Abstract
In Hong Kong, after-school activities have long been used to foster friendships and to allow students to pursue their interests in an informal setting. This case study reports on a three-phase action research process in which information technology teachers delivered after-school activities focused on artificial intelligence during the COVID-19 transition to remote learning. Using semi-structured interviews, a motivational questionnaire, and lesson observations, this study describes how extracurricular activities were delivered online using social networking sites and how students perceived the new experience. Our results suggest that, in order to deploy meaningful activities via social media, teachers need to build collaborative environments that facilitate social engagement among students. These findings have implications for new practices in social media and other blended technologies, and can help students strike a healthy balance between their academic and non-academic life during this challenging period.
Highlights
In Hong Kong, after-school activities have long been used to foster friendships and to allow students to pursue their interests in an informal setting
Data collection consisted of semi-structured interviews, motivational questionnaires (Lee, Yin & Zhang, 2010), and lesson observations; analyses created rich descriptions which helped us understand which components of the course were working well and which components needed improvement in order to better meet the needs of struggling students (Stringer, 2008; Efron & Ravid, 2019)
The approach helped secondary students to connect with other classmates which may, in turn, sustain their interest in developing information technology (IT) hobbies, skills, and knowledge
Summary
Each phase represented a ministudy, including a review of past evidence, ensuing program redesign, data collection regarding the redesign, and analysis of those data, which provided evidence for the phase and opportunity for improvement (Avison, Baskerville & Myers, 2001; Creswell, 2014). Data collection consisted of semi-structured interviews, motivational questionnaires (Lee, Yin & Zhang, 2010), and lesson observations; analyses created rich descriptions which helped us understand which components of the course were working well and which components needed improvement in order to better meet the needs of struggling students (Stringer, 2008; Efron & Ravid, 2019). We provide more detail for each phase, including past evidence which informed that phase redesign, key redesign features, data collection and analysis, and practical implications for the phase of redesign
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