Using Machine Learning to Detect SMART Model Cognitive Operations in Mathematical Problem-Solving Process

Autor/inn/en	Zhang, Jiayi; Andres, Juliana Ma. Alexandra L.; Hutt, Stephen; Baker, Ryan S.; Ocumpaugh, Jaclyn; Nasiar, Nidhi; Mills, Caitlin; Brooks, Jamiella; Sethuaman, Sheela; Young, Tyron
Titel	Using Machine Learning to Detect SMART Model Cognitive Operations in Mathematical Problem-Solving Process
Quelle	In: Journal of Educational Data Mining, 14 (2022) 3, S.76-108 (33 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Hutt, Stephen) ORCID (Ocumpaugh, Jaclyn) ORCID (Mills, Caitlin) ORCID (Brooks, Jamiella) ORCID (Young, Tyron)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Problem Solving; Mathematics Instruction; Learning Management Systems; Learning Analytics; Scaffolding (Teaching Technique); Metacognition; Attention Control; Protocol Analysis; Computer Mediated Communication; Models; Generalization; Reliability; Cognitive Processes; Learning Strategies; Questionnaires; Algorithms; Grade 6; Grade 7; Motivated Strategies for Learning Questionnaire + Suchen Sie Ihr Suchwort? Problemlösen; Mathematics lessons; Mathematikunterricht; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Aufmerksamkeitstest; Computerkonferenz; Analogiemodell; Reliabilität; Cognitive process; Kognitiver Prozess; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Fragebogen; Algorithm; Algorithmus; School year 06; 6. Schuljahr; Schuljahr 06; School year 07; 7. Schuljahr; Schuljahr 07
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 (Winne, 2017), 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, to achieve better scalability, we operationalized indicators of SMART operations and developed automated detectors using machine learning. We analyzed students' 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. Due to the rarity in data for one of the seven SRL indicators operationalized, we built six models to reflect students' use of four SMART operations. These models are found to be reliable and generalizable, with AUC ROCs ranging from 0.76-0.89. When applied to the full test set, these detectors are relatively robust to algorithmic bias, performing well across different student populations and with no consistent bias against a specific group of students. (As Provided).
Anmerkungen	International Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: https://jedm.educationaldatamining.org/index.php/JEDM
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01