T02:18:36Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2009<br />
|-<br />
| '''Study End Date''' || March, 2009<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
=== Background & Significance ===<br />
=== Glossary ===<br />
=== Research questions ===<br />
=== Hypotheses ===<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9228Geometry Greatest Hits2009-05-14T02:18:12Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== Summary Table ===<br />
====Study 1====<br />
{| border="1" cellspacing="0" cellpadding="5" style="text-align: left;"<br />
| '''PIs''' || Vincent Aleven, Ryan Baker, Kirsten Butcher, & Ron Salden<br />
|-<br />
| '''Other Contributers''' || <b>Research Programmers/Associates:</b> Octav Popescu (Research Programmer, CMU HCII), Jessica Kalka (Research Associate, CMU HCII)<br />
<br />
|-<br />
| '''Study Start Date''' || January, 2008<br />
|-<br />
| '''Study End Date''' || March, 2008<br />
|-<br />
| '''LearnLab Site''' || Greenville, Riverview, Steel Valley<br />
|-<br />
| '''LearnLab Course''' || Geometry<br />
|-<br />
| '''Number of Students''' || <br />
|-<br />
| '''Total Participant Hours''' || <br />
|-<br />
| '''DataShop''' || Log data soon to be uploaded and available in the DataShop<br />
|}<br />
=== Abstract ===<br />
=== Background & Significance ===<br />
=== Glossary ===<br />
=== Research questions ===<br />
=== Hypotheses ===<br />
=== Explanation ===<br />
=== Further Information ===<br />
==== Connections ====<br />
==== Annotated Bibliography ====<br />
==== References ====<br />
==== Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9225Geometry Greatest Hits2009-05-14T02:14:03Z<p>Aleven: </p>
<hr />
<div>== Geometry Greatest Hits ==<br />
=== 1.1 Summary Table ===<br />
+ 1.1.1 Study 1<br />
+ 1.1.2 Study 2<br />
=== 1.2 Abstract ===<br />
=== 1.3 Background & Significance ===<br />
=== 1.4 Glossary ===<br />
=== 1.5 Research questions ===<br />
=== 1.6 Hypotheses ===<br />
=== 1.7 Explanation ===<br />
=== 1.8 Further Information ===<br />
==== 1.8.1 Connections ====<br />
==== 1.8.2 Annotated Bibliography ====<br />
==== 1.8.3 References ====<br />
==== 1.8.4 Future Plans ====</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry_Greatest_Hits&diff=9224Geometry Greatest Hits2009-05-14T02:09:17Z<p>Aleven: New page: * 1 Geometry Greatest Hits o 1.1 Summary Table + 1.1.1 Study 1 + 1.1.2 Study 2 o 1.2 Abstract o 1.3 Background & Significa...</p>
<hr />
<div> * 1 Geometry Greatest Hits<br />
o 1.1 Summary Table<br />
+ 1.1.1 Study 1<br />
+ 1.1.2 Study 2<br />
o 1.2 Abstract<br />
o 1.3 Background & Significance<br />
o 1.4 Glossary<br />
o 1.5 Research questions<br />
o 1.6 Hypotheses<br />
o 1.7 Explanation<br />
o 1.8 Further Information<br />
+ 1.8.1 Connections<br />
+ 1.8.2 Annotated Bibliography<br />
+ 1.8.3 References<br />
+ 1.8.4 Future Plans</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Geometry&diff=9222Geometry2009-05-14T02:07:10Z<p>Aleven: </p>
<hr />
<div>===Geometry LearnLab course description===<br />
The Geometry LearnLab course is described [http://learnlab.org/learnlabs/geometry/ here].<br />
<br />
===Algebra & Geometry LearnLab Course Committee===<br />
The Math Course Committee meets monthly and is led by Albert Corbett (corbett@andrew.cmu.edu).<br />
<br />
Teachers (and perhaps researchers) can find answers to questions on the [[FAQ for teachers]].<br />
<br />
===Geometry Learnlab Studies===<br />
<br />
*[[Contiguous Representations for Robust Learning (Aleven & Butcher)]]<br />
*[[Mapping Visual and Verbal Information: Integrated Hints in Geometry (Aleven & Butcher)]]<br />
**[[Training Geometry Concepts with Visual and Verbal Sources (Burchfield, Aleven, & Butcher)]]<br />
*[[Visual Feature Focus in Geometry: Instructional Support for Visual Coordination During Learning (Butcher & Aleven)]]<br />
*[[Using Elaborated Explanations to Support Geometry Learning (Aleven & Butcher)]]<br />
*[[Help_Lite (Aleven, Roll)|Hints during tutored problem solving – the effect of fewer hint levels with greater conceptual content (Aleven & Roll)]]<br />
*[[Does learning from worked-out examples improve tutored problem solving? | Does learning from worked-out examples improve tutored problem solving? (Renkl, Aleven & Salden)]]<br />
* [[The_Help_Tutor__Roll_Aleven_McLaren|Tutoring a meta-cognitive skill: Help-seeking (Roll, Aleven & McLaren)]]<br />
* [[Composition_Effect__Kao_Roll|What is difficult about composite problems? (Kao, Roll)]]<br />
* [[Using learning curves to optimize problem assignment]] (Cen & Koedinger)<br />
* [[Geometry Greatest Hits]] (Aleven, Baker, Butcher, & Salden)<br />
<br />
These studies are also organized within research clusters that address common issues across a variety of academic content domains: [[Coordinative Learning]], [[Interactive Communication]], and [[Refinement and Fluency]].</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Self_Efficacy&diff=8591Self Efficacy2008-11-28T23:45:14Z<p>Aleven: </p>
<hr />
<div>Note: this page is currently under construction. Please do not cite.<br />
<br />
Self-efficacy is a person's perception of their own capability to attain certain goals or complete a certain task (Schunk, Pintrich, & Meece, 2008; p. 8). Self-efficacy is an important construct in multiple theories of motivation (Bandura's social-cognitive theory of motivation, Eccles and Wigfield's expectancy-value theories, Weiner's attribution theory (Weiner). [REF Dweck?] It also features prominently in theories of self-regulated learning [REFs].<br />
<br />
The notion of self-efficacy encompasses both students' beliefs related to fairly specific tasks or abilities (e.g., solving linear equations, or even, solving the next learning task as well as students' beliefs related to broader competencies (e.g., their ability to do or learn algebra). Some studies have investigated how specific self-efficacy beliefs tend to be [REFS]. [VA: what did these studies find?]<br />
<br />
The notion of self-efficacy is related to but different from constructs such as feeling of knowing (FOK), self-concept, self-competence, and self-esteem. <br />
Although self-efficacy is an inherently subjective notion, in many academic learning situations people's subjective self-efficacy beliefs may be heavily influenced by more objective measurements of their competence, for example their results on tests and quizzes.<br />
<br />
'''Measurement:''' self-efficacy is typically assessed by means of questionnaires (MSLQ?) and is often related to a specific academic task [NEED EXAMPLE QUESTIONNAIRE ITEMS). Teacher assessment is sometimes used. Recently, researchers have started to devise ways to automatically measure self-efficacy within computer-based learning environments. For example, Lester, McQuiggan (sp?), Boyer at NC State [REFS] have started to create machine-learned detectors that (unobtrusively and automatically, in real time) detect behaviors that reflect high/low self-efficacy within a serious game. It is still early days for this kind of work; for example, further validation efforts are needed. Nonetheless, these kinds of detectors are promising tools for researchers and may also be used to create learning environments that adapt to students' level of self-efficacy (e.g., by varying the challenge level).<br />
<br />
'''Sources of self-efficacy beliefs''' Under attribution theory, students' self-efficacy is influenced heavily by the causes to which they attribute their successes and failures in learning tasks. [REFs] A key way in which learners may increase their self-efficacy is by having successful learning experiences and attributing them to their ability in the domain being studied. Attributing unsuccessful learning experiences to lack of ability, on the other hand, is likely lead to diminished self-efficacy. However, unsuccessful learning experiences do not inevitably lead to diminished self-efficacy, for example when they are attributed to lack of effort rather than lack of ability. [VA: NEED TO CHECK ON THIS STORY] Under expectancy-value theory, students' self-efficacy beliefs are grounded in their prior learning experiences. [VA: Duh. Need something more specific.] Within certain models of self-regulated learning (e.g., Zimmerman, 2008), students' satisfaction with their performance in certain learning tasks is seen as a major source of self-efficacy beliefs. These feelings are in focus especially in a self-reflection phase that follows - in this particular cyclical model - phases of forethought and performance. [VA: after feedback? or self-evaluation?] <br />
<br />
'''Effects of self-efficacy beliefs''' Under the expectancy-value theory, self-efficacy is one factor that learners take into account when assessing the probability of success prior to executing a certain learning task; under this theory, the amount of effort they will expend depends on how likely they think it is that they will be successful and on how much they value the expected outcome. Thus, all else being equal, learners with greater self-efficacy will expend greater effort. Under Zimmerman's (2008) model of self-regulated learning, students' self-efficacy beliefs are a factor influencing their planning process (e.g., goals they set for themselves).<br />
<br />
Many studies find that students' self-efficacy beliefs are highly correlated with academic achievement [REFs], as well as with measures of self-regulated learning [REFs].<br />
<br />
