An interactive avatar toolkit: Enhancing students’ online learning engagement in higher education

​

Online learning in higher education faces the major challenge of scaling up innovative pedagogical practices, which COVID-19-driven school closures and subsequent mass online learning has accelerated. The sudden transition from face-to-face to online learning has raised sudden challenges to both teachers and students (Chiu, 2021). When college students feel involved and belonging during online learning activities, they have positive views of online learning and adapt more easily (Besser et al., 2020). However, many teachers lack experience and capacity to conduct intensive online lessons with innovative pedagogical strategies. As a result, poor learning effectiveness and social isolation have increased the prevalence of social-emotional problems among students (Dhawan, 2020).

To address these issues, this proposed project will develop and implement an interactive avatar (iAvatar) toolkit aligned with:

1. a framework of five dimensions of meaningful learning with technology,

2. the iAvatar toolkit design model, and

3. engagement to create a virtual interactive learning community.

The iAvatar toolkit model will be implemented in 17 courses across different faculties and departments at The Education University of Hong Kong (EdUHK) (refer to Attachment III) to enhance online teaching effectiveness, and student engagement in higher education.

The significance of this iAvatar toolkit project includes

1. supporting teachers’ online teaching with a set of instructional guidelines with the iAvatar toolkit design model,

2. improving students’ learning engagement;

3. its model for knowledge transfer to more disciplines and wider communities to scale up innovative practices across online teaching and learning courses; and

4. laying a foundation for further research in exploring effective ways of using the model to improve students’ learning engagement, emotion and outcomes with artificial intelligence and learning analytics at a large scale.