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Help Avoidance: When Students Should Seek Help, and the Consequences of Failing to Do So


by Victoria Q. Almeda, Ryan S. J. D. Baker & Albert Corbett — 2017

Background: Across computer-based and traditional classroom settings, recent studies have identified motivational orientation, prior knowledge, self-regulation, and cognitive load as possible factors that affect help-seeking behaviors and their impact on learning. However, the question of whether there is an optimal point for determining when a student needs help has not been fully explored.

Purpose of Study: Using data from two modules of the Genetics Cognitive Tutor, the present study investigates this question by examining whether the relationship of help avoidance (failing to seek help when it is needed) and student learning is dependent on the student’s level of prior knowledge. We also investigate how the relationship between help avoidance and student learning is mediated by the amount of prior practice, or the number of attempts at a problem step.

Research Design: We obtained existing data from the use of the Genetics Cognitive Tutor. We conducted a series of correlational analyses to better understand the relationship between help avoidance and student learning. We correlated students’ proportions of help avoidance at different levels of knowledge with measures of robust learning. We also analyzed the relationship between students’ proportions of help avoidance and measures of robust learning, taking the amount of practice or the number of attempts at a problem step into account.

Results: Our findings suggest that, except at very high or very low knowledge, help avoidance is generally stably (negatively) related to robust learning outcomes. Our results also indicate that help avoidance is more strongly associated with learning outcomes early in the practice sequence, suggesting that students should be encouraged to seek help on problem-solving skills on the first problem, rather than waiting until later problems. Similarly, our results reveal that help avoidance is more negatively associated with learning outcomes on early attempts at a problem step than on later attempts, indicating that students should be encouraged to seek help on the first attempt if help is needed.

Conclusions: These findings represent a step toward understanding when students should seek help, with the potential of improving the design of metacognitive support within adaptive learning systems.



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Cite This Article as: Teachers College Record Volume 119 Number 3, 2017, p. 1-24
http://www.tcrecord.org ID Number: 21775, Date Accessed: 5/24/2017 12:19:23 AM

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About the Author
  • Victoria Almeda
    Teachers College, Columbia University
    E-mail Author
    VICTORIA Q. ALMEDA is a Ph.D. student in Cognitive Studies in Education at Teachers College, Columbia University. Her research interests include student engagement, Intelligent Tutoring Systems, and math learning. Her recent work includes “Classroom Activities and Off-Task Behavior in Elementary School Children,” in Proceedings of the Annual Meeting of the Cognitive Science Society, and “Clustering of Design Decisions in Classroom Visual Displays,” in Proceedings of the Fourth International Conference on Learning Analytics and Knowledge.
  • Ryan Baker
    Teachers College, Columbia University
    E-mail Author
    RYAN S. BAKER is an Associate Professor of Cognitive Studies and Program Coordinator for Learning Analytics at Teachers College, Columbia University. His research interests include student engagement, robust learning, metacognition, educational data mining, and learning analytics. His recent publications include “Modeling How Incoming Knowledge, Persistence, Affective States, and In-Game Progress Influence Student Learning from an Educational Game,” in Computers & Education, and “Cross-System Transfer of Machine Learned and Knowledge Engineered Models of Gaming the System,” in Proceedings of the 22nd International Conference on User Modeling, Adaptation, and Personalization.
  • Albert Corbett
    Carnegie Mellon University
    E-mail Author
    ALBERT CORBETT is an Associate Research Professor Emeritus in the Human-Computer Interaction Institute, Carnegie Mellon University. His principal research interest is cognitive models of problem solving, and he has conducted extensive empirical evaluations of students learning programming, mathematics, and genetics with intelligent computer tutors. He has recently published “A Cognitive Tutor for Genetics Problem Solving: Learning Gains and Student Modeling,” in Journal of Educational Computing Research.
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