Abstract
Interest in analyses that probe the temporal aspects of learning continues to grow. The study of common and consequential sequences of events (such as learners accessing resources, interacting with other learners and engaging in self-regulatory activities) and how these are associated with learning outcomes, as well as the ways in which knowledge and skills grow or evolve over time are both core areas of interest. Learning analytics datasets are replete with fine-grained temporal data: click streams; chat logs; document edit histories (e.g. wikis, etherpads); motion tracking (e.g. eye-tracking, Microsoft Kinect), and so on. However, the emerging area of temporal analysis presents both technical and theoretical challenges in appropriating suitable techniques and interpreting results in the context of learning. The learning analytics community offers a productive focal ground for exploring and furthering efforts to address these challenges. This workshop, the fourth in a series on temporal analysis of learning, provides a focal point for analytics researchers to consider issues around and approaches to temporality in learning analytics.
Original language | English (US) |
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Title of host publication | Proceedings of the 5th International Conference on Learning Analytics and Knowledge, LAK 2015 |
Publisher | Association for Computing Machinery |
Pages | 388-389 |
Number of pages | 2 |
ISBN (Electronic) | 9781450334174 |
DOIs | |
State | Published - Mar 16 2015 |
Event | 5th International Conference on Learning Analytics and Knowledge, LAK 2015 - Poughkeepsie, United States Duration: Mar 16 2015 → Mar 20 2015 |
Publication series
Name | ACM International Conference Proceeding Series |
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Volume | 16-20-March-2015 |
Other
Other | 5th International Conference on Learning Analytics and Knowledge, LAK 2015 |
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Country/Territory | United States |
City | Poughkeepsie |
Period | 3/16/15 → 3/20/15 |
Bibliographical note
Publisher Copyright:© Copyright 2015 ACM.
Keywords
- CSCL
- Discourse Analytics
- Knowledge Building
- Learning Analytics
- Sequence Mining
- Temporality