Closing the Loop: Automated Data-Driven Cognitive Model Discoveries Lead to Improved Instruction and Learning Gains

Autor/inn/en	Liu, Ran; Koedinger, Kenneth R.
Titel	Closing the Loop: Automated Data-Driven Cognitive Model Discoveries Lead to Improved Instruction and Learning Gains
Quelle	In: Journal of Educational Data Mining, 9 (2017) 1, S.25-41 (17 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Educational Technology; Technology Uses in Education; Data Collection; Data Analysis; Program Evaluation; Cognitive Processes; Problem Solving; Models; Intelligent Tutoring Systems; Geometry; Teaching Methods; Factor Analysis; Grade 10; Instructional Design; Mathematics Instruction; High School Students; Randomized Controlled Trials; Pretests Posttests; Statistical Analysis; Scores; Pennsylvania (Pittsburgh) + Suchen Sie Ihr Suchwort? Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Data capture; Datensammlung; Auswertung; Programme evaluation; Programmevaluation; Cognitive process; Kognitiver Prozess; Problemlösen; Analogiemodell; Intelligentes Tutorsystem; Geometrie; Teaching method; Lehrmethode; Unterrichtsmethode; Faktorenanalyse; Lesson concept; Lessonplan; Unterrichtsentwurf; Mathematics lessons; Mathematikunterricht; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Statistische Analyse
Abstract	As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it yields better student learning when applied. Such an evaluation has been referred to as "closing the loop," as it completes the cycle of system design, deployment, data analysis, and discovery leading back to design. Here, we present an instance of closing the loop on an automated cognitive modeling improvement discovered by Learning Factors Analysis (Cen, Koedinger, & Junker, 2006). We discuss our findings from a process in which we interpret the automated improvements yielded by the best-fitting cognitive model, validate the interpretation on novel data, use it to make changes to classroom-deployed educational technology, and show that the changes lead to significant learning gains relative to a control condition. (As Provided).
Anmerkungen	International Working Group on Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://www.educationaldatamining.org/JEDM/index.php/JEDM/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01