A Comparative Analysis of Flexible Learning and Performance in Computer Programming Course


Learning computer programming entails conceptual and imperative knowledge. Learning difficulties often arise when novice learners apply conceptual knowledge to practice to gain imperative skills. In the post-pandemic era, learners at all levels suffered as learning overturned from fully online to hybrid learning. Lecture recording, live classes, online game-based tools, social and digital media, and collaborative and communications apps are current technologies to support learners. Flexible learning provides learners the choice; of where, when, and how the learning will take place, and time is no longer a constraint. The four primary components of flexible learning comprise technology, pedagogy, implementation, and institution (Collis & Moonen, 2002). Technology merely used without considering the pedagogical aspects is a common pitfall in teaching and learning (Yong et al., 2022). This study investigated the impact on learners' computer programming performance in flexible learning modes compared to blended learning settings. The study samples are four hundred and twenty-five novice programming learners enrolled in Python programming courses in the academic year 2019/2020 (Blended Learning), 2020/2021(Online), and 2021/2022 (Hybrid Learning). The sample learners' performance score data was collected from assignments and the final exam. The result of this study shows that multiple flexible learning modes significantly impact learners' conceptual knowledge attainment and the ability to develop programming writing skills. This study provides insights into implementing flexible learning environments in higher learning institutions and paves the way for further research as the current technologies thrive on supporting flexible learning.

Author Information
Reginamary Matthews, University of Nottingham Malaysia, Malaysia

Paper Information
Conference: SEACE2023
Stream: Teaching Experiences

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Posted by James Alexander Gordon