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Roll, Ido; Baker, Ryan S. J. d.; Aleven, Vincent; Koedinger, Kenneth R. – Journal of the Learning Sciences, 2014
Seeking the right level of help at the right time can support learning. However, in the context of online problem-solving environments, it is still not entirely clear which help-seeking strategies are desired. We use fine-grained data from 38 high school students who worked with the Geometry Cognitive Tutor for 2 months to better understand the…
Descriptors: Help Seeking, Comparative Analysis, Behavior Patterns, Intelligent Tutoring Systems
Lipschultz, Michael; Litman, Diane; Katz, Sandra; Albacete, Patricia; Jordan, Pamela – Grantee Submission, 2014
Post-problem reflective tutorial dialogues between human tutors and students are examined to predict when the tutor changed the level of abstraction from the student's preceding turn (i.e., used more general terms or more specific terms); such changes correlate with learning. Prior work examined lexical changes in abstraction. In this work, we…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Semantics, Abstract Reasoning
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Baker, Ryan S.; Hershkovitz, Arnon; Rossi, Lisa M.; Goldstein, Adam B.; Gowda, Sujith M. – Journal of the Learning Sciences, 2013
We present a new method for analyzing a student's learning over time for a specific skill: analysis of the graph of the student's moment-by-moment learning over time. Moment-by-moment learning is calculated using a data-mined model that assesses the probability that a student learned a skill or concept at a specific time during learning (Baker,…
Descriptors: Learning Processes, Intelligent Tutoring Systems, Probability, Skill Development
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Aleven, Vincent; Roll, Ido; McLaren, Bruce M.; Koedinger, Kenneth R. – Educational Psychologist, 2010
Assessment of students' self-regulated learning (SRL) requires a method for evaluating whether observed actions are appropriate acts of self-regulation in theEv specific learning context in which they occur. We review research that has resulted in an automated method for context-sensitive assessment of a specific SRL strategy, help seeking while…
Descriptors: Feedback (Response), Help Seeking, Intelligent Tutoring Systems, Tutoring
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Hausmann, Robert G. M.; VanLehn, Kurt – International Journal of Artificial Intelligence in Education, 2010
Self-explaining is a domain-independent learning strategy that generally leads to a robust understanding of the domain material. However, there are two potential explanations for its effectiveness. First, self-explanation generates additional "content" that does not exist in the instructional materials. Second, when compared to…
Descriptors: Instructional Design, Intelligent Tutoring Systems, College Students, Predictor Variables