When we start...
This initiative first started in 2017 as a teacher community called "eLearning Community of Practice". It aimed at bringing teachers who share the same interests in eLearning together to facilitate the dissemination of good practices and strategies. Up till 2019, six interest groups of different eLearning themes were formed.
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To cover a wider range of teaching interests and innovations, it is now reformed to "Teaching and Learning Community of Practice" (T&L CoP).
2016-
2019
Before the reformation, there were six interest groups on eLearning:
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Educational Emerging Technologies
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Enriching Classroom Interactions and Learning with Technology
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Flipped Classroom and Blended Learning Pedagogical Explorations
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Learning Opportunities Outside Classroom with Technology
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Micro Module Courseware Development (MMCD)
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Technology in Improving Learning Spaces
2019-2022
It wasrestructured and expanded to the following special interests groups (SIGS) with refined and newly added interests:
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Online, Hybrid and Blended Learning Pedagogical Explorations
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Gamification and Game-based Learning
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Curriculum Design and Development
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Application of Immersive Technologies (e.g. Virtual Reality, Augmented Reality, Extended Reality)
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Artificial Intelligence (AI) for Education
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Micro-module and Courseware Development
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Learning Opportunities Outside Classroom with Technology (e.g. Location-based learning)
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Teaching and Learning for Social Good (e.g. Service Learning, Social Entrepreneurship and Global Citizenship)
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Assessment for Learning
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Design and Enhancement of Communal Classrooms
2022 onwards
It is now restructured and expanded to the following special interests groups (SIGS) with refined and newly added interests:​
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Application of Immersive Technologies for Teaching and Learning (e.g. Virtual Reality, Augmented Reality, Extended Reality)
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Artificial Intelligence (AI) for Education
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Design and Enhancement of Communal Classrooms
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Educational Research
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Gamification and Game-based Learning
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Location-based learning
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Micro-module and Courseware Development
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Mixed-mode Learning
