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2nd Crossmmla : Multimodal learning analytics across physical and digital spaces
- 2018
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Proceedings (redaktörskap) (refereegranskat)abstract
- Students’ learning is ubiquitous. It happens wherever the learner is rather than being constrained to a specific physical or digital learning space (e.g. the classroom or the institutional LMS respectively). A critical question is: how to integrate and coordinate learning analytics to provide continued support to learning across physical and digital spaces? CrossMMLA is the successor to the Learning Analytics Across Spaces (CrossLAK) and MultiModal Learning Analytics (MMLA) series of workshops that were merged in 2017 after successful cross-pollination between the two communities. Although it may be said that CrossLAK and MMLA perspectives follow different philosophical and practical approaches, they both share a common aim. This aim is: deploying learning analytics innovations that can be used across diverse authentic learning environments whilst learners feature various modalities of interaction or behaviour.
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| 2. |
- Friesel, Anna, et al.
(författare)
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Technologies designed and developed in PELARS project : the way to enhance STEM education
- 2017
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Ingår i: 2017 27TH EAEEIE Annual Conference (EAEEIE). - : IEEE. - 9781728109077 - 9781728109084
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Konferensbidrag (refereegranskat)abstract
- Practice-based Experiential Learning Analytics Research and Support (PELARS) is a project about learning and making. The PELARS project finds ways of generating "analytics" (data about the learning process and analysis of this data), which helps learners and teachers by providing feedback from hands-on, project-based and experiential learning situations. In this paper, we present our proposal for improving analytics education with hands-on, project-based and experimental scenarios for engineering students. This is done through teacher and learner engagement, user studies and evaluated trials, performed at UCV (University of Craiova, Romania) and DTU Diplom (Technical University of Denmark, Campus Ballerup, Denmark). The PELARS project provides technological tools and ICT-based methods for collecting activity data ( moving image-based and embedded sensing) for learning analytics (data-mining and reasoning) of practice-based and experiential STEM.
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| 3. |
- Katterfeldt, Eva-Sophie, et al.
(författare)
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Physical computing with plug-and-play toolkits : Key recommendations for collaborative learning implementations
- 2018
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Ingår i: International Journal of Child-Computer Interaction. - : Elsiever. - 2212-8689 .- 2212-8697. ; 17, s. 72-82
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Tidskriftsartikel (refereegranskat)abstract
- Physical computing toolkits have long been used in educational contexts to learn about computational concepts by engaging in the making of interactive projects. This paper presents a comprehensive toolkit that can help educators teach programming with an emphasis on collaboration, and provides suggestions for its effective pedagogical implementation. The toolkit comprises the Talkoo kit with physical computing plug-and-play modules and a visual programming environment. The key suggestions are inspired by the results of the evaluation studies which show that children (aged 14–18 in a sample group of 34 students) are well motivated when working with the toolkit but lack confidence in the kit’s support for collaborative learning. If the intention is to move beyond tools and code in computer education to community and context, thus encouraging computational participation, collaboration should be considered as a key aspect of physical computing activities. Our approach expands the field of programming with physical computing for teenage children with a focus on empowering teachers and students with not only a kit but also its appropriate classroom implementation for collaborative learning.
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| 4. |
- 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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| 5. |
- Spikol, Daniel, 1965-, et al.
(författare)
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Design Strategies for developing a Visual Platform for Physical Computing with Mobile Tools for Project Documentation and Reflection
- 2015
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Ingår i: Proceedings of the Workshops at the 17th International Conference on Artificial Intelligence in Education AIED 2015, Madrid, Spain, June 22 - 26, 2015. - : CEUR-WS.org. ; , s. 57-62, s. 57-62
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Konferensbidrag (refereegranskat)abstract
- This poster discusses work on the design of a visual-based programming language for physical computing and mobile tools for the learners to actively document and reflect on their projects. These are parts of a European project that is investigating how to generate, analyze, use and provide feedback from analytics derived from hands-on learning activities. Our aim is to raise a discussion about how learning analytics, intelligence, and the role of learners’ documenting their work can provide richer opportunities for supporting learning and teaching.
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| 7. |
- Spikol, Daniel, 1965-, et al.
(författare)
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Supervised machine learning in multimodal learning analytics for estimating success in project-based learning
- 2018
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Ingår i: Journal of Computer Assisted Learning. - : John Wiley & Sons. - 0266-4909 .- 1365-2729. ; 34:4, s. 366-377
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Tidskriftsartikel (refereegranskat)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 the use of diverse sensors, including computer vision, user-generated content, and data from the learning objects (physical computing components), to record high-fidelity synchronised multimodal recordings of small groups of learners interacting. 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? To answer this question, we have explored different supervised machine learning approaches (traditional and deep learning techniques) to analyse the data coming from multiple sources. The results illustrate that state-of-the-art computational techniques can be used to generate insights into the "black box" of learning in students' project-based activities. The features identified from the analysis show that distance between learners' hands and faces is a strong predictor of students' artefact quality, which can indicate the value of student collaboration. Our research shows that new and promising approaches such as neural networks, and more traditional regression approaches can both be used to classify multimodal learning analytics data, and both have advantages and disadvantages depending on the research questions and contexts being investigated. The work presented here is a significant contribution towards developing techniques to automatically identify the key aspects of students success in project-based learning environments, and to ultimately help teachers provide appropriate and timely support to students in these fundamental aspects.
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| 8. |
- Zbick, Janosch, 1982-, et al.
(författare)
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Toward an Adaptive and Adaptable Architecture to Support Ubiquitous Learning Activities
- 2016
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Ingår i: Mobile, Ubiquitous, and Pervasive Learning. - Cham : Springer. - 9783319265162 - 9783319265186 ; , s. 193-222
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Bokkapitel (refereegranskat)abstract
- The continuous evolution of learning technologies combined with the changes within ubiquitous learning environments in which they operate result in dynamic and complex requirements that are challenging to meet. The fact that these systems evolve over time makes it difficult to adapt to the constant changing requirements. Existing approaches in the field of Technology Enhanced Learning (TEL) are generally not addressing those issues and they fail to adapt to those dynamic situations. In this chapter, we investigate the notion of an adaptive and adaptable architecture as a possible solution to address these challenges. We conduct a literature survey upon the state of the art and state of practice in this area. The outcomes of those efforts result in an initial model of a Domain-specific architecture to tackle the issues of adaptability and adaptiveness. To illustrate these ideas, we provide a number of scenarios where this architecture can be applied or is already applied. Our proposed approach serves as a foundation for addressing future ubiquitous learning applications where new technologies constantly emerge and new requirements evolve.
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