Development and validation of a learning analytics framework: Two case studies using support vector machines
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 im...
| Main Authors: | , |
|---|---|
| Format: | Journal Article |
| Published: |
Kluwer Academic Publishers
2014
|
| Online Access: | http://hdl.handle.net/20.500.11937/26154 |
| _version_ | 1848751904218152960 |
|---|---|
| author | Ifenthaler, Dirk Widanapathirana, C. |
| author_facet | Ifenthaler, Dirk Widanapathirana, C. |
| author_sort | Ifenthaler, Dirk |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:00:08Z |
| format | Journal Article |
| id | curtin-20.500.11937-26154 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:00:08Z |
| publishDate | 2014 |
| publisher | Kluwer Academic Publishers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-261542017-09-13T15:25:10Z Development and validation of a learning analytics framework: Two case studies using support vector machines Ifenthaler, Dirk Widanapathirana, C. 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. 2014 Journal Article http://hdl.handle.net/20.500.11937/26154 10.1007/s10758-014-9226-4 Kluwer Academic Publishers restricted |
| spellingShingle | Ifenthaler, Dirk Widanapathirana, C. Development and validation of a learning analytics framework: Two case studies using support vector machines |
| title | Development and validation of a learning analytics framework: Two case studies using support vector machines |
| title_full | Development and validation of a learning analytics framework: Two case studies using support vector machines |
| title_fullStr | Development and validation of a learning analytics framework: Two case studies using support vector machines |
| title_full_unstemmed | Development and validation of a learning analytics framework: Two case studies using support vector machines |
| title_short | Development and validation of a learning analytics framework: Two case studies using support vector machines |
| title_sort | development and validation of a learning analytics framework: two case studies using support vector machines |
| url | http://hdl.handle.net/20.500.11937/26154 |