It is interesting to ask what these correlations may mean. For example, the correlations observed between self-efficacy and academic achievement may mean simply that students are accurate in their beliefs about their own abilities. This interpretation does not assign a causal role to self-efficacy beliefs in achieving better academic outcomes. On the other hand, these correlations could reflect the causal role assigned by expectancy-value theories, as we have seen: self-efficacious students will be more optimistic about their chances of success and therefore will show greater persistence. The success of interventions aimed at helping students make more adaptive attributions of their learning outcomes [REFs] provide some measure of support for such an interpretation. [VA: really? check on this. Also, wouldn't a correlation between self-efficacy and time on task be more direct evidence? Are there papers that document such as relation?] Similarly, one could ask whether the correlations between self-efficacy and use of self-regulation reflect a causal influence of self-efficacy (on greater self-regulation). This correlation could simply reflect the fact that learners who use the strategies have more positive self-efficacy beliefs (possibly, because these strategies led to positive learning results in the past, which in turn led to greater self-efficacy - consistent with Zimmerman's (2008) cyclical model of self-regulated learning). This explanation does not assign a causal role to self-efficacy in self-regulation. On the other hand, this correlation could reflect an indirect causal influence of self-efficacy, for example if greater self-efficacy made the ''use'' of certain self-regulatory strategies more likely (i.e., if students, perhaps enthused by a greater expectancy of success, were more likely to apply strategies - in this explanation, self-efficacy has an enabling role). [VA: perhaps the path analysis in the Schunk & Ertmer studies points in this direction?]<br />
<br />
What is needed is a way to "manipulate" students' self-efficacy beliefs that does not affect other aspects of their learning. [VA: any studies that do this? attribution retraining comes to mind, or some kind of feedback; perhaps that old Dweck study?]<br />
<br />
'''Interventions that enhance self-efficacy''' A number of studies have demonstrated that certain interventions enhance self-efficacy and learning (Schunk & Ertmer, 2000). [VA: need specifics] [Check papers referenced in Zimmerman, 2008 Am Ed Res J.] [VA: need more examples here]<br />
<br />
'''Accuracy of self-efficacy beliefs.''' (Need to cite some research about how accurate people's self-efficacy beliefs tend to be.) It is generally believed among motivation researchers that underestimating one's capabilities tends to be worse - from a viewpoint of future academic achievement (??? check!) - than overestimating one's capabilities.<br />
<br />
<br />
<br />
'''References'''<br />
<br />
Schunk D., & Ertmer P. (2000) Self-regulation and academic learning: Self-efficacy enhancing interventions., In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego: Academic Press. <br />
<br />
Schunk, D. H., Pintrich P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.<br />
<br />
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166-183. <br />
<br />
<br />
<br />
'''Links'''<br />
<br />
http://www.des.emory.edu/mfp/efftalk.html<br />
<br />
http://www.des.emory.edu/mfp/effchapter.html<br />
<br />
http://en.wikipedia.org/wiki/Self-efficacy</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Self_Efficacy&diff=8590Self Efficacy2008-11-28T23:40:57Z<p>Aleven: </p>
<hr />
<div>Note: this page is currently under construction. Please do not cite.<br />
<br />
Self-efficacy is a person's perception of their own capability to attain certain goals or complete a certain task (Schunk, Pintrich, & Meece, 2008; p. 8). Self-efficacy is an important construct in multiple theories of motivation (Bandura's social-cognitive theory of motivation, Eccles and Wigfield's expectancy-value theories, Weiner's attribution theory (Weiner). [REF Dweck?] It also features prominently in theories of self-regulated learning [REFs].<br />
<br />
The notion of self-efficacy encompasses both students' beliefs related to fairly specific tasks or abilities (e.g., solving linear equations, or even, solving the next learning task as well as students' beliefs related to broader competencies (e.g., their ability to do or learn algebra). Some studies have investigated how specific self-efficacy beliefs tend to be [REFS]. [VA: what did these studies find?]<br />
<br />
The notion of self-efficacy is related to but different from constructs such as feeling of knowing (FOK), self-concept, self-competence, and self-esteem. <br />
Although self-efficacy is an inherently subjective notion, in many academic learning situations people's subjective self-efficacy beliefs may be heavily influenced by more objective measurements of their competence, for example their results on tests and quizzes.<br />
<br />
'''Measurement:''' self-efficacy is typically assessed by means of questionnaires (MSLQ?) and is often related to a specific academic task [NEED EXAMPLE QUESTIONNAIRE ITEMS). Teacher assessment is sometimes used. Recently, researchers have started to devise ways to automatically measure self-efficacy within computer-based learning environments. For example, Lester, McQuiggan (sp?), Boyer at NC State [REFS] have started to create machine-learned detectors that (unobtrusively and automatically, in real time) detect behaviors that reflect high/low self-efficacy within a serious game. It is still early days for this kind of work; for example, further validation efforts are needed. Nonetheless, these kinds of detectors are promising tools for researchers and may also be used to create learning environments that adapt to students' level of self-efficacy (e.g., by varying the challenge level).<br />
<br />
'''Sources of self-efficacy beliefs''' Under attribution theory, students' self-efficacy is influenced heavily by the causes to which they attribute their successes and failures in learning tasks. [REFs] A key way in which learners may increase their self-efficacy is by having successful learning experiences and attributing them to their ability in the domain being studied. Attributing unsuccessful learning experiences to lack of ability, on the other hand, is likely lead to diminished self-efficacy. However, unsuccessful learning experiences do not inevitably lead to diminished self-efficacy, for example when they are attributed to lack of effort rather than lack of ability. [VA: NEED TO CHECK ON THIS STORY] Under expectancy-value theory, students' self-efficacy beliefs are grounded in their prior learning experiences. [VA: Duh. Need something more specific.] Within certain models of self-regulated learning (e.g., Zimmerman, 2008), students' satisfaction with their performance in certain learning tasks is seen as a major source of self-efficacy beliefs. These feelings are in focus especially in a self-reflection phase that follows - in this particular cyclical model - phases of forethought and performance. [VA: after feedback? or self-evaluation?] <br />
<br />
'''Effects of self-efficacy beliefs''' Under the expectancy-value theory, self-efficacy is one factor that learners take into account when assessing the probability of success prior to executing a certain learning task; under this theory, the amount of effort they will expend depends on how likely they think it is that they will be successful and on how much they value the expected outcome. Thus, all else being equal, learners with greater self-efficacy will expend greater effort. Under Zimmerman's (2008) model of self-regulated learning, students' self-efficacy beliefs are a factor influencing their planning process (e.g., goals they set for themselves).<br />
<br />
Many studies find that students' self-efficacy beliefs are highly correlated with academic achievement [REFs], as well as with measures of self-regulated learning [REFs].<br />
<br />
It is interesting to ask what these correlations may mean. For example, the correlations observed between self-efficacy and academic achievement may mean simply that students are accurate in their beliefs about their own abilities. This interpretation does not assign a causal role to self-efficacy beliefs in achieving better academic outcomes. On the other hand, these correlations could reflect the causal role assigned by expectancy-value theories, as we have seen: self-efficacious students will be more optimistic about their chances of success and therefore will show greater persistence. The success of interventions aimed at helping students make more adaptive attributions of their learning outcomes [REFs] provide some measure of support for such an interpretation. [VA: really? check on this. Also, wouldn't a correlation between self-efficacy and time on task be more direct evidence? Are there papers that document such as relation?] Similarly, one could ask whether the correlations between self-efficacy and use of self-regulation reflect a causal influence of self-efficacy (on greater self-regulation). This correlation could simply reflect the fact that learners who use the strategies have more positive self-efficacy beliefs (possibly, because these strategies led to positive learning results in the past, which in turn led to greater self-efficacy - consistent with Zimmerman's (2008) cyclical model of self-regulated learning). This explanation does not assign a causal role to self-efficacy in self-regulation. On the other hand, this correlation could reflect an indirect causal influence of self-efficacy, for example if greater self-efficacy made the ''use'' of certain self-regulatory strategies more likely (i.e., if students, perhaps enthused by a greater expectancy of success, were more likely to apply strategies - in this explanation, self-efficacy has an enabling role).<br />
