Combining Techniques to Refine Item to Skills Q-Matrices with a Partition Tree [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Desmarais, Michel C.; Xu, Peng; Beheshti, Behzad
Institution	International Educational Data Mining Society
Titel	Combining Techniques to Refine Item to Skills Q-Matrices with a Partition Tree [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Matrices; Test Items; Skills; Expertise; Methods; Data Analysis; Accuracy + Suchen Sie Ihr Suchwort? Matrizenrechnung; Test content; Testaufgabe; Skill; Fertigkeit; Expert appraisal; Method; Methode; Auswertung
Abstract	The problem of mapping items to skills is gaining interest with the emergence of recent techniques that can use data for both defining this mapping, and for refining mappings given by experts. We investigate the problem of refining mapping from an expert by combining the output of different techniques. The combination is based on a partition tree that combines the suggested refinements of three known techniques from the literature. Each technique is given as input a Q-matrix, that maps items to skills, and student test outcome data, and outputs a modified Q-matrix that constitutes suggested improvements. We test the accuracy of the partition tree combination techniques over both synthetic and real data. The results over synthetic data show a high improvement over the best single technique with a 86% error reduction on average for four different Q-matrices. For real data, the error reduction is 55%. In addition to the substantial error reduction, the partition tree refinements provide a much more stable performance than any single technique. These results suggest that the partition tree is a valuable refinement combination approach that can effectively take advantage of the complementarity of the Q-matrix refinement techniques. It brings the goal of using a data driven approach to refine the item to skill mapping closer to real applications, although challenges remain and are discussed. [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