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Persona profiling: a multi-dimensional model to study learner subgroups in Massive Open Online Courses

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Abstract

Existing research studying MOOC learner diversity has mainly taken unidimensional approaches, which have led to partial or inconsistent findings. This paper addresses this issue by proposing a multi-dimensional model that helps to identify and build the personas of key learner subgroups in any given MOOC course. By linking learners’ behavioral patterns to their characteristics, and situating them in wider context of learning environments, this multi-dimensional model intends to generate more complicated and dynamic understandings of how different factors interweave, together forging learners experience with MOOCs. The model was piloted with an authentic MOOC, and had helped to identified four learner subgroups who actively participated in the course in certain ways: 1) ‘studying against the clock employees’, who studied whenever and wherever it suited them; 2) ‘catch-up employees and homemakers’ who studied a chunk of content in each visit, but failed to complete the course as they returned too infrequently; 3) ‘certificate-driven students and job seekers’, who skipped engaging with course content, instead guessing their way to high scores in assessments through multiple attempts, and 4) ‘learning-at-work employees’, who learned from MOOCs informally as an integral part of their work. The evidence suggests that compared to unidimensional approaches, the multi-dimensional model, with different perspectives complementing and explaining one another, puts together a more comprehensive picture of learners that reflects their preferences and learning habits, dedication and efforts, as well as underlying constraints and difficulties associated with MOOC learning. These enriched insights inspired new directions for designing tailored course formats and delivery modes that are not currently acknowledged in the discourses regarding MOOCs.

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Funding

This study was supported both by Shanghai Science and Technology Innovation Action Plan International Cooperation project "Research on international multi language online learning platform and key technologies (No.20510780100)" and Science and Technology Commission of Shanghai Municipality research project “Shanghai Engineering Research Centre of Open Distance Education (No.13DZ2252200)”.

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  1. Department of Education Information Technology, Faculty of Education, East China Normal University, Shanghai, China

    Ling Li

  2. Shanghai Engineering Research Center of Open Distance Education, Shanghai Open University, No.288 Guoshun Road, Shanghai, 80305, CO, China

    Jun Xiao

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  1. Ling Li
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  2. Jun Xiao
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Correspondence to Jun Xiao.

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Li, L., Xiao, J. Persona profiling: a multi-dimensional model to study learner subgroups in Massive Open Online Courses. Educ Inf Technol 27, 5521–5549 (2022). https://doi.org/10.1007/s10639-021-10829-0

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