The Experts below are selected from a list of 20628 Experts worldwide ranked by ideXlab platform
Kenneth R. Koedinger - One of the best experts on this subject based on the ideXlab platform.
-
Toward Cognitive Tutoring in a Collaborative Web-Based Environment
2018Co-Authors: Bruce M. Mclaren, Kenneth R. Koedinger, Mike Schnieder, Andreas Harrer, Lars BollenAbstract:While Intelligent Tutoring has been applied to collaborative learning environments, it has met with little success so far because of the complexity involved in adding a Tutoring component to a collaborative environment. We propose to tackle this problem by using Cognitive Tutors as the basis for our approach and by applying a technique we call Bootstrapping Novice Data (BND). The BND approach involves feeding student log files from a problem-solving tool into Tutor development software to create the beginnings of a Tutor for the tool. We describe an initial implementation of our approach in which Cool Modes, a collaborative software tool, is integrated with the Behavior Recorder, Tutor-authoring software that supports development by demonstration. We show how our initial implementation provides a foundation for an Intelligent Tutor for collaboration. We also discuss some of the challenges ahead
-
Opening the Door to Non-Programmers: Authoring Intelligent Tutor Behavior by Demonstration
2018Co-Authors: Kenneth R. Koedinger, Bruce M. Mclaren, Vincent Aleven, Matthew Hockenberry, Neil HeffernanAbstract:Intelligent Tutoring systems are quite difficult and time intensive to develop. In this paper, we describe a method and set of software tools that ease the process of cognitive task analysis and Tutor development by allowing the author to demonstrate, instead of programming, the behavior of an Intelligent Tutor. We focus on the subset of our tools that allow authors to create “Pseudo Tutors” that exhibit the behavior of Intelligent Tutors without requiring AI programming. Authors build user interfaces by direct manipulation and then use a Behavior Recorder tool to demonstrate alternative correct and incorrect actions. The resulting behavior graph is annotated with instructional messages and knowledge labels. We present some preliminary evidence of the effectiveness of this approach, both in terms of reduced development time and learning outcome. Pseudo Tutors have now been built for economics, analytic logic, mathematics, and language learning. Our data supports an estimate of about 25:1 ratio of development time to instruction time for Pseudo Tutors, which compares favorably to the 200:1 estimate for Intelligent Tutors, though we acknowledge and discuss limitations of such estimates
-
example tracing Tutors Intelligent Tutor development for non programmers
International Journal of Artificial Intelligence in Education, 2016Co-Authors: Vincent Aleven, Bruce M. Mclaren, Jonathan Sewall, Martin Van Velsen, Octav Popescu, Sandra Demi, Michael A Ringenberg, Kenneth R. KoedingerAbstract:In 2009, we reported on a new Intelligent Tutoring Systems (ITS) technology, example-tracing Tutors, that can be built without programming using the Cognitive Tutor Authoring Tools (CTAT). Creating example-tracing Tutors was shown to be 4–8 times as cost-effective as estimates for ITS development from the literature. Since 2009, CTAT and its associated learning management system, the Tutorshop, have been extended and have been used for both research and real-world instruction. As evidence that example-tracing Tutors are an effective and mature ITS paradigm, CTAT-built Tutors have been used by approximately 44,000 students and account for 40 % of the data sets in DataShop, a large open repository for educational technology data sets. We review 18 example-tracing Tutors built since 2009, which have been shown to be effective in helping students learn in real educational settings, often with large pre/post effect sizes. These Tutors support a variety of pedagogical approaches, beyond step-based problem solving, including collaborative learning, educational games, and guided invention activities. CTAT and other ITS authoring tools illustrate that non-programmer approaches to building ITS are viable and useful and will likely play a key role in making ITS widespread.
