Challenges of big data in educational assessment
This paper briefly discusses four measurement challenges of data science or 'big data' in educational assessments that are enabled by technology: 1. Dealing with change over time via time-based data. 2. How a digital performance space's relationships interact with learner actions, com...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/39798 |
| _version_ | 1848755691084316672 |
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| author | Gibson, D. Webb, M. Ifenthaler, Dirk |
| author_facet | Gibson, D. Webb, M. Ifenthaler, Dirk |
| author_sort | Gibson, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper briefly discusses four measurement challenges of data science or 'big data' in educational assessments that are enabled by technology: 1. Dealing with change over time via time-based data. 2. How a digital performance space's relationships interact with learner actions, communications and products. 3. How layers of interpretation are formed from translations of atomistic data into meaningful larger units suitable for making inferences about what someone knows and can do. 4. How to represent the dynamics of interactions between and among learners who are being assessed by their interactions with each other as well as with digital resources and agents in digital performance spaces. Because of the movement from paper-based tests to online learning, and in order to make progress on these challenges, the authors advocate the restructuring of training of the next generation of researchers and psychometricians to specialize in data science in technology enabled assessments. This call to action stemmed from discussions at EDUsummIT 2013, which will be published in depth in a special issue of Education and Information Technologies. |
| first_indexed | 2025-11-14T09:00:19Z |
| format | Conference Paper |
| id | curtin-20.500.11937-39798 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:00:19Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-397982017-01-30T14:37:06Z Challenges of big data in educational assessment Gibson, D. Webb, M. Ifenthaler, Dirk This paper briefly discusses four measurement challenges of data science or 'big data' in educational assessments that are enabled by technology: 1. Dealing with change over time via time-based data. 2. How a digital performance space's relationships interact with learner actions, communications and products. 3. How layers of interpretation are formed from translations of atomistic data into meaningful larger units suitable for making inferences about what someone knows and can do. 4. How to represent the dynamics of interactions between and among learners who are being assessed by their interactions with each other as well as with digital resources and agents in digital performance spaces. Because of the movement from paper-based tests to online learning, and in order to make progress on these challenges, the authors advocate the restructuring of training of the next generation of researchers and psychometricians to specialize in data science in technology enabled assessments. This call to action stemmed from discussions at EDUsummIT 2013, which will be published in depth in a special issue of Education and Information Technologies. 2015 Conference Paper http://hdl.handle.net/20.500.11937/39798 restricted |
| spellingShingle | Gibson, D. Webb, M. Ifenthaler, Dirk Challenges of big data in educational assessment |
| title | Challenges of big data in educational assessment |
| title_full | Challenges of big data in educational assessment |
| title_fullStr | Challenges of big data in educational assessment |
| title_full_unstemmed | Challenges of big data in educational assessment |
| title_short | Challenges of big data in educational assessment |
| title_sort | challenges of big data in educational assessment |
| url | http://hdl.handle.net/20.500.11937/39798 |