Unsupervised MDP Value Selection for Automating ITS Capabilities [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, Jul 1-3, 2009).

Autor/inn/en	Stamper, John; Barnes, Tiffany
Institution	International Working Group on Educational Data Mining
Titel	Unsupervised MDP Value Selection for Automating ITS Capabilities [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, Jul 1-3, 2009).
Quelle	(2009), (10 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Data Analysis; Computer Assisted Instruction; Intelligent Tutoring Systems; Markov Processes; Logical Thinking; Automation; Comparative Analysis; College Instruction; College Students; North Carolina + Suchen Sie Ihr Suchwort? Auswertung; Computer based training; Computerunterstützter Unterricht; Intelligentes Tutorsystem; Markowscher Prozess; Hochschullehre; Collegestudent
Abstract	We seek to simplify the creation of intelligent tutors by using student data acquired from standard computer aided instruction (CAI) in conjunction with educational data mining methods to automatically generate adaptive hints. In our previous work, we have automatically generated hints for logic tutoring by constructing a Markov Decision Process (MDP) that holds and rates historical student work for automatic selection of the best prior cases for hint generation. This method has promise for domain-independent use, but requires that correct solutions be assigned high positive values by the CAI or an expert. In this research we propose a novel method for assigning prior values to student work that depends only on frequency of occurrence for the component steps, and compare how these values impact automatic hint generation when compared to our MDP approach. Our results show that the utility metric outperforms a classic MDP solution in selecting hints in logic. We believe this method will be particularly useful for automatic hint generation for ill-defined domains. (Contains 2 figures and 4 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.] (As Provided).
Anmerkungen	International Working Group on Educational Data Mining. Available from: 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	2017/4/10