Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates high-fidelity synchronised multimodal recordings of small groups of learners interacting from diverse sensors that include computer vision, user generated content, and data from the learning objects (like physical computing components or laboratory equipment). We processed and extracted different aspects of the students' interactions to answer the following question: which features of student group work are good predictors of team success in open-ended tasks with physical computing? The answer to the question provides ways to automatically identify the students' performance during the learning activities.
Original language | English |
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Title of host publication | Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017 |
Editors | Ronghuai Huang, Radu Vasiu, Kinshuk, Demetrios G Sampson, Nian-Shing Chen, Maiga Chang |
Number of pages | 5 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Publication date | 3 Aug 2017 |
Pages | 269-273 |
Article number | 8001779 |
ISBN (Electronic) | 9781538638705 |
DOIs | |
Publication status | Published - 3 Aug 2017 |
Externally published | Yes |
Event | 17th IEEE International Conference on Advanced Learning Technologies, ICALT 2017 - Timisoara, Romania Duration: 3 Jul 2017 → 7 Jul 2017 |
Conference
Conference | 17th IEEE International Conference on Advanced Learning Technologies, ICALT 2017 |
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Land | Romania |
By | Timisoara |
Periode | 03/07/2017 → 07/07/2017 |
Series | Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017 |
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- collaborative learning, Multimodal learning analytics, practice-based learning
Research areas
ID: 256265814