<br />
What is needed is a way to "manipulate" students' self-efficacy beliefs that does not affect other aspects of their learning. [VA: any studies that do this? attribution retraining comes to mind, or some kind of feedback; perhaps that old Dweck study?]<br />
<br />
'''Interventions that enhance self-efficacy''' A number of studies have demonstrated that certain interventions enhance self-efficacy and learning (Schunk & Ertmer, 2000). [VA: need specifics] [Check papers referenced in Zimmerman, 2008 Am Ed Res J.] [VA: need more examples here]<br />
<br />
'''Accuracy of self-efficacy beliefs.''' (Need to cite some research about how accurate people's self-efficacy beliefs tend to be.) It is generally believed among motivation researchers that underestimating one's capabilities tends to be worse - from a viewpoint of future academic achievement (??? check!) - than overestimating one's capabilities.<br />
<br />
<br />
<br />
'''References'''<br />
<br />
Schunk D., & Ertmer P. (2000) Self-regulation and academic learning: Self-efficacy enhancing interventions., In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego: Academic Press. <br />
<br />
Schunk, D. H., Pintrich P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.<br />
<br />
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166-183. <br />
<br />
<br />
<br />
'''Links'''<br />
<br />
http://www.des.emory.edu/mfp/efftalk.html<br />
<br />
http://www.des.emory.edu/mfp/effchapter.html<br />
<br />
http://en.wikipedia.org/wiki/Self-efficacy</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Self_Efficacy&diff=8589Self Efficacy2008-11-28T23:40:41Z<p>Aleven: </p>
<hr />
<div>Note: this page is currently under construction. Please do not cite.<br />
<br />
Self-efficacy is a person's perception of their own capability to attain certain goals or complete a certain task (Schunk, Pintrich, & Meece, 2008; p. 8). Self-efficacy is an important construct in multiple theories of motivation (Bandura's social-cognitive theory of motivation, Eccles and Wigfield's expectancy-value theories, Weiner's attribution theory (Weiner). [REF Dweck?] It also features prominently in theories of self-regulated learning [REFs].<br />
<br />
The notion of self-efficacy encompasses both students' beliefs related to fairly specific tasks or abilities (e.g., solving linear equations, or even, solving the next learning task as well as students' beliefs related to broader competencies (e.g., their ability to do or learn algebra). Some studies have investigated how specific self-efficacy beliefs tend to be [REFS]. [VA: what did these studies find?]<br />
<br />
The notion of self-efficacy is related to but different from constructs such as feeling of knowing (FOK), self-concept, self-competence, and self-esteem. <br />
Although self-efficacy is an inherently subjective notion, in many academic learning situations people's subjective self-efficacy beliefs may be heavily influenced by more objective measurements of their competence, for example their results on tests and quizzes.<br />
<br />
'''Measurement:''' self-efficacy is typically assessed by means of questionnaires (MSLQ?) and is often related to a specific academic task [NEED EXAMPLE QUESTIONNAIRE ITEMS). Teacher assessment is sometimes used. Recently, researchers have started to devise ways to automatically measure self-efficacy within computer-based learning environments. For example, Lester, McQuiggan (sp?), Boyer at NC State [REFS] have started to create machine-learned detectors that (unobtrusively and automatically, in real time) detect behaviors that reflect high/low self-efficacy within a serious game. It is still early days for this kind of work; for example, further validation efforts are needed. Nonetheless, these kinds of detectors are promising tools for researchers and may also be used to create learning environments that adapt to students' level of self-efficacy (e.g., by varying the challenge level).<br />
<br />
'''Sources of self-efficacy beliefs''' Under attribution theory, students' self-efficacy is influenced heavily by the causes to which they attribute their successes and failures in learning tasks. [REFs] A key way in which learners may increase their self-efficacy is by having successful learning experiences and attributing them to their ability in the domain being studied. Attributing unsuccessful learning experiences to lack of ability, on the other hand, is likely lead to diminished self-efficacy. However, unsuccessful learning experiences do not inevitably lead to diminished self-efficacy, for example when they are attributed to lack of effort rather than lack of ability. [VA: NEED TO CHECK ON THIS STORY] Under expectancy-value theory, students' self-efficacy beliefs are grounded in their prior learning experiences. [VA: Duh. Need something more specific.] Within certain models of self-regulated learning (e.g., Zimmerman, 2008), students' satisfaction with their performance in certain learning tasks is seen as a major source of self-efficacy beliefs. These feelings are in focus especially in a self-reflection phase that follows - in this particular cyclical model - phases of forethought and performance. [VA: after feedback? or self-evaluation?] <br />
<br />
'''Effects of self-efficacy beliefs''' Under the expectancy-value theory, self-efficacy is one factor that learners take into account when assessing the probability of success prior to executing a certain learning task; under this theory, the amount of effort they will expend depends on how likely they think it is that they will be successful and on how much they value the expected outcome. Thus, all else being equal, learners with greater self-efficacy will expend greater effort. Under Zimmerman's (2008) model of self-regulated learning, students' self-efficacy beliefs are a factor influencing their planning process (e.g., goals they set for themselves).<br />
<br />
Many studies find that students' self-efficacy beliefs are highly correlated with academic achievement [REFs], as well as with measures of self-regulated learning [REFs].<br />
<br />
It is interesting to ask what these correlations may mean. For example, the correlations observed between self-efficacy and academic achievement may mean simply that students are accurate in their beliefs about their own abilities. This interpretation does not assign a causal role to self-efficacy beliefs in achieving better academic outcomes. On the other hand, these correlations could reflect the causal role assigned by expectancy-value theories, as we have seen: self-efficacious students will be more optimistic about their chances of success and therefore will show greater persistence. The success of interventions aimed at helping students make more adaptive attributions of their learning outcomes [REFs] provide some measure of support for such an interpretation. [VA: really? check on this. Also, wouldn't a correlation between self-efficacy and time on task be more direct evidence? Are there papers that document such as relation?] Similarly, one could ask whether the correlations between self-efficacy and use of self-regulation reflect a causal influence of self-efficacy (on greater self-regulation). This correlation could simply reflect the fact that learners who use the strategies have more positive self-efficacy beliefs (possibly, because these strategies led to positive learning results in the past, which in turn led to greater self-efficacy - consistent with Zimmerman's (2008) cyclical model of self-regulated learning). This explanation does not assign a causal role to self-efficacy in self-regulation. On the other hand, this correlation could reflect an indirect causal influence of self-efficacy, for example if greater self-efficacy made the ''use'' of certain self-regulatory strategies more likely (i.e., if students, perhaps enthused by a greater expectancy of success, were more likely to apply strategies - in this explanation, self-efficacy has an enabling role).<br />
<br />
What is needed is a way to "manipulate" students' self-efficacy beliefs that does not affect other aspects of their learning. [VA: any studies that do this? attribution retraining comes to mind, or some kind of feedback; perhaps that old Dweck study?]<br />
<br />
<br />
'''Interventions that enhance self-efficacy''' A number of studies have demonstrated that certain interventions enhance self-efficacy and learning (Schunk & Ertmer, 2000). [VA: need specifics] [Check papers referenced in Zimmerman, 2008 Am Ed Res J.] [VA: need more examples here]<br />
<br />
'''Accuracy of self-efficacy beliefs.''' (Need to cite some research about how accurate people's self-efficacy beliefs tend to be.) It is generally believed among motivation researchers that underestimating one's capabilities tends to be worse - from a viewpoint of future academic achievement (??? check!) - than overestimating one's capabilities.<br />
<br />
<br />
<br />
'''References'''<br />
<br />
Schunk D., & Ertmer P. (2000) Self-regulation and academic learning: Self-efficacy enhancing interventions., In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego: Academic Press. <br />
<br />
Schunk, D. H., Pintrich P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.<br />
<br />
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166-183. <br />
<br />
<br />
<br />
'''Links'''<br />
<br />
http://www.des.emory.edu/mfp/efftalk.html<br />
<br />