-
improving students help seeking skills using metacognitive feedback in an Intelligent Tutoring system
Learning and Instruction, 2011Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Kenneth R. KoedingerAbstract:The present research investigated whether immediate metacognitive feedback on students’ help-seeking errors can help students acquire better help-seeking skills. The Help Tutor, an Intelligent Tutor agent for help seeking, was integrated into a commercial Tutoring system for geometry, the Geometry Cognitive Tutor. Study 1, with 58 students, found that the real-time assessment of students’ help-seeking behavior correlated with other independent measures of help seeking, and that the Help Tutor improved students’ help-seeking behavior while learning Geometry with the Geometry Cognitive Tutor. Study 2, with 67 students, evaluated more elaborated support that included, in addition to the Help Tutor, also help-seeking instruction and support for self-assessment. The study replicated the effect found in Study 1. It was also found that the improved help-seeking skills transferred to learning new domain-level content during the month following the intervention, while the helpseeking support was no longer in effect. Implications for metacognitive Tutoring are discussed. 2010 Elsevier Ltd. All rights reserved.
-
using Intelligent Tutor technology to implement adaptive support for student collaboration
Educational Psychology Review, 2010Co-Authors: Dejana Diziol, Erin Walker, Nikol Rummel, Kenneth R. KoedingerAbstract:Research on computer-supported collaborative learning has shown that students need support to benefit from collaborative activities. While classical collaboration scripts have been effective in providing such support, they have also been criticized for being coercive and not allowing students to self-regulate their learning. Adaptive collaboration support, which would provide students with assistance when and where they need it, is a possible solution. However, due to limitations of natural language processing, the development of adaptive support based on an analysis of student dialogue is difficult. To facilitate the implementation of adaptive collaboration support, we propose to leverage existing Intelligent Tutoring technology to provide support based on student problem-solving actions. The present paper gives two examples that demonstrate this approach and reports first experiences from the implementation of the systems in real classrooms. We conclude the paper with a discussion of possible future developments in adaptive collaboration support.
Min Chi - One of the best experts on this subject based on the ideXlab platform.
-
Correction to: Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor
International Journal of Artificial Intelligence in Education, 2020Co-Authors: Mehak Maniktala, Christa Cody, Tiffany Barnes, Min ChiAbstract:A Correction to this paper has been published: https://doi.org/10.1007/s40593-020-00232-0
-
Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor
International Journal of Artificial Intelligence in Education, 2020Co-Authors: Mehak Maniktala, Christa Cody, Tiffany Barnes, Min ChiAbstract:Within Intelligent Tutoring systems, considerable research has investigated hints, including how to generate data-driven hints, what hint content to present, and when to provide hints for optimal learning outcomes. However, less attention has been paid to how hints are presented. In this paper, we propose a new hint delivery mechanism called “Assertions” for providing unsolicited hints in a data-driven Intelligent Tutor. Assertions are partially-worked example steps designed to appear within a student workspace, and in the same format as student-derived steps, to show students a possible subgoal leading to the solution. We hypothesized that Assertions can help address the well-known hint avoidance problem. In systems that only provide hints upon request, hint avoidance results in students not receiving hints when they are needed. Our unsolicited Assertions do not seek to improve student help-seeking, but rather seek to ensure students receive the help they need. We contrast Assertions with Messages, text-based, unsolicited hints that appear after student inactivity. Our results show that Assertions significantly increase unsolicited hint usage compared to Messages. Further, they show a significant aptitude-treatment interaction between Assertions and prior proficiency, with Assertions leading students with low prior proficiency to generate shorter (more efficient) posttest solutions faster. We also present a clustering analysis that shows patterns of productive persistence among students with low prior knowledge when the Tutor provides unsolicited help in the form of Assertions. Overall, this work provides encouraging evidence that hint presentation can significantly impact how students use them and using Assertions can be an effective way to address help avoidance.
Bruce M. Mclaren - One of the best experts on this subject based on the ideXlab platform.
-
work in progress a generalizable virtual reality training and Intelligent Tutor for additive manufacturing
2020 6th International Conference of the Immersive Learning Research Network (iLRN), 2020Co-Authors: Michael Mogessie, Sandra Devincent Wolf, Matheus Barbosa, Nicholas J Jones, Bruce M. MclarenAbstract:There is currently significant demand for training in how to use metals additive manufacturing (AM) machines. Such training is important not only for the technicians who run and maintain the machines, but also for engineers and strategic decision makers who need to support AM part fabrication. Furthermore, there are a variety of AM machines, each with different details to be learned and potential hazards to overcome, and it is difficult to train more than a handful of users at one time. To address these challenges, a prototype training system has been developed, the AM Training Tutor, which uses interactive virtual reality (VR) to train users on a specific AM machine – the EOS M290. To make the training technology more widely available and expand its use across a variety of different AM machines, efforts are underway to develop a modularized and generic version of the AM Training Tutor that can be customized with relatively little effort to train users to operate other AM machines. This work-in-progress paper details the progress to-date, challenges and proposed solutions with the aim to demonstrate how standalone VR-based training systems can be redesigned for relatively easy repurposing and generalization.
