Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Ye, Cheng; Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam
Institution	International Educational Data Mining Society
Titel	Learning Behavior Characterization with Multi-Feature, Hierarchical Activity Sequences [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (4 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Learning Activities; Learning Processes; Data Collection; Student Behavior; Sequential Approach; Middle School Students; Secondary School Science; Science Instruction; Problem Solving; Classification; Comparative Analysis; Sequential Learning; Information Retrieval; Mathematical Concepts; Data Analysis + Suchen Sie Ihr Suchwort? Lernaktivität; Learning process; Lernprozess; Data capture; Datensammlung; Student behaviour; Schülerverhalten; Schrittfolge; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Problemlösen; Classification system; Klassifikation; Klassifikationssystem; Didaktische Sequenzierung; Lernsequenz; Auswertung
Abstract	This paper discusses Multi-Feature Hierarchical Sequential Pattern Mining, MFH-SPAM, a novel algorithm that efficiently extracts patterns from students' learning activity sequences. This algorithm extends an existing sequential pattern mining algorithm by dynamically selecting the level of specificity for hierarchically-defined features individually for each pattern. Consequently, MFH-SPAM operates on a larger space of patterns in the activity sequences. In this paper, we employ a differential version of MFH-SPAM to extract a small set of patterns that best differentiate students with different learning behavior profiles in the Betty's Brain system. Our results illustrate that: (1) MFH-SPAM identifies important patterns missed by traditional sequence mining approaches; and (2) the differential patterns provide additional information for characterizing learning behaviors. This has implications for developing targeted and adaptive scaffolding in open-ended learning environments. [For complete proceedings, see ED560503.] (As Provided).
Anmerkungen	International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01