An Illustration of the Use of Markov Decision Processes to Represent Student Growth (Learning). Research Report. ETS RR-07-40

Autor/in	Almond, Russell G.
Titel	An Illustration of the Use of Markov Decision Processes to Represent Student Growth (Learning). Research Report. ETS RR-07-40
Quelle	In: ETS Research Report Series, (2007), (68 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2330-8516
Schlagwörter	Markov Processes; Decision Making; Student Evaluation; Learning Processes; Academic Achievement; Computation; At Risk Students; Identification; Models; Cognitive Development; Simulation; Benchmarking; Reliability; Bayesian Statistics + Suchen Sie Ihr Suchwort? Markowscher Prozess; Decision-making; Entscheidungsfindung; Schulnote; Studentische Bewertung; Learning process; Lernprozess; Schulleistung; Identifikation; Identifizierung; Analogiemodell; Kognitive Entwicklung; Simulation program; Simulationsprogramm; Reliabilität
Abstract	Over the course of instruction, instructors generally collect a great deal of information about each student. Integrating that information intelligently requires models for how a student's proficiency changes over time. Armed with such models, instructors can "filter" the data--more accurately estimate the student's current proficiency levels--and "forecast" the student's future proficiency levels. The process of instructional planning can be described as a "partially observed Markov decision process" (POMDP). Recently developed computer algorithms can be used to help instructors create strategies for student achievement and identify at-risk students. Implementing this vision requires models for how instructional actions change student proficiencies. The "general action model" (also called the "bowtie model") separately models the factors contributing to the success or effectiveness of an action, proficiency growth when the action is successful, and proficiency growth when the action is unsuccessful. This class of models requires parameterization, and this paper presents two: a simple linear process model (suitable for continuous proficiencies) and a birth-and-death process model (for proficiency scales expressed as ordered categorical variables). Both models show how to take prerequisites and zones of proximal development into account. The filtering process is illustrated using a simple artificial example. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01