A Contextualized, Differential Sequence Mining Method to Derive Students' Learning Behavior Patterns

Autor/inn/en	Kinnebrew, John S.; Loretz, Kirk M.; Biswas, Gautam
Titel	A Contextualized, Differential Sequence Mining Method to Derive Students' Learning Behavior Patterns
Quelle	In: Journal of Educational Data Mining, 5 (2013) 1, S.190-219 (30 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Data Analysis; Middle School Students; Information Retrieval; Student Behavior; Learning Processes; Metacognition; Pacing; Individualized Instruction; Questionnaires; Protocol Analysis; Technology Uses in Education; Cognitive Development; Educational Technology; Artificial Intelligence; Grade 8; Feedback (Response); Tennessee + Suchen Sie Ihr Suchwort? Auswertung; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Student behaviour; Schülerverhalten; Learning process; Lernprozess; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Lerntempo; Individualisierender Unterricht; Fragebogen; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Kognitive Entwicklung; Unterrichtsmedien; Künstliche Intelligenz; School year 08; 8. Schuljahr; Schuljahr 08
Abstract	Computer-based learning environments can produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs a novel combination of sequence mining techniques to identify deferentially frequent patterns between groups of students (e.g., experimental versus control conditions or high versus low performers). We extend this technique by contextualizing the sequence mining with information about the student's performance over the course of the learning interactions. Specifically, we employ a piecewise linear segmentation algorithm in concert with the differential sequence mining technique to identify and compare segments of students' productive and unproductive learning behaviors. We present the results from the application of this exploratory data mining methodology to learning interaction trace data gathered during a recent middle school class study with the Betty's Brain learning environment. These results illustrate the potential of this methodology in identifying learning behavior patterns relevant to the investigation of metacognition and strategy use. (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