http://www.des.emory.edu/mfp/effchapter.html<br />
<br />
http://en.wikipedia.org/wiki/Self-efficacy</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Self_Efficacy&diff=8588Self Efficacy2008-11-28T22:20:30Z<p>Aleven: </p>
<hr />
<div>Note: this page is currently under construction. Please do not cite.<br />
<br />
Self-efficacy is a person's perception of their own capability to attain certain goals or complete a certain task (Schunk, Pintrich, & Meece, 2008; p. 8). Self-efficacy is an important construct in multiple theories of motivation (Bandura's social-cognitive theory of motivation, Eccles and Wigfield's expectancy-value theories, Weiner's attribution theory (Weiner). [REF Dweck?] It also features prominently in theories of self-regulated learning [REFs].<br />
<br />
The notion of self-efficacy encompasses both students' beliefs related to fairly specific tasks or abilities (e.g., solving linear equations, or even, solving the next learning task as well as students' beliefs related to broader competencies. Some studies have investigated how specific self-efficacy beliefs tend to be [REFS]. [VA: what did these studies find?]<br />
<br />
The notion of self-efficacy is related to but different from constructs such as feeling of knowing (FOK), self-concept, self-competence, and self-esteem. <br />
Although self-efficacy is an inherently subjective notion, in many academic learning situations people's subjective self-efficacy beliefs may be heavily influenced by more objective measurements of their competence, for example their results on tests and quizzes.<br />
<br />
'''Measurement:''' self-efficacy is typically assessed by means of questionnaires (MSLQ?) and is often related to a specific academic task [NEED EXAMPLE QUESTIONNAIRE ITEMS). Teacher assessment is sometimes used. Recently, researchers have started to devise ways to automatically measure self-efficacy within computer-based learning environments. For example, Lester, McQuiggan (sp?), Boyer at NC State [REFS] have started to create machine-learned detectors that (unobtrusively and automatically, in real time) detect behaviors that reflect high/low self-efficacy within a serious game. It is still early days for this kind of work; for example, further validation efforts are needed. Nonetheless, these kinds of detectors are promising tools for researchers and may also be used to create learning environments that adapt to students' level of self-efficacy (e.g., by varying the challenge level).<br />
<br />
'''Sources of self-efficacy beliefs''' Under attribution theory, students' self-efficacy is influenced heavily by the causes to which they attribute their successes and failures in learning tasks. [REFs] A key way in which learners may increase their self-efficacy is by having successful learning experiences and attributing them to their ability in the domain being studied. Attributing unsuccessful learning experiences to lack of ability, on the other hand, is likely lead to diminished self-efficacy. However, unsuccessful learning experiences do not inevitably lead to diminished self-efficacy, for example when they are attributed to lack of effort rather than lack of ability. [VA: NEED TO CHECK ON THIS STORY] Under expectancy-value theory, students' self-efficacy beliefs are grounded in their prior learning experiences. [VA: Duh. Need something more specific.] Within certain models of self-regulated learning (e.g., Zimmerman, 2008), students' satisfaction with their performance in certain learning tasks is seen as a major source of self-efficacy beliefs. These feelings are in focus especially in a self-reflection phase that follows - in this particular cyclical model - phases of forethought and performance. [VA: after feedback? or self-evaluation?] <br />
<br />
'''Effects of self-efficacy beliefs''' Many studies find that self-efficacy is highly correlated with academic achievement [REFs].<br />
<br />
'''Interventions that enhance self-efficacy''' A number of studies have demonstrated that certain interventions enhance self-efficacy and learning (Schunk & Ertmer, 2000). [Check papers referenced in Zimmerman, 2008 Am Ed Res J.]<br />
<br />
'''Accuracy of self-efficacy beliefs.''' (Need to cite some research about how accurate people's self-efficacy beliefs tend to be.) It is generally believed among motivation researchers that underestimating one's capabilities tends to be worse - from a viewpoint of future academic achievement (??? check!) - than overestimating one's capabilities.<br />
<br />
<br />
<br />
'''References'''<br />
<br />
Schunk D., & Ertmer P. (2000) Self-regulation and academic learning: Self-efficacy enhancing interventions., In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego: Academic Press. <br />
<br />
Schunk, D. H., Pintrich P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.<br />
<br />
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166-183. <br />
<br />
<br />
<br />
'''Links'''<br />
<br />
http://www.des.emory.edu/mfp/efftalk.html<br />
<br />
http://www.des.emory.edu/mfp/effchapter.html<br />
<br />
http://en.wikipedia.org/wiki/Self-efficacy</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Self_Efficacy&diff=8587Self Efficacy2008-11-28T22:10:50Z<p>Aleven: </p>
<hr />
<div>Note: this page is currently under construction. Please do not cite.<br />
<br />
Self-efficacy is a person's perception of their own capability to attain certain goals or complete a certain task (Schunk, Pintrich, & Meece, 2008; p. 8). Self-efficacy is an important construct in multiple theories of motivation (Bandura's social-cognitive theory of motivation, Eccles and Wigfield's expectancy-value theories, Weiner's attribution theory (Weiner). [REF Dweck?] It also features prominently in theories of self-regulated learning [REFs].<br />
<br />
Although there is some debate about how specific self-efficacy beliefs tend to be [REFS], they are often taken to relate to relatively specific abilities. Not so much the general belief that I am competent in algebra, but that I am competent in a specific (type of) algebraic task (e.g., solving linear equations or even linear equations of a certain type). <br />
<br />
Self-efficacy is related to but different from constructs such as feeling of knowing (FOK), self-concept, self-competence, and self-esteem. <br />
Although self-efficacy is an inherently subjective notion, in many academic learning situations people's subjective self-efficacy beliefs may be heavily influenced by more objective measurements of their competence, for example their results on tests and quizzes.<br />
<br />
'''Measurement:''' self-efficacy is typically assessed by means of questionnaires (MSLQ?) and is often related to a specific academic task [NEED EXAMPLE QUESTIONNAIRE ITEMS). Teacher assessment is sometimes used. Recently, researchers have started to devise ways to automatically measure self-efficacy within computer-based learning environments. For example, Lester, McQuiggan (sp?), Boyer at NC State [REFS] have started to create machine-learned detectors that (unobtrusively and automatically, in real time) detect behaviors that reflect high/low self-efficacy within a serious game. It is still early days for this kind of work; for example, further validation efforts are needed. Nonetheless, these kinds of detectors are promising tools for researchers and may also be used to create learning environments that adapt to students' level of self-efficacy (e.g., by varying the challenge level).<br />
<br />
'''Sources of self-efficacy beliefs''' Under attribution theory, students' self-efficacy is influenced heavily by the causes to which they attribute their successes and failures in learning tasks. [REFs] A key way in which learners may increase their self-efficacy is by having successful learning experiences and attributing them to their ability in the domain being studied. Attributing unsuccessful learning experiences to lack of ability, on the other hand, is likely lead to diminished self-efficacy. However, unsuccessful learning experiences do not inevitably lead to diminished self-efficacy, for example when they are attributed to lack of effort rather than lack of ability. [VA: NEED TO CHECK ON THIS STORY] Under expectancy-value theory, students' self-efficacy beliefs are grounded in their prior learning experiences. [VA: Duh. Need something more specific.] Within certain models of self-regulated learning (e.g., Zimmerman, 2008), students' satisfaction with their performance in certain learning tasks is seen as a major source of self-efficacy beliefs. These feelings are in focus especially in a self-reflection phase that follows - in this particular cyclical model - phases of forethought and performance. [VA: after feedback? or self-evaluation?] <br />
<br />
'''Effects of self-efficacy beliefs''' Many studies find that self-efficacy is highly correlated with academic achievement [REFs].<br />
<br />
'''Interventions that enhance self-efficacy''' A number of studies have demonstrated that certain interventions enhance self-efficacy and learning (Schunk & Ertmer, 2000). [Check papers referenced in Zimmerman, 2008 Am Ed Res J.]<br />
<br />
'''Accuracy of self-efficacy beliefs.''' (Need to cite some research about how accurate people's self-efficacy beliefs tend to be.) It is generally believed among motivation researchers that underestimating one's capabilities tends to be worse - from a viewpoint of future academic achievement (??? check!) - than overestimating one's capabilities.<br />
<br />
<br />
<br />
'''References'''<br />
<br />
