Tracking with (Un)certainty

Autor/inn/en	Hofman, Abe D.; Brinkhuis, Matthieu J. S.; Bolsinova, Maria; Klaiber, Jonathan; Maris, Gunter; van der Maas, Han L. J.
Titel	Tracking with (Un)certainty
Quelle	In: Journal of Intelligence, 8 (2020), Artikel 10 (15 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Hofman, Abe D.) ORCID (Brinkhuis, Matthieu J. S.)
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2079-3200
Schlagwörter	Teaching Methods; Computer Assisted Instruction; Difficulty Level; Individualized Instruction; Learning Analytics; Integrated Learning Systems; Error Patterns; Responses; Statistical Inference; Cognitive Ability; Testing; Comparative Analysis; Competition; Educational Games; Computer Games; Probability; Visual Aids; Item Response Theory; Mathematics Instruction + Suchen Sie Ihr Suchwort? Teaching method; Lehrmethode; Unterrichtsmethode; Computer based training; Computerunterstützter Unterricht; Schwierigkeitsgrad; Individualisierender Unterricht; Fehlertyp; Inferential statistics; Schließende Statistik; Denkfähigkeit; Testdurchführung; Testen; Wettkampf; Educational game; Lernspiel; Computer game; Computerspiel; Computerspiele; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Anschauungsmaterial; Item-Response-Theorie; Mathematics lessons; Mathematikunterricht
Abstract	One of the highest ambitions in educational technology is the move towards personalized learning. To this end, computerized adaptive learning (CAL) systems are developed. A popular method to track the development of student ability and item difficulty, in CAL systems, is the Elo Rating System (ERS). The ERS allows for dynamic model parameters by updating key parameters after every response. However, drawbacks of the ERS are that it does not provide standard errors and that it results in rating variance inflation. We identify three statistical issues responsible for both of these drawbacks. To solve these issues we introduce a new tracking system based on urns, where every person and item is represented by an urn filled with a combination of green and red marbles. Urns are updated, by an exchange of marbles after each response, such that the proportions of green marbles represent estimates of person ability or item difficulty. A main advantage of this approach is that the standard errors are known, hence the method allows for statistical inference, such as testing for learning effects. We highlight features of the Urnings algorithm and compare it to the popular ERS in a simulation study and in an empirical data example from a large-scale CAL application. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01