Predicting Correctness of Problem Solving from Low-Level Log Data in Intelligent Tutoring Systems [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, Jul 1-3, 2009).

Autor/inn/en	Cetintas, Suleyman; Si, Luo; Xin, Yan Ping; Hord, Casey
Institution	International Working Group on Educational Data Mining
Titel	Predicting Correctness of Problem Solving from Low-Level Log Data in Intelligent Tutoring Systems [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	Programming; Evidence; Intelligent Tutoring Systems; Regression (Statistics); Prediction; Problem Solving; Artificial Intelligence; Elementary School Students; Mathematics Instruction; Grade 4; Special Needs Students; Federal Aid; Educational Experiments; Computer System Design; Computer Managed Instruction; Computer Software; Data; Information Retrieval; Data Analysis; Word Problems (Mathematics); Least Squares Statistics + Suchen Sie Ihr Suchwort? Programmierung; Evidenz; Intelligentes Tutorsystem; Regression; Regressionsanalyse; Vorhersage; Problemlösen; Künstliche Intelligenz; Mathematics lessons; Mathematikunterricht; School year 04; 4. Schuljahr; Schuljahr 04; Sonderpädagogischer Förderbedarf; Schulversuch; Computer-assisted instruction; Computerunterstützter Unterricht; Daten; Auswertung; Textaufgabe
Abstract	This paper proposes a learning based method that can automatically determine how likely a student is to give a correct answer to a problem in an intelligent tutoring system. Only log files that record students' actions with the system are used to train the model, therefore the modeling process doesn't require expert knowledge for identifying domain specific skills that are needed to solve the problem or students' possible solution methods etc. The model utilizes a set of performance features, problem features, time and mouse movement features and is compared to i) a model that utilizes performance and problem features, ii) a model that uses performance, problem and time features. In order to address data sparseness problem, a robust Ridge Regression algorithm is designed to estimate model parameters. An extensive set of experiment results demonstrate the power of using multiple types of evidence as well as the robust Ridge Regression algorithm. (Contains 3 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