Can Sequence Mining Augment Our Understanding of Metacomprehension During Multimedia Learning? Evidence From Eye Movements

• In Event: 29.028 - Novel Methods for Assessing and Enhancing Metacognitive Monitoring

Abstract

Detecting, tracking, and inferring metacognitive monitoring processes during multimedia learning from traditional self-report measures pose several conceptual, theoretical, methodological, and analytical challenges for traditional models of metacomprehension. We argue that current models of metacomprehension can be augmented by (1) converging online trace data (e.g., eye tracking) and (2) using novel methods (e.g., sequential pattern mining) to understand successful metacomprehension with multimedia materials. This study investigated how sequential pattern mining can augment our understanding of metacomprehension during multimedia learning. Eye-movement data from 32 undergraduates revealed important sequences in students’ fixations on specific components of content (i.e., text and graph) indicative of metacognitive monitoring and control processes. Implications for using eye movements to study metacomprehension and designing intelligent user interfaces are discussed.

Authors

• Nicholas Vincent Mudrick, North Carolina State University

• Joseph Grafsgaard, North Carolina State University

• Roger Azevedo, North Carolina State University