Schunk D., & Ertmer P. (2000) Self-regulation and academic learning: Self-efficacy enhancing interventions., In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego: Academic Press. <br />
<br />
Schunk, D. H., Pintrich P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.<br />
<br />
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166-183. <br />
<br />
<br />
<br />
'''Links'''<br />
<br />
http://www.des.emory.edu/mfp/efftalk.html<br />
<br />
http://www.des.emory.edu/mfp/effchapter.html<br />
<br />
http://en.wikipedia.org/wiki/Self-efficacy</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Self_Efficacy&diff=8586Self Efficacy2008-11-28T22:07:35Z<p>Aleven: </p>
<hr />
<div>Note: this page is currently under construction. Please do not cite.<br />
<br />
Self-efficacy is a person's perception of their own capability to attain certain goals or complete a certain task (Schunk, Pintrich, & Meece, 2008; p. 8). Self-efficacy is an important construct in multiple theories of motivation (Bandura's social-cognitive theory of motivation, Eccles and Wigfield's expectancy-value theories, Weiner's attribution theory (Weiner). [REF Dweck?] It also features prominently in theories of self-regulated learning [REFs].<br />
<br />
Although there is some debate about how specific self-efficacy beliefs tend to be [REFS], they are often taken to relate to relatively specific abilities. Not so much the general belief that I am competent in algebra, but that I am competent in a specific (type of) algebraic task (e.g., solving linear equations or even linear equations of a certain type). <br />
<br />
Self-efficacy is related to but different from constructs such as feeling of knowing (FOK), self-concept, self-competence, and self-esteem. <br />
Although self-efficacy is an inherently subjective notion, in many academic learning situations people's subjective self-efficacy beliefs may be heavily influenced by more objective measurements of their competence, for example their results on tests and quizzes.<br />
<br />
'''Measurement:''' self-efficacy is typically assessed by means of questionnaires (MSLQ?) and is often related to a specific academic task [NEED EXAMPLE QUESTIONNAIRE ITEMS). Teacher assessment is sometimes used. Recently, researchers have started to devise ways to automatically measure self-efficacy within computer-based learning environments. For example, Lester, McQuiggan (sp?), Boyer at NC State [REFS] have started to create machine-learned detectors that (unobtrusively and automatically, in real time) detect behaviors that reflect high/low self-efficacy within a serious game. It is still early days for this kind of work; for example, further validation efforts are needed. Nonetheless, these kinds of detectors are promising tools for researchers and may also be used to create learning environments that adapt to students' level of self-efficacy (e.g., by varying the challenge level).<br />
<br />
'''Sources of self-efficacy beliefs''' Under attribution theory, students' self-efficacy is influenced heavily by the causes to which they attribute their successes and failures in learning tasks. [REFs] A key way in which learners may increase their self-efficacy is by having successful learning experiences and attributing them to their ability in the domain being studied. Attributing unsuccessful learning experiences to lack of ability is likely lead to diminished self-efficacy. However, unsuccessful learning experiences do not inevitably lead to diminished self-efficacy, for example when they are attributed to lack of effort rather than lack of ability. [VA: NEED TO CHECK ON THIS STORY] Under expectancy-value theory, students' self-efficacy beliefs are grounded in their prior learning experiences. [VA: Duh. Need something more specific.] Within certain models of self-regulated learning (e.g., Zimmerman, 2008), students' satisfaction with their performance in certain learning tasks [VA: after feedback? or self-evaluation?] is seen as a major source of self-efficacy beliefs.<br />
<br />
'''Effects of self-efficacy beliefs''' Many studies find that self-efficacy is highly correlated with academic achievement [REFs].<br />
<br />
'''Interventions that enhance self-efficacy''' A number of studies have demonstrated that certain interventions enhance self-efficacy and learning (Schunk & Ertmer, 2000). [Check papers referenced in Zimmerman, 2008 Am Ed Res J.]<br />
<br />
'''Accuracy of self-efficacy beliefs.''' (Need to cite some research about how accurate people's self-efficacy beliefs tend to be.) It is generally believed among motivation researchers that underestimating one's capabilities tends to be worse - from a viewpoint of future academic achievement (??? check!) - than overestimating one's capabilities.<br />
<br />
<br />
<br />
'''References'''<br />
<br />
Schunk D., & Ertmer P. (2000) Self-regulation and academic learning: Self-efficacy enhancing interventions., In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego: Academic Press. <br />
<br />
Schunk, D. H., Pintrich P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.<br />
<br />
Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166-183. <br />
<br />
<br />
<br />
'''Links'''<br />
<br />
http://www.des.emory.edu/mfp/efftalk.html<br />
<br />
http://www.des.emory.edu/mfp/effchapter.html<br />
<br />
http://en.wikipedia.org/wiki/Self-efficacy</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Self_Efficacy&diff=8585Self Efficacy2008-11-28T21:45:34Z<p>Aleven: </p>
<hr />
<div>Note: this page is currently under construction. Please do not cite.<br />
<br />
Self-efficacy is a person's perception of their own capability to attain certain goals or complete a certain task (Schunk, Pintrich, & Meece, 2008; p. 8). Self-efficacy is an important construct in multiple theories of motivation (Bandura's social-cognitive theory of motivation, Eccles and Wigfield's expectancy-value theories, Weiner's attribution theory (Weiner). [REF Dweck?] It also features prominently in theories of self-regulated learning [REFs].<br />
<br />
Although there is some debate about how specific self-efficacy beliefs tend to be [REFS], they are often taken to relate to relatively specific abilities. Not so much the general belief that I am competent in algebra, but that I am competent in a specific (type of) algebraic task (e.g., solving linear equations or even linear equations of a certain type). <br />
<br />
Self-efficacy is related to but different from constructs such as feeling of knowing (FOK), self-concept, self-competence, and self-esteem. <br />
Although self-efficacy is an inherently subjective notion, in many academic learning situations people's subjective self-efficacy beliefs may be heavily influenced by more objective measurements of their competence, for example their results on tests and quizzes.<br />
<br />
'''Measurement:''' self-efficacy is typically assessed by means of questionnaires (MSLQ?) and is often related to a specific academic task [NEED EXAMPLE QUESTIONNAIRE ITEMS). Teacher assessment is sometimes used. Recently, researchers have started to devise ways to automatically measure self-efficacy within computer-based learning environments. For example, Lester, McQuiggan (sp?), Boyer at NC State [REFS] have started to create machine-learned detectors that (unobtrusively and automatically, in real time) detect behaviors that reflect high/low self-efficacy within a serious game. It is still early days for this kind of work; for example, further validation efforts are needed. These kinds of detectors however are promising tools for researchers and may also be used to create learning environments that adapt to students' level of self-efficacy (e.g., by varying the challenge level).<br />
<br />
'''Sources of self-efficacy beliefs''' Under attribution theory, students' self-efficacy is influenced heavily by the causes to which they attribute their successes and failures in learning tasks. [REFs] A key way in which learners may increase their self-efficacy is by having successful learning experiences and attributing them to their ability in the domain being studied. Attributing unsuccessful learning experiences to lack of ability is likely lead to diminished self-efficacy. However, unsuccessful learning experiences do not inevitably lead to diminished self-efficacy, for example when they are attributed to lack of effort rather than lack of ability. [VA: NEED TO CHECK ON THIS STORY] Under expectancy-value theory, students' self-efficacy beliefs are grounded in their prior learning experiences. [VA: Duh. Need something more specific.] <br />
<br />
'''Effects of self-efficacy beliefs''' Many studies find that self-efficacy is highly correlated with academic achievement [REFs].<br />
<br />
'''Interventions that enhance self-efficacy''' A number of studies have demonstrated that certain interventions enhance self-efficacy and learning (Schunk & Ertmer, 2000). [Check papers referenced in Zimmerman, 2008 Am Ed Res J.]<br />
<br />
'''Accuracy of self-efficacy beliefs.''' (Need to cite some research about how accurate people's self-efficacy beliefs tend to be.) It is generally believed among motivation researchers that underestimating one's capabilities tends to be worse - from a viewpoint of future academic achievement (??? check!) - than overestimating one's capabilities.<br />
<br />
<br />
<br />
'''References'''<br />
<br />
Schunk D., & Ertmer P. (2000) Self-regulation and academic learning: Self-efficacy enhancing interventions., In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego: Academic Press. <br />
<br />
Schunk, D. H., Pintrich P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.<br />
<br />
<br />
