Current and future multimodal learning analytics data challenges
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Multimodal Learning Analytics (MMLA) captures, integrates and analyzes learning traces from different sources in order to obtain a more holistic understanding of the learning process, wherever it happens. MMLA leverages the increasingly widespread availability of diverse sensors, highfrequency data collection technologies and sophisticated machine learning and artificial intelligence techniques. The aim of this workshop is twofold: first, to expose participants to, and develop, different multimodal datasets that reflect how MMLA can bring new insights and opportunities to investigate complex learning processes and environments; second, to collaboratively identify a set of grand challenges for further MMLA research, built upon the foundations of previous workshops on the topic.
Original language | English |
---|---|
Title of host publication | LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference : Understanding, Informing and Improving Learning with Data |
Number of pages | 2 |
Publisher | ACM Association for Computing Machinery |
Publication date | 13 Mar 2017 |
Pages | 518-519 |
ISBN (Electronic) | 9781450348706 |
DOIs | |
Publication status | Published - 13 Mar 2017 |
Externally published | Yes |
Event | 7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada Duration: 13 Mar 2017 → 17 Mar 2017 |
Conference
Conference | 7th International Conference on Learning Analytics and Knowledge, LAK 2017 |
---|---|
Land | Canada |
By | Vancouver |
Periode | 13/03/2017 → 17/03/2017 |
Series | ACM International Conference Proceeding Series |
---|
- Challenges, Datasets, Multimodal learning analytics
Research areas
ID: 256267532