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Koedinger, Kenneth R.; Aleven, Vincent – International Journal of Artificial Intelligence in Education, 2016
Our 1997 article in "IJAIED" reported on a study that showed that a new algebra curriculum with an embedded intelligent tutoring system (the Algebra Cognitive Tutor) dramatically enhanced high-school students' learning. The main motivation for the study was to demonstrate that intelligent tutors that have cognitive science research…
Descriptors: Intelligent Tutoring Systems, Technology Uses in Education, Educational Technology, Algebra
Liu, Ran; Koedinger, Kenneth R. – International Educational Data Mining Society, 2015
A growing body of research suggests that accounting for student specific variability in educational data can improve modeling accuracy and may have implications for individualizing instruction. The Additive Factors Model (AFM), a logistic regression model used to fit educational data and discover/refine skill models of learning, contains a…
Descriptors: Models, Regression (Statistics), Learning, Classification
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Walker, Erin; Rummel, Nikol; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2014
Adaptive collaborative learning support (ACLS) involves collaborative learning environments that adapt their characteristics, and sometimes provide intelligent hints and feedback, to improve individual students' collaborative interactions. ACLS often involves a system that can automatically assess student dialogue, model effective and…
Descriptors: Algebra, Peer Teaching, Tutoring, Cooperative Learning
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Aleven, Vincent; Roll, Ido; McLaren, Bruce M.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2016
Help seeking is an important process in self-regulated learning (SRL). It may influence learning with intelligent tutoring systems (ITSs), because many ITSs provide help, often at the student's request. The Help Tutor was a tutor agent that gave in-context, real-time feedback on students' help-seeking behavior, as they were learning with an ITS.…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Help Seeking, Feedback (Response)
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Matsuda, Noboru; Cohen, William W.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2015
SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitive tutor by tutoring SimStudent on how to solve…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Simulation, Models
MacLellan, Christopher J.; Liu, Ran; Koedinger, Kenneth R. – International Educational Data Mining Society, 2015
Additive Factors Model (AFM) and Performance Factors Analysis (PFA) are two popular models of student learning that employ logistic regression to estimate parameters and predict performance. This is in contrast to Bayesian Knowledge Tracing (BKT) which uses a Hidden Markov Model formalism. While all three models tend to make similar predictions,…
Descriptors: Factor Analysis, Regression (Statistics), Knowledge Level, Markov Processes
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Pavlik, Philip I., Jr.; Yudelson, Michael; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2015
Efforts to improve instructional task design often make reference to the mental structures, such as "schemas" (e.g., Gick & Holyoak, 1983) or "identical elements" (Thorndike & Woodworth, 1901), that are common to both the instructional and target tasks. This component based (e.g., Singley & Anderson, 1989) approach…
Descriptors: Models, Measurement, Instructional Design, Transfer of Training
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Booth, Julie L.; Koedinger, Kenneth R. – British Journal of Educational Psychology, 2012
Background: High school and college students demonstrate a verbal, or textual, advantage whereby beginning algebra problems in story format are easier to solve than matched equations (Koedinger & Nathan, 2004). Adding diagrams to the stories may further facilitate solution (Hembree, 1992; Koedinger & Terao, 2002). However, diagrams may not be…
Descriptors: Student Problems, Problem Solving, Grade 8, Grade 6
Gobert, Janice D.; Koedinger, Kenneth R. – Society for Research on Educational Effectiveness, 2011
The National frameworks for science emphasize inquiry skills (NRC, 1996), however, in typical classroom practice, science learning often focuses on rote learning in part because science process skills are difficult to assess (Fadel, Honey, & Pasnick, 2007) and rote knowledge is prioritized on high-stakes tests. Short answer assessments of inquiry…
Descriptors: Performance Based Assessment, Predictive Validity, High Stakes Tests, Rote Learning
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Walker, Erin; Rummel, Nikol; Koedinger, Kenneth R. – International Journal of Computer-Supported Collaborative Learning, 2011
Adaptive collaborative learning support systems analyze student collaboration as it occurs and provide targeted assistance to the collaborators. Too little is known about how to design adaptive support to have a positive effect on interaction and learning. We investigated this problem in a reciprocal peer tutoring scenario, where two students take…
Descriptors: Cooperative Learning, Peer Teaching, Tutoring, Helping Relationship
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Li, Nan; Cohen, William W.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2013
The order of problems presented to students is an important variable that affects learning effectiveness. Previous studies have shown that solving problems in a blocked order, in which all problems of one type are completed before the student is switched to the next problem type, results in less effective performance than does solving the problems…
Descriptors: Teaching Methods, Teacher Effectiveness, Problem Solving, Problem Based Learning
Koedinger, Kenneth R.; McLaughlin, Elizabeth A.; Stamper, John C. – International Educational Data Mining Society, 2012
Student modeling plays a critical role in developing and improving instruction and instructional technologies. We present a technique for automated improvement of student models that leverages the DataShop repository, crowd sourcing, and a version of the Learning Factors Analysis algorithm. We demonstrate this method on eleven educational…
Descriptors: Educational Technology, Intelligent Tutoring Systems, Educational Improvement, Mathematics
Goldin, Ilya M.; Koedinger, Kenneth R.; Aleven, Vincent – International Educational Data Mining Society, 2012
Although ITSs are supposed to adapt to differences among learners, so far, little attention has been paid to how they might adapt to differences in how students learn from help. When students study with an Intelligent Tutoring System, they may receive multiple types of help, but may not comprehend and make use of this help in the same way. To…
Descriptors: Performance Factors, Intelligent Tutoring Systems, Individual Differences, Prediction
Koedinger, Kenneth R.; Corbett, Albert T.; Perfetti, Charles – Online Submission, 2012
Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of…
Descriptors: Cognitive Science, Theory Practice Relationship, Interdisciplinary Approach, Praxis
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Koedinger, Kenneth R.; Corbett, Albert T.; Perfetti, Charles – Cognitive Science, 2012
Despite the accumulation of substantial cognitive science research relevant to education, there remains confusion and controversy in the application of research to educational practice. In support of a more systematic approach, we describe the Knowledge-Learning-Instruction (KLI) framework. KLI promotes the emergence of instructional principles of…
Descriptors: Cognitive Science, Educational Research, Research and Development, Theory Practice Relationship
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