'''Links'''<br />
<br />
http://www.des.emory.edu/mfp/efftalk.html<br />
<br />
http://www.des.emory.edu/mfp/effchapter.html<br />
<br />
http://en.wikipedia.org/wiki/Self-efficacy</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Self_Efficacy&diff=8584Self Efficacy2008-11-28T21:27:57Z<p>Aleven: </p>
<hr />
<div>Note: this page is currently under construction. Please do not cite.<br />
<br />
Self-efficacy is a person's perception of their own capability to attain certain goals or complete a certain task (Schunk, Pintrich, & Meece, 2008; p. 8). Self-efficacy is an important construct in multiple theories of motivation (Bandura's social-cognitive theory of motivation, Eccles and Wigfield's expectancy-value theories, Weiner's attribution theory (Weiner). [REF Dweck?] It also features prominently in theories of self-regulated learning [REFs].<br />
<br />
Although there is some debate about how specific self-efficacy beliefs tend to be [REFS], they are often taken to relate to relatively specific abilities. Not so much the general belief that I am competent in algebra, but that I am competent in a specific (type of) algebraic task (e.g., solving linear equations or even linear equations of a certain type). <br />
<br />
Self-efficacy is related to but different from constructs such as feeling of knowing (FOK), self-concept, self-competence, and self-esteem. <br />
Although self-efficacy is an inherently subjective notion, in many academic learning situations people's subjective self-efficacy beliefs may be heavily influenced by more objective measurements of their competence, for example their results on tests and quizzes.<br />
<br />
'''Measurement:''' self-efficacy is typically assessed by means of questionnaires (MSLQ?) and is often related to a specific academic task [NEED EXAMPLE QUESTIONNAIRE ITEMS). Teacher assessment is sometimes used. Recently, researchers have started to look at ways of automatically measuring self-efficacy within computer-based learning environments. For example, Lester, McQuiggan (sp?), Boyer at NC State [REFS] have started to create machine-learned detectors that (unobtrusively and automatically, in real time) detect behaviors that reflect high/low self-efficacy within a serious game. It is still early days for this kind of work. Further validation efforts are needed.<br />
<br />
'''Sources of self-efficacy beliefs''' Under attribution theory, students' self-efficacy is influenced heavily by the causes to which they attribute their successes and failures in learning task. [REFs] Attributing unsuccessful learning experiences to lack of ability do not necessarily need to lead to diminished self-efficacy, for example when they are attributed to lack of effort rather than lack of ability.<br />
<br />
'''Effects of self-efficacy beliefs''' Many studies find that self-efficacy is highly correlated with academic achievement [REFs].<br />
<br />
'''Interventions that enhance self-efficacy''' A number of studies have demonstrated that certain interventions enhance self-efficacy and learning (Schunk & Ertmer, 2000). [Check papers referenced in Zimmerman, 2008 Am Ed Res J.]<br />
<br />
'''Accuracy of self-efficacy beliefs.''' (Need to cite some research about how accurate people's self-efficacy beliefs tend to be.) It is generally believed among motivation researchers that underestimating one's capabilities tends to be worse - from a viewpoint of future academic achievement (??? check!) - than overestimating one's capabilities.<br />
<br />
<br />
<br />
'''References'''<br />
<br />
Schunk D., & Ertmer P. (2000) Self-regulation and academic learning: Self-efficacy enhancing interventions., In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego: Academic Press. <br />
<br />
Schunk, D. H., Pintrich P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.<br />
<br />
<br />
'''Links'''<br />
<br />
http://www.des.emory.edu/mfp/efftalk.html<br />
<br />
http://www.des.emory.edu/mfp/effchapter.html<br />
<br />
http://en.wikipedia.org/wiki/Self-efficacy</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Talk:Self_Efficacy&diff=8583Talk:Self Efficacy2008-11-28T21:10:15Z<p>Aleven: </p>
<hr />
<div>Is learned helplessness a related construct? Has it been interpreted as a lack of self-efficacy? (VA)<br />
<br />
(VA) Question: have researchers distinguished between beliefs about one's ability to _perform_ in a certain domain and one's ability to _learn_ in a certain domain? One probably expects these to be highly correlated and perhaps often not clearly distinguished in people's minds ... has that distinction/correlation been studied? Would it be interesting to study? Would it be a PSLCish thing to study? (VA) (The following paper makes the distinction: Lodewyk, Ken R. Department of Human Performance and Sport Management, Mount Union College, Alliance, OH, US; Winne, Philip H. Faculty of Education, Simon Fraser University, Burnaby, BC, Canada E-mail: lodewykk@muc.edu<br />
Relations Among the Structure of Learning Tasks, Achievement, and Changes in Self-Efficacy in Secondary Students.<br />
Journal of Educational Psychology Vol 97(1) (Feb 2005): 3-1.<br />
<br />
(VA) Some studies find that self-efficacy is a better predictor of learning (future academic achievement - probably fair to view that as learning???) than prior knowledge.<br />
<br />
(VA) Are there findings that show that people's attributions of success and failure in learning affect their self-efficacy?<br />
<br />
(VA) The often-observed correlations between self-efficacy and learning (learning??? or performance???) could simply reflect the fact that people are (somewhat) accurate in their assessment of their own competence, although there is probably more to it than that. (E.g. I personally find it rather likely that self-efficacy would lead to greater persistence. Conversely, learned helplessness - if we may interpret it for now as a manifestation of low self-efficacy - would lead one to not try or give up easily.) But a causal link between self-efficacy and learning is hard to establish, because it is different to manipulate self-efficacy without also manipulating other factors of the learning environment (but see Schunk & Ertmer (2000); also, perhaps attribution re-training is an effective way of fairly directly "manipulating" self-efficacy).</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Self_Efficacy&diff=8582Self Efficacy2008-11-28T21:08:31Z<p>Aleven: </p>
<hr />
<div>Note: this page is currently under construction. Please do not cite.<br />
<br />
Self-efficacy is a person's perception of their own capability to attain certain goals or complete a certain task (Schunk, Pintrich, & Meece, 2008; p. 8). Self-efficacy is an important construct in multiple theories of motivation (Bandura's social-cognitive theory of motivation, Eccles and Wigfield's expectancy-value theories, Weiner's attribution theory (Weiner). [REF Dweck?] It also features prominently in theories of self-regulated learning [REFs].<br />
<br />
Although there is some debate about how specific self-efficacy beliefs tend to be [REFS], they are often taken to relate to relatively specific abilities. Not so much the general belief that I am competent in algebra, but that I am competent in a specific (type of) algebraic task (e.g., solving linear equations or even linear equations of a certain type). <br />
<br />
Self-efficacy is related to but different from constructs such as feeling of knowing (FOK), self-concept, self-competence, and self-esteem. <br />
Although self-efficacy is an inherently subjective notion, in many academic learning situations people's "subjective" self-efficacy beliefs may be heavily influenced by "objective" measurements of their competence, such as for example their results on tests and quizzes.<br />
<br />
'''Measurement:''' self-efficacy is typically assessed by means of questionnaires (MSLQ?) and is often related to a specific task [NEED EXAMPLE QUESTIONNAIRE ITEMS). Teacher assessment is sometimes used.<br />
<br />
'''Sources of self-efficacy beliefs''' A number of studies have demonstrated that certain interventions enhance self-efficacy and learning (Schunk & Ertmer, 2000). [Check papers referenced in Zimmerman, 2008 Am Ed Res J.]<br />
<br />
'''Effects of self-efficacy beliefs''' Many studies find that self-efficacy is highly correlated with academic achievement [REFs].<br />
<br />
'''Accuracy of self-efficacy beliefs.''' (Need to cite some research about how accurate people's self-efficacy beliefs tend to be.) It is generally believed among motivation researchers that underestimating one's capabilities tends to be worse - from a viewpoint of future academic achievement (??? check!) - than overestimating one's capabilities.<br />
<br />
Recently, researchers have started to look at ways of automatically measuring self-efficacy within computer-based learning environments. For example, Lester, McQuiggan (sp?), Boyer at NC State [REFS] have started to create machine-learned detectors that (unobtrusively and automatically, in real time) detect behaviors that reflect high/low self-efficacy within a serious game. These types of detectors open up novel opportunities for learning sciences research. It is still an open question to what degree such detectors can be used to enhance the effectiveness of the learning environment (for example, by helping the learner develop greater self-efficacy, or by adapting in other ways to learners' self-efficacy). Further validation efforts may be needed to ensure that these types of automated detectors honor the original notion of self-efficacy.<br />
<br />
<br />
'''References'''<br />
