An Extended Learner Modeling Method to Assess Students' Learning Behaviors [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017).

Autor/inn/en	Dong, Yi; Biswas, Gautam
Titel	An Extended Learner Modeling Method to Assess Students' Learning Behaviors [Konferenzbericht] Paper presented at the International Conference on Educational Data Mining (EDM) (10th, Wuhan, China, Jun 25-28, 2017).
Quelle	(2017), (4 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Student Behavior; Models; Monte Carlo Methods; Learning Processes; Middle School Students; Grade 6; Computer Software; Educational Technology; Technology Uses in Education; Scaffolding (Teaching Technique); STEM Education + Suchen Sie Ihr Suchwort? Student behaviour; Schülerverhalten; Analogiemodell; Monte-Carlo-Methode; Learning process; Lernprozess; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; School year 06; 6. Schuljahr; Schuljahr 06; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; STEM
Abstract	This paper discusses a novel approach for developing more refined and accurate learner models from student data collected from Open Ended Learning Environments (OELEs). OELEs provide students choice in how they go about constructing solutions to problems, and students exhibit a variety of learning behaviors in such environments. Building accurate models from limited amount of student data is difficult; to address this we develop a methodology that uses Monte Carlo Tree Search methods to boost the initial set of student action sequences in such a way that we can learn more accurate models of students' learning behaviors. We use an HMM representation to model students' learning behaviors and demonstrate the effectiveness of our approach by running a case study on data collected from 98 students, who worked with the Betty's Brain system for four days. The results have interesting implications for learner modeling and its applications to adaptive scaffolding of students' learning behaviors and strategies as they learn from OELEs. [For the full proceedings, see ED596512.] (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