From Predictive Models to Instructional Policies [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Rollinson, Joseph; Brunskill, Emma
Institution	International Educational Data Mining Society
Titel	From Predictive Models to Instructional Policies [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	Prediction; Models; Educational Policy; Intelligent Tutoring Systems; Accuracy; Academic Achievement; Teaching Methods; Measurement Techniques; Mastery Learning; Instructional Effectiveness; Mathematical Models; Comparative Analysis; Statistical Data; Data Analysis + Suchen Sie Ihr Suchwort? Vorhersage; Analogiemodell; Politics of education; Bildungspolitik; Intelligentes Tutorsystem; Schulleistung; Teaching method; Lehrmethode; Unterrichtsmethode; Messtechnik; Unterrichtserfolg; Mathematical model; Mathematisches Modell; Auswertung
Abstract	At their core, Intelligent Tutoring Systems consist of a student model and a policy. The student model captures the state of the student and the policy uses the student model to individualize instruction. Policies require different properties from the student model. For example, a mastery threshold policy requires the student model to have a way to quantify whether the student has mastered a skill. A large amount of work has been done on building student models that can predict student performance on the next question. In this paper, we leverage this prior work with a new when-to-stop policy that is compatible with any such predictive student model. Our results suggest that, when employed as part of our new predictive similarity policy, student models with similar predictive accuracies can suggest that substantially different amounts of practice are necessary. This suggests that predictive accuracy may not be a sufficient metric by itself when choosing which student model to use in intelligent tutoring systems. [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