Predicting Individualized Learner Models across Tutor Lessons [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (11th, Raleigh, NC, Jul 16-20, 2018).

Autor/inn/en	Eagle, Michael; Corbett, Albert; Stamper, John; Mclaren, Bruce
Titel	Predicting Individualized Learner Models across Tutor Lessons [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (11th, Raleigh, NC, Jul 16-20, 2018).
Quelle	(2018), (5 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Individualized Instruction; Models; Data Analysis; Knowledge Level; Prediction; Intelligent Tutoring Systems; Individual Differences; Undergraduate Students; Genetics; Biology; Science Instruction; Goodness of Fit; Bayesian Statistics + Suchen Sie Ihr Suchwort? Individualisierender Unterricht; Analogiemodell; Auswertung; Wissensbasis; Vorhersage; Intelligentes Tutorsystem; Individueller Unterschied; Humangenetik; Biologie; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht
Abstract	In this work we use prior to tutor-session data to generate an individualized student knowledge model. Intelligent learning environments use student models to individualize curriculum sequencing and help messages. Researchers decompose the learning tasks into sets of Knowledge Components (KCs) that represent individual units of knowledge; the student model estimates a parameters for each KC, but not for each student. Using existing performance data to adjust parameters for each individual student improves model fit, and leads to different practice recommendations. However, in order to be implemented in a live system we need to have a method to estimate the student parameters using only the student's prior activities. In this work, we use data collected from student reading, prior tutor lessons, to predict individualized difference weights for parameters of a Bayesian Knowledge Tracing (BKT) variant. We find that best-fitting student parameters trained on previous lessons do not directly transfer to new lessons; however, we can effectively predict the student parameters for the new lesson by using features derived from prior lessons, and prior to tutor text-reading transaction data. [For the full proceedings, see ED593090.] (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