Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

Autor/inn/en	Kinnebrew, John S.; Segedy, James R.; Biswas, Gautam
Titel	Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments
Quelle	In: IEEE Transactions on Learning Technologies, 10 (2017) 2, S.140-153 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2015.2513387
Schlagwörter	Computer Assisted Instruction; Problem Solving; Learning; Student Behavior; Data Analysis; Case Studies; Models; Metacognition; Science Instruction; Grade 6; Tennessee + Suchen Sie Ihr Suchwort? Computer based training; Computerunterstützter Unterricht; Problemlösen; Lernen; Student behaviour; Schülerverhalten; Auswertung; Case study; Fallstudie; Case Study; Analogiemodell; Meta cognitive ability; Meta-cognition; Metakognitive Fähigkeit; Metakognition; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; School year 06; 6. Schuljahr; Schuljahr 06
Abstract	Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and the choice to adopt a variety of strategies and approaches to solving these problems. To help students overcome their difficulties and become effective learners and problem solvers, we have to develop methods that can track and interpret students' open-ended learning and problem-solving behaviors. The complexity of the problems and the open-ended nature of the solution processes pose considerable challenges to accurately interpret and evaluate student behaviors and performance as they work on the system. In this paper, we develop a framework that combines model-driven strategy detection with data-driven pattern discovery for analyzing students' learning activity data in open-ended environments. We present results from an in-depth case study of multiple activity patterns identified in data from the Betty's Brain learning environment. The results illustrate the benefits of combining model- and data-driven techniques to precisely characterize the learning behavior of students in an open-ended environment. (As Provided).
Anmerkungen	Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01