Detecting SMART Model Cognitive Operations in Mathematical Problem-Solving Process

Jiayi Zhang, Juliana Ma Alexandra L. Andres, Stephen Hutt, Ryan S. Baker, Jaclyn Ocumpaugh, Caitlin Mills, Jamiella Brooks, Sheela Sethuraman, Tyron Young

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Self-regulated learning (SRL) is a critical component of mathematics problem solving. Students skilled in SRL are more likely to effectively set goals, search for information, and direct their attention and cognitive process so that they align their efforts with their objectives. An influential framework for SRL, the SMART model, proposes that five cognitive operations (i.e., searching, monitoring, assembling, rehearsing, and translating) play a key role in SRL. However, these categories encompass a wide range of behaviors, making measurement challenging – often involving observing individual students and recording their think-aloud activities or asking students to complete labor-intensive tagging activities as they work. In the current study, we develop machine-learned indicators of SMART operations, in order to achieve better scalability than other measurement approaches. We analyzed student’s textual responses and interaction data collected from a mathematical learning platform where students are asked to thoroughly explain their solutions and are scaffolded in communicating their problem-solving process to their peers and teachers. We built detectors of four indicators of SMART operations (namely, assembling and translating operations). Our detectors are found to be reliable and generalizable, with AUC ROCs ranging from .76-.89. When applied to the full test set, the detectors are robust against algorithmic bias, performing well across different student populations.

Original languageEnglish (US)
Title of host publicationProceedings of the 15th International Conference on Educational Data Mining, EDM 2022
PublisherInternational Educational Data Mining Society
ISBN (Electronic)9781733673631
DOIs
StatePublished - 2022
Externally publishedYes
Event15th International Conference on Educational Data Mining, EDM 2022 - Hybrid, Durham, United Kingdom
Duration: Jul 24 2022Jul 27 2022

Publication series

NameProceedings of the 15th International Conference on Educational Data Mining, EDM 2022

Conference

Conference15th International Conference on Educational Data Mining, EDM 2022
Country/TerritoryUnited Kingdom
CityHybrid, Durham
Period7/24/227/27/22

Bibliographical note

Publisher Copyright:
© 2022 Copyright is held by the author(s).

Keywords

  • automated detectors
  • self-regulated learning
  • SMART model

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