International Journal of Artificial Intelligence in Education
, Volume 23, Issue 1, pp 7193
Problem Order Implications for Learning
 Nan LiAffiliated withComputer Science Department, Carnegie Mellon University Email author
 , William W. CohenAffiliated withMachine Learning Department, Carnegie Mellon University
 , Kenneth R. KoedingerAffiliated withHuman Computer Interaction Institute, Carnegie Mellon University
Abstract
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 in an interleaved order. However, we have no precise understanding of the reason for this effect. In addition to existing theoretical results, we use a machinelearning agent that learns cognitive skills from examples and problem solving experience, SimStudent, to provide a computational model of the problem order question. We conduct a controlled simulation study in three different math and science domains (i.e., fraction addition, equation solving and stoichiometry), where SimStudent is tutored by automatic tutors given problems that have been used to teach human students. We compare two problem orders: the blocked problem order, and the interleaved problem order. The results show that the interleaved problem order yields as effective or more effective learning in all three domains, because the interleaved problem order provides more or better opportunities for error detection and correction to the learning agent. Examination of the agent’s performance shows that learning when to apply a skill benefits more from interleaved problem orders, and suggests that learning how to apply a skill benefits more from blocked problem orders.
Keywords
Learning transfer Learner modeling Interleaved problem order Blocked problem order Title
 Problem Order Implications for Learning
 Journal

International Journal of Artificial Intelligence in Education
Volume 23, Issue 14 , pp 7193
 Cover Date
 201311
 DOI
 10.1007/s4059301300055
 Print ISSN
 15604292
 Online ISSN
 15604306
 Publisher
 Springer New York
 Additional Links
 Topics
 Keywords

 Learning transfer
 Learner modeling
 Interleaved problem order
 Blocked problem order
 Authors

 Nan Li ^{(1)}
 William W. Cohen ^{(2)}
 Kenneth R. Koedinger ^{(3)}
 Author Affiliations

 1. Computer Science Department, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA
 2. Machine Learning Department, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA
 3. Human Computer Interaction Institute, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA