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MacLellan, Christopher J.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2022
Intelligent tutoring systems are effective for improving students' learning outcomes (Pane et al. 2013; Koedinger and Anderson, "International Journal of Artificial Intelligence in Education," 8, 1-14, 1997; Bowen et al. "Journal of Policy Analysis and Management," 1, 94-111 2013). However, constructing tutoring systems that…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Models, Instructional Design
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Liu, Ran; Koedinger, Kenneth R. – Journal of Educational Data Mining, 2017
As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it…
Descriptors: Educational Technology, Technology Uses in Education, Data Collection, Data Analysis
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Wiese, Eliane S.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2017
This paper proposes "grounded feedback" as a way to provide implicit verification when students are working with a novel representation. In grounded feedback, students' responses are in the target, to-be-learned representation, and those responses are reflected in a more-accessible linked representation that is intrinsic to the domain.…
Descriptors: Instructional Design, Feedback (Response), Evaluation Criteria, Instructional Effectiveness
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Koedinger, Kenneth R.; McLaughlin, Elizabeth A. – International Educational Data Mining Society, 2016
Many educational data mining studies have explored methods for discovering cognitive models and have emphasized improving prediction accuracy. Too few studies have "closed the loop" by applying discovered models toward improving instruction and testing whether proposed improvements achieve higher student outcomes. We claim that such…
Descriptors: Educational Research, Data Collection, Task Analysis, Cognitive Processes
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Rosé, Carolyn P.; McLaughlin, Elizabeth A.; Liu, Ran; Koedinger, Kenneth R. – British Journal of Educational Technology, 2019
Using data to understand learning and improve education has great promise. However, the promise will not be achieved simply by AI and Machine Learning researchers developing innovative models that more accurately predict labeled data. As AI advances, modeling techniques and the models they produce are getting increasingly complex, often involving…
Descriptors: Discovery Learning, Man Machine Systems, Artificial Intelligence, Models
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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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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
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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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Booth, Julie L.; Lange, Karin E.; Koedinger, Kenneth R.; Newton, Kristie J. – Learning and Instruction, 2013
In a series of two "in vivo" experiments, we examine whether correct and incorrect examples with prompts for self-explanation can be effective for improving students' conceptual understanding and procedural skill in Algebra when combined with guided practice. In Experiment 1, students working with the Algebra I Cognitive Tutor were randomly…
Descriptors: Computer Assisted Instruction, Computer Software, Instructional Design, Educational Experiments
Pavlik, Philip I. Jr.; Cen, Hao; Koedinger, Kenneth R. – Online Submission, 2009
This paper describes a novel method to create a quantitative model of an educational content domain of related practice item-types using learning curves. By using a pairwise test to search for the relationships between learning curves for these item-types, we show how the test results in a set of pairwise transfer relationships that can be…
Descriptors: Instructional Design, Test Results, Models, Pattern Recognition
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Aleven, Vincent; McLaren, Bruce M.; Sewall, Jonathan; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2009
The Cognitive Tutor Authoring Tools (CTAT) support creation of a novel type of tutors called example-tracing tutors. Unlike other types of ITSs (e.g., model-tracing tutors, constraint-based tutors), example-tracing tutors evaluate student behavior by flexibly comparing it against generalized examples of problem-solving behavior. Example-tracing…
Descriptors: Feedback (Response), Student Behavior, Intelligent Tutoring Systems, Problem Solving
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Rittle-Johnson, Bethany; Koedinger, Kenneth R. – Cognition and Instruction, 2005
We present a methodology for designing better learning environments. In Phase 1, 6th-grade students' (n = 223) prior knowledge was assessed using a difficulty factors assessment (DFA). The assessment revealed that scaffolds designed to elicit contextual, conceptual, or procedural knowledge each improved students' ability to add and subtract…
Descriptors: Prior Learning, Intervention, Mathematics Instruction, Problem Solving
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Mathan, Santosh A.; Koedinger, Kenneth R. – Educational Psychologist, 2005
This article explores 2 important aspects of metacognition: (a) how students monitor their ongoing performance to detect and correct errors and (b) how students reflect on those errors to learn from them. Although many instructional theories have advocated providing students with immediate feedback on errors, some researchers have argued that…
Descriptors: Metacognition, Instructional Design, Feedback, Teaching Methods