| 1. |
- Cojocaru, Dorian, et al.
(författare)
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Prototyping Feedback for Technology Enhanced Learning
- 2016
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Ingår i: INTERNATIONAL JOURNAL OF EDUCATION AND INFORMATION TECHNOLOGIES. - : North atlantic university union. - 2074-1316. ; 10, s. 144-151
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Tidskriftsartikel (refereegranskat)abstract
- The development of new educational technologies, in the area of practical activities is the main aim of the FP7 PELARS project. As part of the constructivist learning scenarios, according to the project proposal, the development and evaluation of technology designs are envisaged, for analytic data generation for Science, Technology, Engineering and Mathematics (STEM) subjects, such as: technology solutions, infrastructure, activities, assessment, curricula, and classroom furniture and environment designs. Inside four EU national settings, three separate learning contexts are being dealt with - from secondary-level high school STEM learning environments to post-secondary level engineering classes and design studios. Given this experience and framework, the present paper provides a perspective on the importance of using such research experience and iterative prototyping in real learning environments for engineering students.
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| 2. |
- Cukurova, Mutlu, et al.
(författare)
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An analysis framework for collaborative problem solving in practice-based learning activities : A mixed-method approach
- 2016
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Ingår i: Proceedings of LAK '16 6th International Conference on Learning Analytics and Knowledge. - New York, New York, USA : ACM Digital Library. ; , s. 84-88
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Konferensbidrag (refereegranskat)abstract
- Systematic investigation of the collaborative problem solving process in open-ended, hands-on, physical computing design tasks requires a framework that highlights the main process features, stages and actions that then can be used to provide 'meaningful' learning analytics data. This paper presents an analysis framework that can be used to identify crucial aspects of the collaborative problem solving process in practice-based learning activities. We deployed a mixed-methods approach that allowed us to generate an analysis framework that is theoretically robust, and generalizable. Additionally, the framework is grounded in data and hence applicable to real-life learning contexts. This paper presents how our framework was developed and how it can be used to analyse data. We argue for the value of effective analysis frameworks in the generation and presentation of learning analytics for practice-based learning activities.
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| 3. |
- Cukurova, Mutlu, et al.
(författare)
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Diagnosing collaboration in practice-based learning : Equality and Intra-individual variability of physical interactivity
- 2017
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Ingår i: Data Driven Approaches in Digital Education. - Cham : Springer. ; , s. 30-42
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Konferensbidrag (refereegranskat)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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| 4. |
- Cukurova, Mutlu, et al.
(författare)
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Modelling Collaborative Problem-solving Competence with Transparent Learning Analytics : Is Video Data Enough?
- 2020
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Ingår i: LAK20. - New York, NY, USA : Association for Computing Machinery (ACM). ; , s. 270-275
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Konferensbidrag (refereegranskat)abstract
- In this study, we describe the results of our research to model collaborative problem-solving (CPS) competence based on analytics generated from video data. We have collected similar to 500 mins video data from 15 groups of 3 students working to solve design problems collaboratively. Initially, with the help of OpenPose, we automatically generated frequency metrics such as the number of the face-in-the-screen; and distance metrics such as the distance between bodies. Based on these metrics, we built decision trees to predict students' listening, watching, making, and speaking behaviours as well as predicting the students' CPS competence. Our results provide useful decision rules mined from analytics of video data which can be used to inform teacher dashboards. Although, the accuracy and recall values of the models built are inferior to previous machine learning work that utilizes multimodal data, the transparent nature of the decision trees provides opportunities for explainable analytics for teachers and learners. This can lead to more agency of teachers and learners, therefore can lead to easier adoption. We conclude the paper with a discussion on the value and limitations of our approach.
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| 5. |
- Healion, Daniel, et al.
(författare)
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Designing Spaces for Collaboration in Practice-Based Learning
- 2017
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Ingår i: CSCL’17 : The 12th International Conference on Computer Supported Collaborative Learning. - : International Society of the Learning Sciences.. ; , s. 565-568
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Konferensbidrag (refereegranskat)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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| 6. |
- Healion, Donal, et al.
(författare)
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Tracing physical movement during practice-based learning through Multimodal Learning Analytics
- 2017
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Ingår i: Proceedings of the Seventh International Learning Analytics & Knowledge Conference. - New York, NY, USA : ACM Digital Library. ; , s. 588-598
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Konferensbidrag (refereegranskat)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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| 7. |
- 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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| 8. |
- Luckin, Rose, et al.
(författare)
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How Do We Unleash AIEd at Scale to Benefit All Teachers and Learners?
- 2017
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Ingår i: Artificial Intelligence in Education. - : Springer. ; , s. 665-667
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Konferensbidrag (refereegranskat)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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| 9. |
- 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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| 10. |
- 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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