<br />
Schunk D., & Ertmer P. (2000) Self-regulation and academic learning: Self-efficacy enhancing interventions., In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego: Academic Press. <br />
<br />
Schunk, D. H., Pintrich P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.<br />
<br />
<br />
'''Links'''<br />
<br />
http://www.des.emory.edu/mfp/efftalk.html<br />
<br />
http://www.des.emory.edu/mfp/effchapter.html<br />
<br />
http://en.wikipedia.org/wiki/Self-efficacy</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Talk:Self_Efficacy&diff=8581Talk:Self Efficacy2008-11-28T15:08:18Z<p>Aleven: </p>
<hr />
<div>Is learned helplessness a related construct? Has it been interpreted as a lack of self-efficacy? (VA)<br />
<br />
<br />
<br />
Question: have researchers distinguished between beliefs about one's ability to _perform_ in a certain domain and one's ability to _learn_ in a certain domain? One probably expects these to be highly correlated and perhaps often not clearly distinguished in people's minds ... has that distinction/correlation been studied? Would it be interesting to study? Would it be a PSLCish thing to study? (VA) (The following paper makes the distinction: Lodewyk, Ken R. Department of Human Performance and Sport Management, Mount Union College, Alliance, OH, US; Winne, Philip H. Faculty of Education, Simon Fraser University, Burnaby, BC, Canada E-mail: lodewykk@muc.edu<br />
Relations Among the Structure of Learning Tasks, Achievement, and Changes in Self-Efficacy in Secondary Students.<br />
Journal of Educational Psychology Vol 97(1) (Feb 2005): 3-1.<br />
<br />
<br />
Some studies find that self-efficacy is a better predictor of learning (future academic achievement - probably fair to view that as learning???) than prior knowledge.<br />
<br />
<br />
Are there findings that show that people's attributions of success and failure in learning affect their self-efficacy?</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Self_Efficacy&diff=8580Self Efficacy2008-11-28T15:07:55Z<p>Aleven: </p>
<hr />
<div>Note: this page is currently under construction. Please do not cite.<br />
<br />
Self-efficacy is a person's perception of their own capability to attain certain goals or complete a certain task (Schunk, Pintrich, & Meece, 2008; p. 8). Self-efficacy is an important construct in multiple theories of motivation (Bandura's social-cognitive theory of motivation, Eccles and Wigfield's expectancy-value theories, Weiner's attribution theory (Weiner). [REF Dweck?] It also features prominently in theories of self-regulated learning [REFs].<br />
<br />
Although there is some debate about how specific self-efficacy beliefs tend to be [REFS], they are often taken to relate to relatively specific abilities. Not so much the general belief that I am competent in algebra, but that I am competent in a specific (type of) algebraic task (e.g., solving linear equations or even linear equations of a certain type). <br />
<br />
Self-efficacy is related to but different from constructs such as feeling of knowing (FOK), self-concept, self-competence, and self-esteem. <br />
Although self-efficacy is an inherently subjective notion, in many academic learning situations people's "subjective" self-efficacy beliefs may be heavily influenced by "objective" measurements of their competence, such as for example their results on tests and quizzes.<br />
<br />
'''Measurement:''' self-efficacy is typically assessed by means of questionnaires (MSLQ?) and is often related to a specific task [NEED EXAMPLE QUESTIONNAIRE ITEMS). Teacher assessment is sometimes used.<br />
<br />
'''Sources of self-efficacy beliefs''' A number of studies have demonstrated that certain interventions enhance self-efficacy and learning (Schunk & Ertmer, 2000). [Check papers referenced in Zimmerman, 2008 Am Ed Res J.]<br />
<br />
'''Effects of self-efficacy beliefs''' Many studies find that self-efficacy is highly correlated with academic achievement [REFs].<br />
<br />
The often-observed correlations between self-efficacy and learning (learning??? or performance???) could simply reflect the fact that people are (somewhat) accurate in their assessment of their own competence, although there is probably more to it than that. (E.g. I personally find it rather likely that self-efficacy would lead to greater persistence. Conversely, learned helplessness - if we may interpret it for now as a manifestation of low self-efficacy - would lead one to not try or give up easily.) But a causal link between self-efficacy and learning is hard to establish, because it is different to manipulate self-efficacy without also manipulating other factors of the learning environment (but see Schunk & Ertmer (2000); also, perhaps attribution re-training is an effective way of fairly directly "manipulating" self-efficacy). <br />
<br />
'''Accuracy of self-efficacy beliefs.''' (Need to cite some research about how accurate people's self-efficacy beliefs tend to be.) It is generally believed among motivation researchers that underestimating one's capabilities tends to be worse - from a viewpoint of future academic achievement (??? check!) - than overestimating one's capabilities.<br />
<br />
Recently, researchers have started to look at ways of automatically measuring self-efficacy within computer-based learning environments. For example, Lester, McQuiggan (sp?), Boyer at NC State [REFS] have started to create machine-learned detectors that (unobtrusively and automatically, in real time) detect behaviors that reflect high/low self-efficacy within a serious game. These types of detectors open up novel opportunities for learning sciences research. It is still an open question to what degree such detectors can be used to enhance the effectiveness of the learning environment (for example, by helping the learner develop greater self-efficacy, or by adapting in other ways to learners' self-efficacy). Further validation efforts may be needed to ensure that these types of automated detectors honor the original notion of self-efficacy.<br />
<br />
<br />
'''References'''<br />
<br />
Schunk D., & Ertmer P. (2000) Self-regulation and academic learning: Self-efficacy enhancing interventions., In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego: Academic Press. <br />
<br />
Schunk, D. H., Pintrich P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.<br />
<br />
<br />
'''Links'''<br />
<br />
http://www.des.emory.edu/mfp/efftalk.html<br />
<br />
http://www.des.emory.edu/mfp/effchapter.html<br />
<br />
http://en.wikipedia.org/wiki/Self-efficacy</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Talk:Self_Efficacy&diff=8579Talk:Self Efficacy2008-11-28T15:06:30Z<p>Aleven: </p>
<hr />
<div>Is learned helplessness a related construct? Has it been interpreted as a lack of self-efficacy? (VA)<br />
<br />
<br />
<br />
Question: have researchers distinguished between beliefs about one's ability to _perform_ in a certain domain and one's ability to _learn_ in a certain domain? One probably expects these to be highly correlated and perhaps often not clearly distinguished in people's minds ... has that distinction/correlation been studied? Would it be interesting to study? Would it be a PSLCish thing to study? (VA) (The following paper makes the distinction: Lodewyk, Ken R. Department of Human Performance and Sport Management, Mount Union College, Alliance, OH, US; Winne, Philip H. Faculty of Education, Simon Fraser University, Burnaby, BC, Canada E-mail: lodewykk@muc.edu<br />
Relations Among the Structure of Learning Tasks, Achievement, and Changes in Self-Efficacy in Secondary Students.<br />
Journal of Educational Psychology Vol 97(1) (Feb 2005): 3-1.<br />
<br />
<br />
Some studies find that self-efficacy is a better predictor of learning (future academic achievement - probably fair to view that as learning???) than prior knowledge.</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Self_Efficacy&diff=8578Self Efficacy2008-11-28T15:06:16Z<p>Aleven: </p>
<hr />
<div>Note: this page is currently under construction. Please do not cite.<br />
<br />
Self-efficacy is a person's perception of their own capability to attain certain goals or complete a certain task (Schunk, Pintrich, & Meece, 2008; p. 8). Self-efficacy is an important construct in multiple theories of motivation (Bandura's social-cognitive theory of motivation, Eccles and Wigfield's expectancy-value theories, Weiner's attribution theory (Weiner). [REF Dweck?] It also features prominently in theories of self-regulated learning [REFs].<br />
<br />
Although there is some debate about how specific self-efficacy beliefs tend to be [REFS], they are often taken to relate to relatively specific abilities. Not so much the general belief that I am competent in algebra, but that I am competent in a specific (type of) algebraic task (e.g., solving linear equations or even linear equations of a certain type). <br />
<br />
Self-efficacy is related to but different from constructs such as feeling of knowing (FOK), self-concept, self-competence, and self-esteem. <br />
Although self-efficacy is an inherently subjective notion, in many academic learning situations people's "subjective" self-efficacy beliefs may be heavily influenced by "objective" measurements of their competence, such as for example their results on tests and quizzes.<br />
<br />
'''Measurement:''' self-efficacy is typically assessed by means of questionnaires (MSLQ?) and is often related to a specific task [NEED EXAMPLE QUESTIONNAIRE ITEMS). Teacher assessment is sometimes used.<br />
<br />
