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Rizvi, Saman Zehra
(2022).
DOI: https://doi.org/10.21954/ou.ro.000142c4
Abstract
Initially, there was a strong expectation amongst some in the online learning and teaching community that free, widely advertised, massive, open, online courses (MOOCs) would potentially address the global disparity in educational attainment. However, it turned out that most popular MOOC providers and the majority of active learners still originate from developed countries, mainly in the Global North. Moreover, how successful online learners are in achieving their learning goals found to vary along geo-cultural and socioeconomic dimensions as well as with learning design features. 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. This PhD project firstly sets out to study the role of demographic contexts in success in online learning using state of the art predictive modelling methods and data from four large online courses. Then to evaluate the potential link between learners’ geo-cultural and socioeconomic contexts and their successful progression. In total around 60,000 learners from ten courses were included in the analyses. Secondly, the research moves on to study how the learning designs can be adapted at scale in various contexts to improve learners’ persistence. The research leveraged data from the largest MOOC platform in Europe, FutureLearn. In addition, the qualitative data were collected using semi-structured interviews and artefact-mediated questions. The analysis methods included a broad range of algorithms primarily affiliated with Learning Analytics (LA) and Educational Data Mining (EDM), such as decision trees, sequence mining, and cross-validated interactions in survival analysis. Finally, the research investigates the contextual differences in MOOC learners’ perception about various elements of learning design. Therefore, the final mixed-method study used an innovative approach and combined a qualitative method (thematic analysis) with sentiment mining. Overall, the research clearly demonstrated that in comparison to subgroup/interaction analyses, an overall analysis of online learning data can 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, which can stand in contrast to patterns of other, smaller subgroups. Suppose overall data analysis findings are used to guide course design and iterative improvement. In that case, it can lead to improved outcomes for the majority group while leaving behind members of underrepresented groups. This research has therefore made a valuable contribution in solving part of the jigsaw and outlining new directions for the future research as well as highlighting the broader implications that go beyond the domain of learning technologies.
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