Making use of data for assessments: harnessing analytics and data science
The increased availability of vast and highly varied amounts of data from learners, teachers, learning environments, and administrative systems within educational settings is overwhelming. The focus of this chapter is on how data with a large number of records, of widely differing datatypes, and arr...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Book Chapter |
| Published: |
Springer
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/75870 |
| _version_ | 1848763570143100928 |
|---|---|
| author | Ifenthaler, D. Greiff, S. Gibson, David |
| author2 | Voogt, J |
| author_facet | Voogt, J Ifenthaler, D. Greiff, S. Gibson, David |
| author_sort | Ifenthaler, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The increased availability of vast and highly varied amounts of data from learners, teachers, learning environments, and administrative systems within educational settings is overwhelming. The focus of this chapter is on how data with a large number of records, of widely differing datatypes, and arriving rapidly from multiple sources can be harnessed for meaningful assessments and supporting learners in a wide variety of learning situations. Distinct features of analytics-driven assessments may include self-assessments, peer assessments, and semantic rich and personalized feedback as well as adaptive prompts for reflection. The chapter concludes with future directions in the broad area of analytics-driven assessments for teachers and educational researchers. |
| first_indexed | 2025-11-14T11:05:33Z |
| format | Book Chapter |
| id | curtin-20.500.11937-75870 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:05:33Z |
| publishDate | 2018 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-758702019-07-03T07:12:30Z Making use of data for assessments: harnessing analytics and data science Ifenthaler, D. Greiff, S. Gibson, David Voogt, J Knezek, G Christensen, R Lai, K-W The increased availability of vast and highly varied amounts of data from learners, teachers, learning environments, and administrative systems within educational settings is overwhelming. The focus of this chapter is on how data with a large number of records, of widely differing datatypes, and arriving rapidly from multiple sources can be harnessed for meaningful assessments and supporting learners in a wide variety of learning situations. Distinct features of analytics-driven assessments may include self-assessments, peer assessments, and semantic rich and personalized feedback as well as adaptive prompts for reflection. The chapter concludes with future directions in the broad area of analytics-driven assessments for teachers and educational researchers. 2018 Book Chapter http://hdl.handle.net/20.500.11937/75870 10.1007/978-3-319-53803-7_41-1 Springer restricted |
| spellingShingle | Ifenthaler, D. Greiff, S. Gibson, David Making use of data for assessments: harnessing analytics and data science |
| title | Making use of data for assessments: harnessing analytics and data science |
| title_full | Making use of data for assessments: harnessing analytics and data science |
| title_fullStr | Making use of data for assessments: harnessing analytics and data science |
| title_full_unstemmed | Making use of data for assessments: harnessing analytics and data science |
| title_short | Making use of data for assessments: harnessing analytics and data science |
| title_sort | making use of data for assessments: harnessing analytics and data science |
| url | http://hdl.handle.net/20.500.11937/75870 |