'''Sources of self-efficacy beliefs''' Some studies find (I believe) that particular interventions enhance self-efficacy and learning (Schunk & Ertmer, 2000). Are there findings that show that people's attributions of success and failure in learning affect their self-efficacy?<br />
<br />
'''Effects of self-efficacy beliefs''' Many studies find that self-efficacy is highly correlated with academic achievement [REFs].<br />
<br />
The often-observed correlations between self-efficacy and learning (learning??? or performance???) could simply reflect the fact that people are (somewhat) accurate in their assessment of their own competence, although there is probably more to it than that. (E.g. I personally find it rather likely that self-efficacy would lead to greater persistence. Conversely, learned helplessness - if we may interpret it for now as a manifestation of low self-efficacy - would lead one to not try or give up easily.) But a causal link between self-efficacy and learning is hard to establish, because it is different to manipulate self-efficacy without also manipulating other factors of the learning environment (but see Schunk & Ertmer (2000); also, perhaps attribution re-training is an effective way of fairly directly "manipulating" self-efficacy). <br />
<br />
'''Accuracy of self-efficacy beliefs.''' (Need to cite some research about how accurate people's self-efficacy beliefs tend to be.) It is generally believed among motivation researchers that underestimating one's capabilities tends to be worse - from a viewpoint of future academic achievement (??? check!) - than overestimating one's capabilities.<br />
<br />
Recently, researchers have started to look at ways of automatically measuring self-efficacy within computer-based learning environments. For example, Lester, McQuiggan (sp?), Boyer at NC State [REFS] have started to create machine-learned detectors that (unobtrusively and automatically, in real time) detect behaviors that reflect high/low self-efficacy within a serious game. These types of detectors open up novel opportunities for learning sciences research. It is still an open question to what degree such detectors can be used to enhance the effectiveness of the learning environment (for example, by helping the learner develop greater self-efficacy, or by adapting in other ways to learners' self-efficacy). Further validation efforts may be needed to ensure that these types of automated detectors honor the original notion of self-efficacy.<br />
<br />
<br />
'''References'''<br />
<br />
Schunk D., & Ertmer P. (2000) Self-regulation and academic learning: Self-efficacy enhancing interventions., In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego: Academic Press. <br />
<br />
Schunk, D. H., Pintrich P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.<br />
<br />
<br />
'''Links'''<br />
<br />
http://www.des.emory.edu/mfp/efftalk.html<br />
<br />
http://www.des.emory.edu/mfp/effchapter.html<br />
<br />
http://en.wikipedia.org/wiki/Self-efficacy</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Talk:Self_Efficacy&diff=8577Talk:Self Efficacy2008-11-28T15:04:47Z<p>Aleven: </p>
<hr />
<div>Is learned helplessness a related construct? Has it been interpreted as a lack of self-efficacy? (VA)<br />
<br />
<br />
<br />
Question: have researchers distinguished between beliefs about one's ability to _perform_ in a certain domain and one's ability to _learn_ in a certain domain? One probably expects these to be highly correlated and perhaps often not clearly distinguished in people's minds ... has that distinction/correlation been studied? Would it be interesting to study? Would it be a PSLCish thing to study? (VA) (The following paper makes the distinction: Lodewyk, Ken R. Department of Human Performance and Sport Management, Mount Union College, Alliance, OH, US; Winne, Philip H. Faculty of Education, Simon Fraser University, Burnaby, BC, Canada E-mail: lodewykk@muc.edu<br />
Relations Among the Structure of Learning Tasks, Achievement, and Changes in Self-Efficacy in Secondary Students.<br />
Journal of Educational Psychology Vol 97(1) (Feb 2005): 3-1.</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Self_Efficacy&diff=8576Self Efficacy2008-11-28T15:02:53Z<p>Aleven: </p>
<hr />
<div>Note: this page is currently under construction. Please do not cite.<br />
<br />
Self-efficacy is a person's perception of their own capability to attain certain goals or complete a certain task (Schunk, Pintrich, & Meece, 2008; p. 8). Self-efficacy is an important construct in multiple theories of motivation (Bandura's social-cognitive theory of motivation, Eccles and Wigfield's expectancy-value theories, Weiner's attribution theory (Weiner). [REF Dweck?] It also features prominently in theories of self-regulated learning [REFs].<br />
<br />
Although there is some debate about how specific self-efficacy beliefs tend to be [REFS], they are often taken to relate to relatively specific abilities. Not so much the general belief that I am competent in algebra, but that I am competent in a specific (type of) algebraic task (e.g., solving linear equations or even linear equations of a certain type). <br />
<br />
Self-efficacy is related to but different from constructs such as feeling of knowing (FOK), self-concept, self-competence, and self-esteem. <br />
Although self-efficacy is an inherently subjective notion, in many academic learning situations people's "subjective" self-efficacy beliefs may be heavily influenced by "objective" measurements of their competence, such as for example their results on tests and quizzes.<br />
<br />
<br />
<br />
'''Measurement:''' self-efficacy is typically assessed by means of questionnaires (MSLQ?) and is often related to a specific task (e.g., a subject is presented with a specific task instances and asked "how well do you think you will do on this task?". (Need to check this and give real not made-up examples.) Teacher assessment is sometimes used.<br />
<br />
'''Sources of self-efficacy beliefs''' Some studies find (I believe) that particular interventions enhance self-efficacy and learning (Schunk & Ertmer, 2000). Are there findings that show that people's attributions of success and failure in learning affect their self-efficacy?<br />
<br />
'''Effects of self-efficacy beliefs''' Many studies find that self-efficacy is highly correlated with learning (or performance???). Some studies find that self-efficacy is a better predictor of learning (future academic achievement - probably fair to view that as learning???) than prior knowledge. <br />
<br />
The often-observed correlations between self-efficacy and learning (learning??? or performance???) could simply reflect the fact that people are (somewhat) accurate in their assessment of their own competence, although there is probably more to it than that. (E.g. I personally find it rather likely that self-efficacy would lead to greater persistence. Conversely, learned helplessness - if we may interpret it for now as a manifestation of low self-efficacy - would lead one to not try or give up easily.) But a causal link between self-efficacy and learning is hard to establish, because it is different to manipulate self-efficacy without also manipulating other factors of the learning environment (but see Schunk & Ertmer (2000); also, perhaps attribution re-training is an effective way of fairly directly "manipulating" self-efficacy). <br />
<br />
'''Accuracy of self-efficacy beliefs.''' (Need to cite some research about how accurate people's self-efficacy beliefs tend to be.) It is generally believed among motivation researchers that underestimating one's capabilities tends to be worse - from a viewpoint of future academic achievement (??? check!) - than overestimating one's capabilities.<br />
<br />
Recently, researchers have started to look at ways of automatically measuring self-efficacy within computer-based learning environments. For example, Lester, McQuiggan (sp?), Boyer at NC State [REFS] have started to create machine-learned detectors that (unobtrusively and automatically, in real time) detect behaviors that reflect high/low self-efficacy within a serious game. These types of detectors open up novel opportunities for learning sciences research. It is still an open question to what degree such detectors can be used to enhance the effectiveness of the learning environment (for example, by helping the learner develop greater self-efficacy, or by adapting in other ways to learners' self-efficacy). Further validation efforts may be needed to ensure that these types of automated detectors honor the original notion of self-efficacy.<br />
<br />
<br />
'''References'''<br />
<br />
Schunk D., & Ertmer P. (2000) Self-regulation and academic learning: Self-efficacy enhancing interventions., In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 631-649). San Diego: Academic Press. <br />
<br />
Schunk, D. H., Pintrich P. R., & Meece, J. L. (2008). Motivation in education: Theory, research, and applications (3rd Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.<br />
<br />
<br />
'''Links'''<br />
<br />
http://www.des.emory.edu/mfp/efftalk.html<br />
<br />
http://www.des.emory.edu/mfp/effchapter.html<br />
<br />
http://en.wikipedia.org/wiki/Self-efficacy</div>Alevenhttps://learnlab.org/mediawiki-1.44.2/index.php?title=Talk:Self_Efficacy&diff=8575Talk:Self Efficacy2008-11-28T15:02:23Z<p>Aleven: New page: Is learned helplessness a related construct? Has it been interpreted as a lack of self-efficacy?</p>
<hr />
<div>Is learned helplessness a related construct? Has it been interpreted as a lack of self-efficacy?</div>Aleven