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Investigating what learners value in marketing MOOCs: a content analysis

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Abstract

The purpose of this study was to investigate learners’ experiences in marketing Massive Open Online Courses (MOOCs). The comments of 255 learners, collected from three top-rated marketing MOOCs, were analyzed with MAXQDA, a content analysis software. The analysis of the 517 meanings (unit of analysis) that emerged from these comments produced five themes and 16 associated categories valued by learners, each comprising several categories as follows: (a) topic and its categories: value, content, difficulty level, knowledge gain, insight increase, and cost effectiveness; (b) instructor and its categories: characteristics, content delivery, and communication; (c) peers and its categories: interaction and evaluation; (d) instructional design and its categories: workload, structuredness, and assessment; and (e) learning resources and its categories: quality and diversity. Among the 517 meanings, 448 were positive and 69 were negative, suggesting that the learners approved of the current practices of teaching and learning in the three marketing MOOCs. Further analyses showed that content delivery in the instructor theme and content and value in the topic theme were of considerable importance from the learners’ perspectives with regard to positive experiences; however, peer evaluation in the peers theme and assessment in the instructional design theme were negatively viewed by the learners. Discussion is provided to interpret the findings.

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Authors and Affiliations

  1. Instructional Design, Development and Evaluation, Syracuse University, Syracuse, NY, USA

    Moon-Heum Cho, Tianxiao Yang & Zhijuan Niu

  2. Korea International University of Ferghana, A Navoiy ko’chasi 55/3 uy, Ferghana, Uzbekistan, 150100

    Jae Kum Kim

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  1. Moon-Heum Cho
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  2. Tianxiao Yang
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  3. Zhijuan Niu
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  4. Jae Kum Kim
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Correspondence to Jae Kum Kim.

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Cho, MH., Yang, T., Niu, Z. et al. Investigating what learners value in marketing MOOCs: a content analysis. J Comput High Educ 36, 93–115 (2024). https://doi.org/10.1007/s12528-022-09347-w

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