Conditional Subspace Clustering of Skill Mastery: Identifying Skills that Separate Students [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, Jul 1-3, 2009).

Autor/inn/en	Nugent, Rebecca; Ayers, Elizabeth; Dean, Nema
Institution	International Working Group on Educational Data Mining
Titel	Conditional Subspace Clustering of Skill Mastery: Identifying Skills that Separate Students [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, Jul 1-3, 2009).
Quelle	(2009), (10 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Data Analysis; Students; Skills; Cluster Grouping; Matrices; Computation; Automation; Identification; Profiles; Intelligent Tutoring Systems; Mathematics Instruction + Suchen Sie Ihr Suchwort? Auswertung; Student; Schüler; Schülerin; Studentin; Skill; Fertigkeit; Eingruppierung; Matrizenrechnung; Identifikation; Identifizierung; Charakterisierung; Profilanalyse; Intelligentes Tutorsystem; Mathematics lessons; Mathematikunterricht
Abstract	In educational research, a fundamental goal is identifying which skills students have mastered, which skills they have not, and which skills they are in the process of mastering. As the number of examinees, items, and skills increases, the estimation of even simple cognitive diagnosis models becomes difficult. We adopt a faster, simpler approach: cluster a "capability matrix" estimating each student's individual skill knowledge to generate skill set profile clusters of students. We complement this approach with the introduction of an automatic subspace clustering method that first identifies skills on which students are well-separated prior to clustering smaller subspaces. This method also allows teachers to dictate the size and separation of the clusters, if need be, for practical reasons. We demonstrate the feasibility and scalability of our method on several simulated datasets and illustrate the difficulties inherent in real data using a subset of online mathematics tutor data. (Contains 3 figures and 2 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.] (As Provided).
Anmerkungen	International Working Group on Educational Data Mining. Available from: 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	2017/4/10