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Educators’ motivations in massive open online courses for professional development

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Abstract

Massive Open Online Courses (MOOCs) are increasingly popular for teachers’ professional development (PD). Understanding why teachers take MOOCs and how this relates to course completion could help identify underserved needs in teachers’ professional learning. In the current study, we explored this question, as well as potential gaps between intention to complete the course and actual completion. Using a sample of 3,212 participants in four PD MOOCs, we applied topic modeling to open-ended and Likert-style data to identify teachers’ motivations. The results show that most participants had intrinsic or professional motivations, but a subgroup of participants had prosocial motivations, namely, they wanted to support their students. In a set of logistic regression predicting course completion, we found that participants with intrinsic motivations were less likely to complete a course and participants with prosocial motivations were more likely to do so even after controlling for their initial intention. Our study contributes to the field by, first, identifying an underexplored group of learners, the prosocial learners. More research is needed to better understand this group. We also found that among teachers taking MOOCs, intrinsic motivations were associated with lower levels of engagement, contrary to findings in other populations, making a contribution to motivation theory as well as online learning practice. We concluded that the motivation-engagement relationship is more complex than previously thought, and recommend researchers continue examining this association to understand this discrepancy. Finally, we suggest practitioners take learners’ a-priori motivations into account when designing MOOCs, as these could be important for course engagement.

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Data availability

The data that support the findings of this study are available from MIT Teaching Systems Lab but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of MIT Teaching Systems Lab.

Notes

  1. Note that throughout the paper, we sometimes use “PDs” as meaning “professional development courses” as opposed to the general term “professional development”.

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Acknowledgements

We thank Justin Reich and the Teaching Systems Lab for generously providing access to their data. We also thank Michael Russell for his insightful comments on earlier drafts of this paper.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Author notes

  1. Joshua Littenberg-Tobias

    Present address: GBH Education, 1 Guest Street, Boston, MA, 02135, USA

Authors and Affiliations

  1. Department of Measurement, Evaluation, Statistics, and Assessment, Lynch School of Education and Human Development, Boston College, 140 Commonwealth Avenue, Campion Hall 336, Chestnut Hill, MA, 02467, USA

    Ella Anghel & Matthias von Davier

  2. Teaching Systems Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

    Joshua Littenberg-Tobias

  3. TIMSS and PIRLS International Study Center, Lynch School of Education and Human Development, Boston College, 140 Commonwealth Avenue, MA, Chestnut Hill, 02467, USA

    Ella Anghel & Matthias von Davier

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EA is responsible for the study’s conception, design, data analysis and interpretation, and drafting of the manuscript. JLT contributed to the study’s conception, data acquisition and revised the manuscript. MvD contributed to the study’s conception and revised the manuscript. All authors have approved the submitted version and agree to be accountable for their contributions.

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Correspondence to Ella Anghel.

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Appendix A

Appendix A

Fig. 2
figure 2

Five- to 20-topic models' Heldout likelihood

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Fig. 3
figure 3

Five- to 20-topic models' mean semantic coherence and exclusivity

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Fig. 4
figure 4

Topics' semantic coherence and exclusivity for the nine-, 11-, and 13-topic models

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see Figures 2, 3 and 4 

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Anghel, E., Littenberg-Tobias, J. & von Davier, M. Educators’ motivations in massive open online courses for professional development. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12590-6

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