Exploratory learning analytics methods from three case studies
Brief outlines of exploratory analysis methods (analysis designed to develop hypotheses) from three research projects illustrate the size, scope, variety and increased resolution that are becoming increasingly available at the unit of analysis for research in the learning sciences. The tools and met...
| Main Authors: | , |
|---|---|
| Format: | Conference Paper |
| Published: |
2014
|
| Online Access: | http://hdl.handle.net/20.500.11937/70264 |
| _version_ | 1848762258589483008 |
|---|---|
| author | Gibson, David De Freitas, S. |
| author_facet | Gibson, David De Freitas, S. |
| author_sort | Gibson, David |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Brief outlines of exploratory analysis methods (analysis designed to develop hypotheses) from three research projects illustrate the size, scope, variety and increased resolution that are becoming increasingly available at the unit of analysis for research in the learning sciences. The tools and methods applied in these studies are briefly outlined, which enable researchers to deal with complexity in time and event structures involving complex data in learning analytics projects. In particular, the transformation of data involving both reduction methods and pattern aggregation into motifs were found to be crucial for data interpretation. The article describes data mining with a self-organizing map, involving unsupervised machine learning and symbolic regression and combining exploratory analysis methods to achieve causal explanations. |
| first_indexed | 2025-11-14T10:44:43Z |
| format | Conference Paper |
| id | curtin-20.500.11937-70264 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:44:43Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-702642018-08-08T04:44:13Z Exploratory learning analytics methods from three case studies Gibson, David De Freitas, S. Brief outlines of exploratory analysis methods (analysis designed to develop hypotheses) from three research projects illustrate the size, scope, variety and increased resolution that are becoming increasingly available at the unit of analysis for research in the learning sciences. The tools and methods applied in these studies are briefly outlined, which enable researchers to deal with complexity in time and event structures involving complex data in learning analytics projects. In particular, the transformation of data involving both reduction methods and pattern aggregation into motifs were found to be crucial for data interpretation. The article describes data mining with a self-organizing map, involving unsupervised machine learning and symbolic regression and combining exploratory analysis methods to achieve causal explanations. 2014 Conference Paper http://hdl.handle.net/20.500.11937/70264 restricted |
| spellingShingle | Gibson, David De Freitas, S. Exploratory learning analytics methods from three case studies |
| title | Exploratory learning analytics methods from three case studies |
| title_full | Exploratory learning analytics methods from three case studies |
| title_fullStr | Exploratory learning analytics methods from three case studies |
| title_full_unstemmed | Exploratory learning analytics methods from three case studies |
| title_short | Exploratory learning analytics methods from three case studies |
| title_sort | exploratory learning analytics methods from three case studies |
| url | http://hdl.handle.net/20.500.11937/70264 |