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- Peiris, Colombage Ranil, 1973-
(författare)
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A Framework for Designing Learning Management Systems to Support Undergraduate Thesis Projects : With a Focus on Sri Lankan Universities
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In Sri Lankan public universities about 41000 undergraduate projects are conducted every year, and on average, the total man-hours spent on the thesis projects is about 1.2 million. Although the universities widely use information technology to support teaching and learning, a specific system supporting undergraduate thesis projects is lacking and literature documents many problems related to these projects. Hence, the present research endeavour was commenced in a Sri Lankan university to develop a framework to guide the design process of a Learning Management System (LMS) that can be used to address those problems and support Undergraduate Thesis Projects (UTP). The following three research questions guided the study: 1) What are the problems of UTP? 2) Which learning theories and pedagogical concepts should be considered when designing an LMS to support the UTP? 3) What are the requirements and components of an LMS which would support the UTP? The Soft Design Science Research Methodology was applied to answer three research questions, and the main findings are as follows: 1) Six main problems areas were identified based on a specific case, 2) These problems were related to unsatisfied requirements of student student-supervisor interaction, scaffolding, and self-regulation processes 3) These requirements further analysed using related learning theories and specific problems were condensed into a general problem. The general problem is the lack of a learning environment that supports the theoretical foundation (pedagogical implications) and practical facilitation (Information and Communication Technology tools), which could support the student-supervisor interaction, scaffolding, and self-regulation processes, 4) The general problem was analysed, comparing the theoretical foundations and pedagogical implications and a framework was suggested as a general solution for designing an LMS with four basic modules. These modules include software subcomponents that can be used to enhance student-supervisor interaction, peer collaboration, students’ self-regulation skills, and students’ motivation, 5) The general solution was evaluated, and it was shown that supervisors accepted the proposed components as parts of an LMS that supports UTP. The findings show that this framework offers features and components that enhance the quality and importance of thesis projects.
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| 2. |
- Saqr, Mohammed, et al.
(författare)
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A Learning Analytics Study of the Effect of Group Size on Social Dynamics and Performance in Online Collaborative Learning
- 2019
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Ingår i: Transforming Learning with Meaningful Technologies. - Cham : Springer. - 9783030297350 - 9783030297367 ; , s. 466-479
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Konferensbidrag (refereegranskat)abstract
- Effective collaborative learning is rarely a spontaneous phenomenon. In fact, it requires that a set of conditions are met. Among these central conditions are group formation, size and interaction dynamics. While previous research has demonstrated that size might have detrimental effects on collaborative learning, few have examined how social dynamics develop depending on group size. This learning analytics paper reports on a study that asks: How is group size affecting social dynamics and performance of collaborating students? In contrast to previous research that was mainly qualitative and assessed a limited sample size, our study included 23,979 interactions from 20 courses, 114 groups and 974 students and the group size ranged from 7 to 15 in the context of online problem-based learning. To capture the social dynamics, we applied social network analysis for the study of how group size affects collaborative learning. In general, we conclude that larger groups are associated with decreased performance of individual students, poorer and less diverse social interactions. A high group size led to a less cohesive group, with less efficient communication and less information exchange among members. Large groups may facilitate isolation and inactivity of some students, which is contrary to what collaborative learning is about.
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| 3. |
- Tedre, Matti, et al.
(författare)
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Teaching Machine Learning in K-12 Classroom : Pedagogical and Technological Trajectories for Artificial Intelligence Education
- 2021
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 9, s. 110558-110572
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Tidskriftsartikel (refereegranskat)abstract
- Over the past decades, numerous practical applications of machine learning techniques have shown the potential of AI-driven and data-driven approaches in a large number of computing fields. Machine learning is increasingly included in computing curricula in higher education, and a quickly growing number of initiatives are expanding it in K-12 computing education, too. As machine learning enters K-12 computing education, understanding how intuition and agency in the context of such systems is developed becomes a key research area. But as schools and teachers are already struggling with integrating traditional computational thinking and traditional artificial intelligence into school curricula, understanding the challenges behind teaching machine learning in K-12 is an even more daunting challenge for computing education research. Despite the central position of machine learning and AI in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space. This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. The article situates the existing work in the context of computing education in general, and describes some differences that K-12 computing educators should take into account when facing this challenge. The article focuses on key aspects of the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning the belief that rule-based "traditional" programming is a central aspect and building block in developing next generation computational thinking.
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| 4. |
- Temitayo Sanusi, Ismaila, et al.
(författare)
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Promoting Machine Learning Concept to Young Learners in a National Science Fair
- 2022
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Ingår i: Proceedings of 22nd Koli Calling Conference on Computing Education Research. - New York, NY, USA : Association for Computing Machinery (ACM).
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Konferensbidrag (refereegranskat)abstract
- There is a growing number of initiatives for teaching artificial intelligence or machine learning in the compulsory levels of education. However, more research and development is required to understand technological and pedagogical aspects of AI teaching especially in K-12 level. In the context of a two day workshop in a science festival, we introduced the concept of Convolution neural network (CNN) and examined how children learn about the way CNN performs image recognition. The concept was presented through hands-on practice with DoodleIt, a simple app for introducing the fundamental ideas behind CNN.
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| 5. |
- Toivonen, Tapani, et al.
(författare)
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Innovative Maker Movement Platform for K-12 Education as a Smart Learning Environment
- 2018
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Ingår i: Challenges and Solutions in Smart Learning. - Singapore : Springer. - 9789811087431 - 9789811087424 ; , s. 61-66
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Konferensbidrag (refereegranskat)abstract
- The growth of the maker movement has created a demand to include tools for digital fabrication in the school curriculum to foster STEAM education. Yet, the tools used by the maker movement remain sparse and do not exist integrated in the same environment for educational purposes. In this paper, we introduce a smart learning environment that collects the tools of design, 3D-printing, programming, sharing and data analytics into the single frame where K-12 level students and their educators can make maker movement artefacts while enhancing their STEAM skills. Our developed smart learning environment gathers data from the users’ digital trails and analyzes these data with several white-box data mining algorithms in order to support the educators’ interventions in the making activities carried out in the classroom.
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