| 1. |
- Cukurova, Mutlu, et al.
(author)
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Diagnosing collaboration in practice-based learning : Equality and Intra-individual variability of physical interactivity
- 2017
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In: Data Driven Approaches in Digital Education. - Cham : Springer. ; , s. 30-42
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Conference paper (peer-reviewed)abstract
- Collaborative problem solving (CPS), as a teaching and learning approach, is considered to have the potential to improve some of the most important skills to prepare students for their future. CPS often differs in its nature, practice, and learning outcomes from other kinds of peer learning approaches, including peer tutoring and cooperation; and it is important to establish what identifies collaboration in problem-solving situations. The identification of indicators of collaboration is a challenging task. However, students physical interactivity can hold clues of such indicators. In this paper, we investigate two non-verbal indexes of student physical interactivity to interpret collaboration in practice-based learning environments: equality and intra-individual variability. Our data was generated from twelve groups of three Engineering students working on open-ended tasks using a learning analytics system. The results show that high collaboration groups have member students who present high and equal amounts of physical interactivity and low and equal amounts of intra-individual variability.
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| 2. |
- Healion, Daniel, et al.
(author)
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Designing Spaces for Collaboration in Practice-Based Learning
- 2017
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In: CSCL’17 : The 12th International Conference on Computer Supported Collaborative Learning. - : International Society of the Learning Sciences.. ; , s. 565-568
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Conference paper (peer-reviewed)abstract
- In order to support equity and access in collaborative learning, it is important to understand the nature of collaborative learning itself. One approach is to look at the physical aspects of how students collaborate while engaged in open-ended group-work during Practice-Based Learning (PBL) activities. By analysing how students and teachers move and interact in relation to each other, the space they are in and the objects within it, we can gain a greater understanding of the physical nature of collaborative group-work. This understanding can help us to create a learning environment that intrinsically but unobtrusively supports access by all user profiles who seek to engage with it, thus promoting equity of engagement and participation. Using the example of the design of a Learning Analytics System (LAS) and the educational furniture in which it is implemented, we will show how the physical design of a CSCL implementation can support increased collaboration.
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| 3. |
- Healion, Donal, et al.
(author)
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Tracing physical movement during practice-based learning through Multimodal Learning Analytics
- 2017
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In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference. - New York, NY, USA : ACM Digital Library. ; , s. 588-598
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Conference paper (peer-reviewed)abstract
- In this paper, we pose the question, can the tracking and analysis of the physical movements of students and teachers within a Practice-Based Learning (PBL) environment reveal information about the learning process that is relevant and informative to Learning Analytics (LA) implementations? Using the example of trials conducted in the design of a LA system, we aim to show how the analysis of physical movement from a macro level can help to enrich our understanding of what is happening in the classroom. The results suggest that Multimodal Learning Analytics (MMLA) could be used to generate valuable information about the human factors of the collaborative learning process and we propose how this information could assist in the provision of relevant supports for small group work. More research is needed to confirm the initial findings with larger sample sizes and refine the data capture and analysis methodology to allow automation.
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| 4. |
- Luckin, Rose, et al.
(author)
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How Do We Unleash AIEd at Scale to Benefit All Teachers and Learners?
- 2017
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In: Artificial Intelligence in Education. - : Springer. ; , s. 665-667
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Conference paper (peer-reviewed)abstract
- The application of artificial intelligence to education (AIEd) has been the subject of academic research for more than 30 years, a period during which much technical progress has been made, but few in-roads into mainstream education have been achieved. With the upsurge of interest in AI in general and increasingly in AI for education in particular, what role could and should the AIED research community play?
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| 5. |
- Spikol, Daniel, 1965-, et al.
(author)
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Current and Future Multimodal Learning Analytics Data Challenges
- 2017
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In: Seventh International Learning Analytics & Knowledge Conference (LAK'17). - New York, NY, USA : ACM Digital Library. ; , s. 518-519
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Conference paper (peer-reviewed)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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| 6. |
- Spikol, Daniel, et al.
(author)
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Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features
- 2017
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In: Proceedings 17th International Conference on Advanced Learning Technologies - ICALT 2017. - : IEEE. ; , s. 269-273
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Conference paper (peer-reviewed)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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| 7. |
- Spikol, Daniel, et al.
(author)
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Using Multimodal Learning Analytics to Identify Aspects of Collaboration in Project-Based Learning
- 2017
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In: 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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Conference paper (peer-reviewed)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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