Abstract
Learner satisfaction is the degree of consistency between learners’ perception and expectation of learning experience. In massive online open courses, analyzing the influencing factors of learner satisfaction is of great significance to improving the quality of course development and learning experience. Taking the open course reviews as data source, the paper adopted topic sentiment analysis and intermediary hierarchical linear modelling to analyze the impact of different student and course level features on learner satisfaction. The data analysis shows that the schedule, workload and completion status, as well as the video, instructor, content and evaluation topics play significant roles in explaining learner satisfaction; However, perceived difficulty, structure and interaction are not related to learner satisfaction; Meanwhile, sentiment-mediated analysis found that, teachers and evaluation topics have significant mediating effect on schedule; Video topic has a significant mediating effect on workload. Based on the above analysis, enlightenment to curriculum designers and developers are also provided.
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Notes
â and \(\widehat{b}\) represent the estimates of a and b, respectively. a and b are symbolic representation of effect quantity from independent variable to dependent variable and from independent variable to intermediate variable. This assumption does not hold in most cases.
Numbers represent the level of variables, and the order of variables is independent variable, intermediate variable and dependent variable.
References
Albelbisi Nour Awni. (2019). Development and validation of the MOOC success scale. Education and Information Technologies, 25, 4535–4555. https://doi.org/10.1007/s10639-020-10186-4
Aparicio, M., Oliveira, T., & Bacao, F. (2019). Gamification: A key determinant of massive open online course (MOOC) success. Information & Management, 56, 39–54. https://doi.org/10.1016/j.im.2018.06.003
Barron-Estrada, M., Zatarain-Cabada, R., & Oramas-Bustillos, R. (2019). Emotion Recognition for Education using Sentiment Analysis. Research in Computing Science, 148(5), 71–80.
Bolliger, D., & Wasilik. (2009). Factors influencing faculty satisfaction with online teaching and learning in higher education. Distance Education, 30(1), 103–116. https://doi.org/10.1080/01587910902845949
Dash, R., Ranjan, K. R., & Rossmann, A. (2021). Dropout management in online learning systems. Behaviour & Information Technology. https://doi.org/10.1080/0144929X.2021.1910730
Deng, R., Benckendorff, P., & Gannaway, D. (2019). Progress and new directions for teaching and learning in MOOCs. Computers & Education, 129(2), 48–60. https://doi.org/10.1016/j.compedu.2018.10.019
Devlin J., Chang M.W., Lee K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv.org, https://arxiv.org/abs/1810.04805v2.
Dhawal, S. (2020). By the Numbers: MOOCs in 2020. Retrieved April 21, 2021, from https://www.classcentral.com/report/mooc-stats-2020/.
Elia, G., Solazzo, G., & Lorenzo, G. (2019). Assessing Learners’ Satisfaction in Collaborative Online Courses through a Big Data approach. Computers in Human Behavior, 92, 589–599. https://doi.org/10.1016/j.chb.2018.04.033
Emily, S. (2013). Welcome to the moocspace: a proposed theory and taxonomy for massive open online courses. In Proceedings of the 1st Workshop on MOOC at the 16th Annual Conference on AI in Education, Memphis, TN.
Emtinan, A. (2019). Predicting student satisfaction and perceived learning within online learning environments. distance education, 40(1):133–148. https://doi.org/10.1080/01587919.2018.1553562.
Feng, X. Y., Zhang, L. L., & Zhang, M. S. (2020). Experimental Research on the Effect of MOOC Video Playback Speed on Cognitive Processing. Modern Educational Technology, 30(2), 51–57. https://doi.org/10.3969/j.issn.1009-8097.2020.02.007
Gameel, G. B. (2017). Learner Satisfaction with Massive Open Online Courses. American Journal of Distance Education, 31(2), 98–111. https://doi.org/10.1080/08923647.2017.1300462
Guo, L. J., & Cao, Y. (2018). Research on the Influencing Mechanism of College Students Learning Satisfaction with MOOCs. Journal of Higher Education, 39(12), 69–75.
Hew, K. F., Hu, X., & Qiao, C. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 1–16. https://doi.org/10.1016/j.compedu.2019.103724
Hew K.F., Qiao C., Tang Y. (2018). Understanding Student Engagement in Large Scale Open Online Courses: A Machine Learning Facilitated Analysis of Student’s Reflections in 18 Highly-Rated MOOCs. International Review of Research in Open & Distributed Learning, 19(3):69–93. https://doi.org/10.19173/irrodl.v19i3.3596.
Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157–168. https://doi.org/10.1016/j.compedu.2016.03.016
Hu, X., Zheng, J., & Fan, T. (2020). Using Multilevel Mediation Model to Measure the Contribution of Beliefs to Judgments of Learning. Frontiers in Psychology, 11, 1–19. https://doi.org/10.3389/fpsyg.2020.00637
Jennifer Roberts. (2020). The MOOC Framework: a metacognitive view. Distance Education in China, 7:50–59, 77. https://doi.org/10.13541/j.cnki.chinade.2020.07.007.
