NotesFAQContact Us
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1037510
Record Type: Journal
Publication Date: 2014-Jul
Pages: 20
Abstractor: As Provided
Reference Count: 52
ISSN: ISSN-2211-1662
Development and Validation of a Learning Analytics Framework: Two Case Studies Using Support Vector Machines
Ifenthaler, Dirk; Widanapathirana, Chathuranga
Technology, Knowledge and Learning, v19 n1-2 p221-240 Jul 2014
Interest in collecting and mining large sets of educational data on student background and performance to conduct research on learning and instruction has developed as an area generally referred to as learning analytics. Higher education leaders are recognizing the value of learning analytics for improving not only learning and teaching but also the entire educational arena. However, theoretical concepts and empirical evidence need to be generated within the fast evolving field of learning analytics. The purpose of the two reported cases studies is to identify alternative approaches to data analysis and to determine the validity and accuracy of a learning analytics framework and its corresponding student and learning profiles. The findings indicate that educational data for learning analytics is context specific and variables carry different meanings and can have different implications across educational institutions and area of studies. Benefits, concerns, and challenges of learning analytics are critically reflected, indicating that learning analytics frameworks need to be sensitive to idiosyncrasies of the educational institution and its stakeholders.
Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A