ALIGNING STUDENTS’ SELF-REPORTED LEARNING APPROACHES WITH OBSERVED ENGAGEMENT ONLINE

Abstract

BACKGROUND. COVID-19 lockdowns have digitalised tertiary education to Learning Management Systems (LMS) thus understanding the effectiveness of teaching practices in promoting meaningful learning in the digital space is vital. Learning metrics from LMS capture student engagement with online material and have been used to understand the correlates of academic performance (Mogus et al., 2012; Conijn et al., 2017; Zacharias, 2015). But there is conflicting evidence on the relationship between engagement level and academic performance with some studies revealing a non-linear relationship (Firat et al., 2019; Li et al., 2021), as engagement level only reached so high in top performers (Firat et al., 2019; Li et al., 2021). This suggests that high achievers are doing something more effective rather than spending hours on LMS. (Li et al., 2016) Therefore, the specific online activities that are cognitively stimulating and the reasonings behind students’ learning choices remain obscure. Addressing the idea of quality over quantity of learning, Marton and Säljö (1976) introduced learning approaches that explain the influence of students’ motivations and learning strategies on the effectiveness of their learning. They distinguish dichotomic learning approaches known as deep and surface learning (Marton & Säljö, 1976). Deep learning involves students motivated to gain in-depth knowledge using application-based learning strategies. Surface learning entails learning only to pass an exam via rote-learning strategies (Marton & Säljö, 1976). Ultimately, bridging the gap between behaviours represented by numerical LMS data and the reasonings behind this engagement and how that influences performance is necessary. Aims: This study aims to provide meaning to numerical LMS engagement data via student perspectives (using a self-report survey and focus groups), to establish best practices that support students' in-depth learning. METHODS. Students’ input will be gathered from a cohort of Biomedical Science (N=600) students via Biggs’ (2001) R-SPQ-2F survey assessing students’ learning approaches and focus groups discussing how meaningful engagement can be encouraged. These perspectives will be used to give reasonings for trends observed in LMS data for the same group of students.FINDINGS. We anticipate a clear distinction between the motivations and learning strategies of top and low performers and differences in the students’ online behaviours between deep and surface learning. Focus groups will guide understanding on how to enhance meaningful learning. OUTCOMES. This study is unique in utilising three measurement strategies to assess the factors believed to influence students’ learning approaches and engagement. Through this investigation, we anticipate insightful students’ perspectives on how academics can encourage meaningful rather than superficial learning.