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  1. International Journal of Artificial Intelligence in Education
  2. International Journal of Artificial Intelligence in Education : Volume 23
  3. International Journal of Artificial Intelligence in Education : Volume 23, Issue 1-4, November 2013
  4. Towards Automatically Detecting Whether Student Learning is Shallow
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International Journal of Artificial Intelligence in Education : Volume 27
International Journal of Artificial Intelligence in Education : Volume 26
International Journal of Artificial Intelligence in Education : Volume 25
International Journal of Artificial Intelligence in Education : Volume 24
International Journal of Artificial Intelligence in Education : Volume 23
International Journal of Artificial Intelligence in Education : Volume 23, Issue 1-4, November 2013
Studying the Effect of a Competitive Game Show in a Learning by Teaching Environment
Statistical Techniques to Explore the Quality of Constraints in Constraint-Based Modeling Environments
Towards Automatically Detecting Whether Student Learning is Shallow
Problem Order Implications for Learning
Understanding and Predicting Student Self-Regulated Learning Strategies in Game-Based Learning Environments
Modelling and Optimizing Mathematics Learning in Children
Understanding Attention to Adaptive Hints in Educational Games: An Eye-Tracking Study

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Towards Automatically Detecting Whether Student Learning is Shallow

Content Provider Springer Nature Link
Author Gowda, Sujith M. Rossi, Lisa M. Corbett, Albert T. Baker, Ryan S.
Copyright Year 2013
Abstract Recent research has extended student modeling to infer not just whether a student knows a skill or set of skills, but also whether the student has achieved robust learning—learning that enables the student to transfer their knowledge and prepares them for future learning (PFL). However, a student may fail to have robust learning in two fashions: they may have no learning, or they may have shallow learning (learning that applies only to the current skill, and does not support transfer or PFL). Within this paper, we present automated detectors which identify shallow learners, who are likely to need different intervention than students who have not yet learned at all. These detectors are developed using K* machine learned models, with data from college students learning introductory genetics from an intelligent tutoring system.
Starting Page 50
Ending Page 70
Page Count 21
File Format PDF
ISSN 15604292
Journal International Journal of Artificial Intelligence in Education
Volume Number 23
Issue Number 1-4
e-ISSN 15604306
Language English
Publisher Springer New York
Publisher Date 2013-10-29
Publisher Institution International AIED Society
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Educational data mining Student modeling Educational Technology User Interfaces and Human Computer Interaction Artificial Intelligence (incl. Robotics) Computers and Education Robust learning Intelligent tutoring system
Content Type Text
Resource Type Article
Subject Education Computational Theory and Mathematics E-learning
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