-
Opening the Door to Non-Programmers: Authoring Intelligent Tutor Behavior by Demonstration
2018Co-Authors: Kenneth R. Koedinger, Bruce M. Mclaren, Vincent Aleven, Matthew Hockenberry, Neil HeffernanAbstract:Intelligent Tutoring systems are quite difficult and time intensive to develop. In this paper, we describe a method and set of software tools that ease the process of cognitive task analysis and Tutor development by allowing the author to demonstrate, instead of programming, the behavior of an Intelligent Tutor. We focus on the subset of our tools that allow authors to create “Pseudo Tutors” that exhibit the behavior of Intelligent Tutors without requiring AI programming. Authors build user interfaces by direct manipulation and then use a Behavior Recorder tool to demonstrate alternative correct and incorrect actions. The resulting behavior graph is annotated with instructional messages and knowledge labels. We present some preliminary evidence of the effectiveness of this approach, both in terms of reduced development time and learning outcome. Pseudo Tutors have now been built for economics, analytic logic, mathematics, and language learning. Our data supports an estimate of about 25:1 ratio of development time to instruction time for Pseudo Tutors, which compares favorably to the 200:1 estimate for Intelligent Tutors, though we acknowledge and discuss limitations of such estimates
-
Toward Cognitive Tutoring in a Collaborative Web-Based Environment
2018Co-Authors: Bruce M. Mclaren, Kenneth R. Koedinger, Mike Schnieder, Andreas Harrer, Lars BollenAbstract:While Intelligent Tutoring has been applied to collaborative learning environments, it has met with little success so far because of the complexity involved in adding a Tutoring component to a collaborative environment. We propose to tackle this problem by using Cognitive Tutors as the basis for our approach and by applying a technique we call Bootstrapping Novice Data (BND). The BND approach involves feeding student log files from a problem-solving tool into Tutor development software to create the beginnings of a Tutor for the tool. We describe an initial implementation of our approach in which Cool Modes, a collaborative software tool, is integrated with the Behavior Recorder, Tutor-authoring software that supports development by demonstration. We show how our initial implementation provides a foundation for an Intelligent Tutor for collaboration. We also discuss some of the challenges ahead
-
example tracing Tutors Intelligent Tutor development for non programmers
International Journal of Artificial Intelligence in Education, 2016Co-Authors: Vincent Aleven, Bruce M. Mclaren, Jonathan Sewall, Martin Van Velsen, Octav Popescu, Sandra Demi, Michael A Ringenberg, Kenneth R. KoedingerAbstract:In 2009, we reported on a new Intelligent Tutoring Systems (ITS) technology, example-tracing Tutors, that can be built without programming using the Cognitive Tutor Authoring Tools (CTAT). Creating example-tracing Tutors was shown to be 4–8 times as cost-effective as estimates for ITS development from the literature. Since 2009, CTAT and its associated learning management system, the Tutorshop, have been extended and have been used for both research and real-world instruction. As evidence that example-tracing Tutors are an effective and mature ITS paradigm, CTAT-built Tutors have been used by approximately 44,000 students and account for 40 % of the data sets in DataShop, a large open repository for educational technology data sets. We review 18 example-tracing Tutors built since 2009, which have been shown to be effective in helping students learn in real educational settings, often with large pre/post effect sizes. These Tutors support a variety of pedagogical approaches, beyond step-based problem solving, including collaborative learning, educational games, and guided invention activities. CTAT and other ITS authoring tools illustrate that non-programmer approaches to building ITS are viable and useful and will likely play a key role in making ITS widespread.
