Calibrated Self-Assessment [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016).

Autor/inn/en	Labutov, Igor; Studer, Christoph
Titel	Calibrated Self-Assessment [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (9th, Raleigh, NC, Jun 29-Jul 2, 2016).
Quelle	(2016), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Self Evaluation (Individuals); Student Evaluation; Item Response Theory; Grading; Incentives + Suchen Sie Ihr Suchwort? Schulnote; Studentische Bewertung; Item-Response-Theorie; Notengebung; Anreiz
Abstract	Peer-grading is widely believed to be an inexpensive and scalable way to assess students in large classroom settings. In this paper, we propose "calibrated self-grading" as a more efficient alternative to peer grading. For self-grading, students assign themselves a grade that they think they deserve via an incentive-compatible mechanism that elicits maximally truthful judgements of performance. We show that the students' self-evaluation scores obtained via this mechanism can be used to perform classic item response theory (IRT) analysis. In order to obtain unbiased estimates of the IRT parameters, we show that the self-assigned grades can be calibrated with a minimum amount of input from instructors or domain experts. We demonstrate the effectiveness of the proposed calibrated self-grading approach via simulations and experiments on Amazon's Mechanical Turk. [For the full proceedings, see ED592609.] (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