Students' Verbalized Metacognition during Computerized Learning

Students' Verbalized Metacognition during Computerized Learning
Nigel Bosch, Yingbin Zhang, Luc Paquette, Ryan Baker, Jaclyn Ocumpaugh, Gautam Biswas

CHI '21: The 2021 ACM CHI Conference on Human Factors in Computing Systems
Session: Systems for Learning

Abstract
Students in computerized learning environments often direct their own learning processes, which requires metacognitive awareness of what should be learned next. We investigated a novel method of measuring verbalized metacognition by applying natural language processing (NLP) to transcripts of interviews conducted in a classroom with 99 middle school students who were using a computerized learning environment. We iteratively adapted the NLP method for the linguistic characteristics of these interviews, then applied it to study three research questions regarding the relationships between verbalized metacognition and measures of 1) learning, 2) confusion, and 3) metacognitive problem-solving strategies. Verbalized metacognition was not directly related to learning, but was related to confusion and metacognitive problem-solving strategies. Results also suggested that interviews themselves may improve learning by encouraging metacognition. We discuss implications for designing computerized environments that support self-regulated learning through metacognition.

DOI:: https://doi.org/10.1145/3411764.3445809
WEB:: https://chi2021.acm.org/

Pre-recorded Presentations for the ACM CHI Virtual Conference on Human Factors in Computing Systems, May 8-13, 2021