-
improving students help seeking skills using metacognitive feedback in an Intelligent Tutoring system
Learning and Instruction, 2011Co-Authors: Ido Roll, Bruce M. Mclaren, Vincent Aleven, Kenneth R. KoedingerAbstract:The present research investigated whether immediate metacognitive feedback on students’ help-seeking errors can help students acquire better help-seeking skills. The Help Tutor, an Intelligent Tutor agent for help seeking, was integrated into a commercial Tutoring system for geometry, the Geometry Cognitive Tutor. Study 1, with 58 students, found that the real-time assessment of students’ help-seeking behavior correlated with other independent measures of help seeking, and that the Help Tutor improved students’ help-seeking behavior while learning Geometry with the Geometry Cognitive Tutor. Study 2, with 67 students, evaluated more elaborated support that included, in addition to the Help Tutor, also help-seeking instruction and support for self-assessment. The study replicated the effect found in Study 1. It was also found that the improved help-seeking skills transferred to learning new domain-level content during the month following the intervention, while the helpseeking support was no longer in effect. Implications for metacognitive Tutoring are discussed. 2010 Elsevier Ltd. All rights reserved.
Mehak Maniktala - One of the best experts on this subject based on the ideXlab platform.
-
Correction to: Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor
International Journal of Artificial Intelligence in Education, 2020Co-Authors: Mehak Maniktala, Christa Cody, Tiffany Barnes, Min ChiAbstract:A Correction to this paper has been published: https://doi.org/10.1007/s40593-020-00232-0
-
Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor
International Journal of Artificial Intelligence in Education, 2020Co-Authors: Mehak Maniktala, Christa Cody, Tiffany Barnes, Min ChiAbstract:Within Intelligent Tutoring systems, considerable research has investigated hints, including how to generate data-driven hints, what hint content to present, and when to provide hints for optimal learning outcomes. However, less attention has been paid to how hints are presented. In this paper, we propose a new hint delivery mechanism called “Assertions” for providing unsolicited hints in a data-driven Intelligent Tutor. Assertions are partially-worked example steps designed to appear within a student workspace, and in the same format as student-derived steps, to show students a possible subgoal leading to the solution. We hypothesized that Assertions can help address the well-known hint avoidance problem. In systems that only provide hints upon request, hint avoidance results in students not receiving hints when they are needed. Our unsolicited Assertions do not seek to improve student help-seeking, but rather seek to ensure students receive the help they need. We contrast Assertions with Messages, text-based, unsolicited hints that appear after student inactivity. Our results show that Assertions significantly increase unsolicited hint usage compared to Messages. Further, they show a significant aptitude-treatment interaction between Assertions and prior proficiency, with Assertions leading students with low prior proficiency to generate shorter (more efficient) posttest solutions faster. We also present a clustering analysis that shows patterns of productive persistence among students with low prior knowledge when the Tutor provides unsolicited help in the form of Assertions. Overall, this work provides encouraging evidence that hint presentation can significantly impact how students use them and using Assertions can be an effective way to address help avoidance.
Ryan S Baker - One of the best experts on this subject based on the ideXlab platform.
-
generalizing automated detection of the robustness of student learning in an Intelligent Tutor for genetics
Journal of Educational Psychology, 2013Co-Authors: Ryan S Baker, Albert Corbett, Sujith M GowdaAbstract:Recently, there has been growing emphasis on supporting robust learning within Intelligent Tutoring systems, assessed by measures such as transfer to related skills, preparation for future learning, and longer term retention. It has been shown that different pedagogical strategies promote robust learning to different degrees. However, the student modeling methods embedded within Intelligent Tutoring systems remain focused on assessing basic skill learning rather than robust learning. Recent work has proposed models, developed using educational data mining, that infer whether students are acquiring learning that transfers to related skills, and prepares the student for future learning (PFL). In this earlier work, evidence was presented that these models achieve superior prediction of robust learning to what can be achieved by traditional methods for student modeling. However, using these models to drive intervention by educational software depends on evidence that these models remain effective within new populations. To this end, we analyze the degree to which these detectors remain accurate for an entirely new population of high school students. We find limited evidence of degradation for transfer. More degradation is seen for PFL. This degradation appears to occur in part because it is generally more difficult to infer this construct within the new population.
