Inclusiveness in open online computing education: geo-cultural perspectives

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My PhD research set out to study the role of demographics and socioeconomic status in online learning. I sought to contribute to our understanding of how online learners learn from, engage with, and perceive various learning design elements (e.g., instructional videos, reading material, discussion-based activities, and assessments). My doctoral research was conducted in pre-Covid times and focused particularly on massive, open, online courses (MOOCs). Despite the strong expectations of the online learning and teaching community that free and widely advertised MOOCs may potentially address the global disparity in education, most active learners originate from specific developed countries. Prior work suggests that how successful online learners are in achieving their learning goals varies along geo-cultural and socioeconomic dimensions as well as with learning design features. But despite diverse enrolments, most MOOCs adopt a one-size-fits-all design that presents the same set and sequence of learning activities to all learners. My research set out to study how learning designs could be adapted at scale in various contexts to improve learners' persistence. The research benefited from a range of theoretical frameworks (e.g., Extended-GLOBE and Hofstede NCDs, culturally-adaptive user interface designs whose constructs are derived from the Technology Acceptance Model or TAM). For conceptualization of socioeconomics, I used gross national income (GNI) for each country. The analysis methods included decision trees, sequence mining, and cross-validated interactions in survival analysis. The mixed-method research used semi-structured interviews and artefact-mediated questions to investigate the contextual differences in MOOC learners' perceptions about various learning design elements. The analysis combined a qualitative (thematic analysis) method with sentiment mining. My doctoral research clearly demonstrated that in comparison to subgroup/interaction analyses, an overall analysis of online learning data could mask geo-cultural and socioeconomic heterogeneity in the correlations between learning design factors and learner persistence. Consequently, overarching data analysis results primarily reflect the behavioural patterns of the largest subgroup (e.g., Anglo-Saxon geo-cultural group, high-income countries), which can stand in contrast to patterns of other, smaller subgroups (e.g., African or South Asian, lower-middle- and low-income countries). As a result, it can lead to improved outcomes for the majority group while leaving behind members of underrepresented groups. For example, using the voluminous log data from ten large MOOCs, Study 3 in my PhD project examined how the quantified the predictive link between learning design elements (e.g., number of videos, reading material, discussion-based learning activities, and quizzes) and learners' persistence varies across the ten geo-cultural contexts. While the qualitative study, Study 4 used semi-structured interviews to explore learners' perceptions. I identified that cross-cultural learning design preferences may vary across the disciplines (e.g., computing, arts and humanities). This research has already made a valuable contribution in solving part of the jigsaw and outlining new directions for future research. Revisiting my research in a post-Covid world, could not be more timely. The implications and lessons learned from my PhD research will appear more relevant if a replication is performed in a more specific context. Computing technology is the most dominant and diverse MOOC subject area. Therefore, benefitting from the allowance of 25% new research, I propose large-scale survey based mixed-method replication of my studies 3 and 4 in the context of online computing education, more specifically, within the context of Computing MOOC developed by Raspberry Pi foundation, offered via FutureLearn platform. Ethical approvals will be attained well before the start of the fellowship.