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- Cojocaru, Dorian, et al.
(author)
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Prototyping Feedback for Technology Enhanced Learning
- 2016
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In: INTERNATIONAL JOURNAL OF EDUCATION AND INFORMATION TECHNOLOGIES. - : North atlantic university union. - 2074-1316. ; 10, s. 144-151
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Journal article (peer-reviewed)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. |
- Cojocaru, Dorian, et al.
(author)
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Supporting STEM knowledge and skills in engineering education : PELARS project
- 2016
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In: Proceedings of the 2016 ASEE Annual Conference & Exposition. - : The American Society for Engineering Education.
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Conference paper (peer-reviewed)abstract
- In this paper we present our proposal for improving education with hands-on, project-based and experimental scenarios for engineering students with the use of learning analytics. We accomplish this through teacher and learner engagement, user studies and evaluated trials, performed at UCV (University of Craiova, Romania) and DTU (Technical University of Denmark). The PELARS project (Practice-based Experiential Learning Analytics Research And Support) 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. This data is used to create analytics support tools for teachers, learners and administrators, providing frameworks for evidence-based curriculum design and learning systems. The PELARS project creates behavioral recording inputs, proving a new learning analytic that is scalable in application, and bridge qualitative and quantitative methods through reasoning and feedback from input data. The project serves to better understand learners' knowledge in physical activities in laboratory and workshop environments, as well as informal learning scenarios. PELARS traces and helps assess learner progress through technology enhancement, in novel ways building upon current research. The project results in learning analytics tools for practice-based STEM learning that are appropriate for real-world learning environments.
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| 3. |
- Friesel, Anna, et al.
(author)
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Technologies designed and developed in PELARS project : the way to enhance STEM education
- 2017
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In: 2017 27TH EAEEIE Annual Conference (EAEEIE). - : IEEE. - 9781728109077 - 9781728109084
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Conference paper (peer-reviewed)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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