Detecting SMART Model Cognitive Operations in Mathematical Problem-Solving Process [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).

Autor/inn/en	Zhang, Jiayi; Andres, Juliana Ma. Alexandra L.; Hutt, Stephen; Baker, Ryan S.; Ocumpaugh, Jaclyn; Mills, Caitlin; Brooks, Jamiella; Sethuraman, Sheela; Young, Tyron
Titel	Detecting SMART Model Cognitive Operations in Mathematical Problem-Solving Process [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (15th, Durham, United Kingdom, Jul 24-27, 2022).
Quelle	(2022), (11 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Mathematics Instruction; Teaching Methods; Problem Solving; Metacognition; Learning Strategies; Guidelines; Protocol Analysis; Models; Learning Analytics; Integrated Learning Systems; Scaffolding (Teaching Technique); Peer Relationship; Measurement; Middle School Students + Suchen Sie Ihr Suchwort? Mathematics lessons; Mathematikunterricht; Teaching method; Lehrmethode; Unterrichtsmethode; Problemlösen; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Richtlinien; Analogiemodell; Peer-Beziehungen; Messverfahren; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin
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 0.76-0.89. When applied to the full test set, the detectors are robust against algorithmic bias, performing well across different student populations. [For the full proceedings, see ED623995.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01