Data science in educational assessment
This article is the second of two articles in this special issue that were developed following further discussions of the Assessment Working Group at EDUsummIT 2013. The article extends our analysis of assessments of collaborative problem solving (CPS) to examine the significance of the data concern...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/75881 |
| _version_ | 1848763573519515648 |
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| author | Gibson, David Webb, Mary E |
| author_facet | Gibson, David Webb, Mary E |
| author_sort | Gibson, David |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This article is the second of two articles in this special issue that were developed following further discussions of the Assessment Working Group at EDUsummIT 2013. The article extends our analysis of assessments of collaborative problem solving (CPS) to examine the significance of the data concerning this complex assessment problem and then for educational assessment more broadly. We introduce four psychometric 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, we call for the restructuring of training of the next generation of researchers and psychometricians to specialize in data science in technology enabled assessments. |
| first_indexed | 2025-11-14T11:05:37Z |
| format | Journal Article |
| id | curtin-20.500.11937-75881 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:05:37Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-758812019-07-03T05:15:40Z Data science in educational assessment Gibson, David Webb, Mary E This article is the second of two articles in this special issue that were developed following further discussions of the Assessment Working Group at EDUsummIT 2013. The article extends our analysis of assessments of collaborative problem solving (CPS) to examine the significance of the data concerning this complex assessment problem and then for educational assessment more broadly. We introduce four psychometric 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, we call for the restructuring of training of the next generation of researchers and psychometricians to specialize in data science in technology enabled assessments. 2015 Journal Article http://hdl.handle.net/20.500.11937/75881 10.1007/s10639-015-9411-7 restricted |
| spellingShingle | Gibson, David Webb, Mary E Data science in educational assessment |
| title | Data science in educational assessment |
| title_full | Data science in educational assessment |
| title_fullStr | Data science in educational assessment |
| title_full_unstemmed | Data science in educational assessment |
| title_short | Data science in educational assessment |
| title_sort | data science in educational assessment |
| url | http://hdl.handle.net/20.500.11937/75881 |