Cognitive Load as a Significant Problem in Learning Programming: Briana Morrison’s Dissertation Proposal

Briana Morrison is defending her proposal today.  One chapter of her work is based on her ICER 2015 paper that won the Chairs Award for best paper (see post here). Good luck, Briana!

Title: Replicating Experiments from Educational Psychology to Develop Insights into Computing Education: Cognitive Load as a Significant Problem in Learning Programming

Briana Morrison
Ph.D. student
Human Centered Computing
College of Computing
Georgia Institute of Technology

Date: Wednesday, November 11, 2015
Time: 2 PM to 4 PM EDT
Location: TSRB 223

Committee
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Dr. Mark Guzdial, School of Interactive Computing (advisor)
Dr. Betsy DiSalvo, School of Interactive Computing
Dr. Wendy Newstetter, School of Interactive Computing
Dr. Richard Catrambone, School of Psychology
Dr. Beth Simon, Jacobs School of Engineering at University of California San Diego and Principal Teaching and Learning Specialist, Coursera

Abstract
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Students often find learning to program difficult. This may be because the concepts are inherently difficult due to the fact that the elements of learning to program are highly interconnected. Instructors may be able to lower the complexity of learning to program by designing instructional materials that use educational psychology principles.

The overarching goal of this research is to gain more understanding and insight into the optimal conditions under which learning programming can be successful which is defined as students being able to apply their acquired knowledge and skills in new or familiar problem-solving situations. Cognitive load theory (CLT), and its associated effects, describe the role of the learner’s memory during the learning process. By minimizing undesirable loads within the instructional materials the learner’s memory can hold more relevant information, thereby improving the effectiveness of the learning process.

This proposal uses cognitive load theory to improve learning in programming.  First an instrument for measuring cognitive load components within introductory programming was developed and initially validated. We have explored reducing the cognitive load by changing the modality in which students receive the learning material. This had no effect on novices’ retention of knowledge or their ability to transfer knowledge. We then attempted to reduce the cognitive load by adding subgoal labels to the instructional material. This had some effect on the learning gains under some conditions. Students who learned using subgoal labels demonstrated higher learning gains than the other conditions on the programming assessment task. We also explored using a low cognitive load assessment task, a Parsons problem, to measure learning gains. This low cognitive load assessment task proved more sensitive than the open ended programming assessment tasks in capturing student learning. Students who were given subgoal labels regardless of context transfer condition out performed those in the other conditions.

In my final, proposed study I change how we teach a programming construct through its format and content in order to reduce cognitive load. The changed construct is presumed to be a more natural cognitive fit for students based on previous research.