NotesFAQContact Us
Collection
Advanced
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1040768
Record Type: Journal
Publication Date: 2013-Nov
Pages: 21
Abstractor: As Provided
Reference Count: 54
ISBN: N/A
ISSN: ISSN-1560-4292
Towards Automatically Detecting Whether Student Learning Is Shallow
Gowda, Sujith M.; Baker, Ryan S.; Corbett, Albert T.; Rossi, Lisa M.
International Journal of Artificial Intelligence in Education, v23 n1-4 p50-70 Nov 2013
Recent research has extended student modeling to infer not just whether a student knows a skill or set of skills, but also whether the student has achieved robust learning--learning that enables the student to transfer their knowledge and prepares them for future learning (PFL). However, a student may fail to have robust learning in two fashions: they may have no learning, or they may have shallow learning (learning that applies only to the current skill, and does not support transfer or PFL). Within this paper, we present automated detectors which identify shallow learners, who are likely to need different intervention than students who have not yet learned at all. These detectors are developed using K* machine learned models, with data from college students learning introductory genetics from an intelligent tutoring system.
Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A