Jiang, Z. H., Zhao, C. L., Li, H. X. (2017). A Study on Learner Satisfaction in Online Open Courses: Development, Influencing Factors and Direction. Modern Distance Education, 3:34–43. https://doi.org/10.13927/j.cnki.yuan.2017.0026.
Kastrati, Z., Imran, A. S., & Kurti, A. (2020). Weakly supervised framework for aspect-based sentiment analysis on students’ reviews of MOOCs. IEEE Access, 8, 799–810. https://doi.org/10.1109/ACCESS.2020.3000739
Kumar, P., & Kumar, N. (2020). A study of learner’s satisfaction from MOOCs through a mediation model. Procedia Computer Science, 173, 354–363. https://doi.org/10.1016/j.procs.2020.06.041
LeBreton, J. M., & Senter, J. L. (2008). Answers to 20 questions about interrater reliability and interrater agreement. Organizational Research Methods, 11(4), 838–839. https://doi.org/10.1177/1094428106296642
Liu, S. Y., Peng, X., & Hercy, N. H. (2019). Unfolding Sentimental and Behavioral Tendencies of Learners’ Concerned Topics from Course Reviews in a MOOC. Journal of Educational Computing Research, 57(3), 670–696. https://doi.org/10.1177/2F0735633118757181
Lu, Y. F., Wang, B., & Lu, Y. B. (2019). Understanding Key Drivers of Mooc Satisfaction and Continuance Intention to Use. Journal of Electronic Commerce Research, 20(2), 105–117.
Michael, M. G. (1991). Editorial: Distance education theory. American Journal of Distance Education, 5(3), 1–6. https://doi.org/10.1080/08923649109526758
Nataliia, D. (2016). Dropout prediction in MOOCs: Using sentiment analysis of users’ comments to predict engagement. University of Twente.
Pozón-López, I., Zoran Kalinic, H. C., & Elena. (2020). A Multi-analytical approach to modeling of customer satisfaction and intention to use in Massive Open Online Courses (MOOC). Interactive Learning Environments, 28, 1003–1021. https://doi.org/10.1080/10494820.2019.1636074
Qin, H. X., Li, Z., & Zhou, J. H. (2020). A Probe into the Satisfaction with Online Teaching of Different Subjects and the Willingness to Continue Using it. Education Research, 41(11), 91–103.
Roslan, M. H., Bin, C. J., & Chen. (2021). Exploring On-Campus Student Satisfaction on Learning via Mooc. International Journal of Academic Research in Business and Social Sciences, 11, 142–153. https://doi.org/10.6007/IJARBSS/v11-i14/8535
Shan, Y. J., Fu, G. S., & Wang, Y. B. (2021). Research on Learners’ Preferences for Gamification Elements in Online Learning. E-education Research, 4:53–60 75. https://doi.org/10.13811/j.cnki.eer.2021.04.010.
Sindhu, I., Daudpota, S. M., & Badar, K. (2019). Aspect-Based Opinion Mining on Student’s Feedback for Faculty Teaching Performance Evaluation. IEEE Access, 2019(7), 29–41. https://doi.org/10.1109/ACCESS.2019.2928872
Wang, Q. (2020). Being Teaching Presence in any Teaching Environments. Modern Distance Education Research, 32(2), 11–19.
Xing, W., Tang, H., & Pei, B. (2019). Beyond positive and negative emotions: Looking into the role of achievement emotions in discussion forums of MOOCs. The Internet and Higher Education, 43, 1–9. https://doi.org/10.1016/j.iheduc.2019.100690
Ye, J. M., Zhou, J., & Li, C. (2020). A Multidimensional Perspective on the Educational Application of Affective Computing. Open Education Research, 26(6):77–88. https://doi.org/10.13966/j.cnki.kfjyyj.2020.06.008.
Yu, L. C., Lee, C. W., & Pan, H. I. (2018). Improving early prediction of academic failure using sentiment analysis on self-evaluated comments. Journal of Computer Assisted Learning, 34(4), 358–365. https://doi.org/10.1111/jcal.12247
Zhao, S., Huang, X. T., & Lu, X. D. (2019). Research on the prediction of emotion index on MOOC students’ achievement. China University Teaching, 5, 66–71.
Acknowledgements
This research was funded by the project of science and Technology Department of Henan Province (192102310288) and Key scientific research projects of colleges and universities in Henan Province (22A880001)
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Du, B. Research on the factors influencing the learner satisfaction of MOOCs. Educ Inf Technol 28, 1935–1955 (2023). https://doi.org/10.1007/s10639-022-11269-0
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DOI: https://doi.org/10.1007/s10639-022-11269-0