| 1. |
- Spikol, Daniel, 1965-, et al.
(författare)
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Current and Future Multimodal Learning Analytics Data Challenges
- 2017
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Ingår i: Seventh International Learning Analytics & Knowledge Conference (LAK'17). - New York, NY, USA : ACM Digital Library. ; , s. 518-519
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Konferensbidrag (refereegranskat)abstract
- 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, high-frequency 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.
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| 2. |
- Spikol, Daniel, et al.
(författare)
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Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features
- 2017
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Ingår i: Proceedings 17th International Conference on Advanced Learning Technologies - ICALT 2017. - : IEEE. ; , s. 269-273
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Konferensbidrag (refereegranskat)abstract
- Abstract: 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.
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| 3. |
- Spikol, Daniel, et al.
(författare)
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Using Multimodal Learning Analytics to Identify Aspects of Collaboration in Project-Based Learning
- 2017
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Ingår i: Making aDifference: Prioritizing Equity and Access in CSCL, 12th International Conference onComputer Supported Collaborative Learning (CSCL). - : International Society of the Learning Sciences.. - 9780990355007 ; , s. 263-270
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Konferensbidrag (refereegranskat)abstract
- Collaborative learning activities are a key part of education and are part of many common teaching approaches including problem-based learning, inquiry-based learning, and project-based learning. However, in open-ended collaborative small group work where learners make unique solutions to tasks that involve robotics, electronics, programming, and design artefacts evidence on the effectiveness of using these learning activities are hard to find. The paper argues that multimodal learning analytics (MMLA) can offer novel methods that can generate unique information about what happens when students are engaged in collaborative, project-based learning activities. Through the use of multimodal learning analytics platform, we collected various streams of data, processed and extracted multimodal interactions to answer the following question: which features of MMLA are good predictors of collaborative problem-solving in open-ended tasks in project-based learning? Manual entered scores of CPS were regressed using machine-learning methods. The answer to the question provides potential ways to automatically identify aspects of collaboration in project-based learning.
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