-
detecting carelessness through contextual estimation of slip probabilities among students using an Intelligent Tutor for mathematics
Artificial Intelligence in Education, 2011Co-Authors: Maria Ofelia Clarissa San Z Pedro, Ryan S Baker, Ma Mercedes T RodrigoAbstract:A student is said to have committed a careless error when a student's answer is wrong despite the fact that he or she knows the answer (Clements, 1982). In this paper, educational data mining techniques are used to analyze log files produced by a cognitive Tutor for Scatterplots to derive a model and detector for carelessness. Bayesian Knowledge Tracing and its variant, the Contextual-Slip-and-Guess Estimation, are used to model and predict carelessness behavior in the Scatterplot Tutor. The study examines as well the robustness of this detector to a major difference in the Tutor's interface, namely the presence or absence of an embodied conversational agent, as well as robustness to data from a different school setting (USA versus Philippines).
-
contextual slip and prediction of student performance after use of an Intelligent Tutor
International Conference on User Modeling Adaptation and Personalization, 2010Co-Authors: Ryan S Baker, Angela Z Wagner, Albert T. Corbett, Sujith M Gowda, Benjamin A Maclaren, Linda R Kauffman, Aaron P Mitchell, Stephen GiguereAbstract:Intelligent Tutoring systems that utilize Bayesian Knowledge Tracing have achieved the ability to accurately predict student performance not only within the Intelligent Tutoring system, but on paper post-tests outside of the system Recent work has suggested that contextual estimation of student guessing and slipping leads to better prediction within the Tutoring software (Baker, Corbett, & Aleven, 2008a, 2008b) However, it is not yet clear whether this new variant on knowledge tracing is effective at predicting the latent student knowledge that leads to successful post-test performance In this paper, we compare the Contextual-Guess-and-Slip variant on Bayesian Knowledge Tracing to classical four-parameter Bayesian Knowledge Tracing and the Individual Difference Weights variant of Bayesian Knowledge Tracing (Corbett & Anderson, 1995), investigating how well each model variant predicts post-test performance We also test other ways to utilize contextual estimation of slipping within the Tutor in post-test prediction, and discuss hypotheses for why slipping during Tutor use is a significant predictor of post-test performance, even after Bayesian Knowledge Tracing estimates are controlled for.
-
differences between Intelligent Tutor lessons and the choice to go off task
Educational Data Mining, 2009Co-Authors: Ryan S BakerAbstract:Recent research has suggested that differences between Intelligent tu tor lessons predict a large amount of the variance in the prevalence of gaming the system (4). Within this paper, we investigate whether such differences also predict how much students choose to go off-task, and if so, which differences predict how much off-task behavior will occur. We utilize an enumeration of the differences between Intelligent Tutor lessons, the Cognitive Tutor Lesson Variation Space 1.1 (CTLVS1.1), to identify 79 differences between Tutor lessons, within 20 lessons from an Intelligent Tutoring system for Algebra. We utilize a machine-learned detector of off-task behavior to predict 58 students' off-task behavior within that Tutor, in each lesson. Surprisingly, the best model predicting off-task behavior from lesson features contains only one feature: lessons that involve equation-solving. We discuss possible explanations for this finding, and further studies that could shed light on this relationship.
-
labeling student behavior faster and more precisely with text replays
Educational Data Mining, 2008Co-Authors: Ryan S Baker, Adriana M J B De CarvalhoAbstract:We present text replays, a method for generating labels that can be used to train classifiers of student behavior. We use this method to label data as to whether students are gaming the system, within 20 Intelligent Tutor units on Algebra. Text replays are 2-6 times faster per label than previous methods for generating labels, such as quantitative field observations and screen replays; however, being able to generate classifiers on retrospective data at the coder’s convenience (rather than being dependent on visits to schools) makes this method about 40 times faster than quantitative field observations. Text replays also give precise predictions of student behavior at multiple grain-sizes, allowing the use of both hierarchical classifiers such as Latent Response Models (LRMs), and non-hierarchical classifiers such as Decision Trees. Training using text replay data appears to lead to better classifiers: LRMs trained using text replay data achieve higher correlation and A' than LRMs trained using quantitative field observations; Decision Trees are more precise than LRMs at identifying exactly when the behavior occurs.