Direct Estimation of the Minimum RSS Value for Training Bayesian Knowledge Tracing Parameters [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).

Autor/inn/en	Martori, Francesc; Cuadros, Jordi; González-Sabaté, Lucinio
Institution	International Educational Data Mining Society
Titel	Direct Estimation of the Minimum RSS Value for Training Bayesian Knowledge Tracing Parameters [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015).
Quelle	(2015), (4 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Bayesian Statistics; Inferences; Prediction; Accuracy; Student Evaluation; Knowledge Level; Mathematical Models; Evaluation Methods; Componential Analysis; Correlation; Mathematics; Computation + Suchen Sie Ihr Suchwort? Inference; Inferenz; Vorhersage; Schulnote; Studentische Bewertung; Wissensbasis; Mathematical model; Mathematisches Modell; Korrelation; Mathematik
Abstract	Student modeling can help guide the behavior of a cognitive tutor system and provide insight to researchers on understanding how students learn. In this context, Bayesian Knowledge Tracing (BKT) is one of the most popular knowledge inference models due to its predictive accuracy, interpretability and ability to infer student knowledge. However, the most popular methods for training the parameters of BKT have some problems, such as identifiability, local minima, degenerate parameters and computational cost during fitting. In this paper we address some of the issues of one of these training models, BKT Brute Force. Instead of finding the parameter values that provide the lowest Residual Sum of Squares (RSS), we estimate this minimum RSS value from some a priori known values of the skill. From there we perform some preliminary analysis to improve our knowledge of the relationship between the RSS, from BKT-BF, and the four BKT parameters. [For complete proceedings, see ED560503.] (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