Gaining an Insight into Learner Satisfaction in MOOCs: An Investigation through Blog Mining

Mehmet Ali Ustaoğlu; Volkan Kukul

doi:10.55982/openpraxis.14.3.490

Gaining an Insight into Learner Satisfaction in MOOCs: An Investigation through Blog Mining

Open Praxis

Volume 14 (2022): Issue 3

By: Mehmet Ali Ustaoğlu and Volkan Kukul

Open Access

|Sep 2022

Abstract

MOOCs can be considered as a powerful alternative in extraordinary situations where people cannot reach formal education. In recent years, the widespread use of the internet worldwide and especially the CoVID-19 has increased the need of people for MOOCs. However, in order to increase the effectiveness of MOOCs, and to provide a better learning environment, the need to evaluate MOOCs has arisen. One of the indicators of quality in online learning is student satisfaction. Accordingly, this research aims to reveal learner satisfaction in MOOCs. The most important indicator for measuring this satisfaction in MOOCs is user comments. In this study, 39101 comments of the participants in 960 MOOCs were examined by using text mining techniques within the framework of satisfaction.

References

Abeer, W., & Miri, B. (2014). Students’ preferences and views about learning in a MOOC. Procedia-Social and Behavioral Sciences, 152, 318–323. DOI: 10.1016/j.sbspro.2014.09.203
Search in Google Scholar Back to article
Aggarwal, C. C., & Wang, H. (2011). Text mining in social networks. In Social network data analytics (pp. 353–378). Springer. DOI: 10.1007/978-1-4419-8462-3_13
Search in Google Scholar Back to article
Aggarwal, C. C., & Zhai, C. (2012). Mining text data. Springer Science & Business Media. DOI: 10.1007/978-1-4614-3223-4
Search in Google Scholar Back to article
Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148. DOI: 10.1080/01587919.2018.1553562
Search in Google Scholar Back to article
Asoodar, M., Vaezi, S., & Izanloo, B. (2016). Framework to improve e-learner satisfaction and further strengthen e-learning implementation. Computers in Human Behavior, 63, 704–716. DOI: 10.1016/j.chb.2016.05.060
Search in Google Scholar Back to article
Badali, M., Hatami, J., Farrokhnia, M., & Noroozi, O. (2020). The effects of using Merrill’s first principles of instruction on learning and satisfaction in MOOC [Article; Early Access]. Innovations in Education and Teaching International, 10. DOI: 10.1080/14703297.2020.1813187
Search in Google Scholar Back to article
Bahati, B., Fors, U., Hansen, P., Nouri, J., & Mukama, E. (2019). Measuring Learner Satisfaction with Formative e-Assessment Strategies. International Journal of Emerging Technologies in Learning (iJET), 14(07), 61–79. DOI: 10.3991/ijet.v14i07.9120
Search in Google Scholar Back to article
Bohnsack, M., & Puhl, S. (2014). Accessibility of MOOCs. In International Conference on Computers for Handicapped Persons (pp. 141–144). Cham: Springer. DOI: 10.1007/978-3-319-08596-8_21
Search in Google Scholar Back to article
Briggs, A. (2015). Ten ways to overcome barriers to student engagement online. Online Learning Consortium.
Search in Google Scholar Back to article
Brooker, A., Corrin, L., De Barba, P., Lodge, J., & Kennedy, G. (2018). A tale of two MOOCs: How student motivation and participation predict learning outcomes in different MOOCs. Australasian Journal of Educational Technology, 34(1). DOI: 10.14742/ajet.3237
Search in Google Scholar Back to article
Cagiltay, N. E., Cagiltay, K., & Celik, B. (2020). An Analysis of Course Characteristics, Learner Characteristics, and Certification Rates in MITx MOOCs. International Review of Research in Open and Distributed Learning, 21(3), 121–139. DOI: 10.19173/irrodl.v21i3.4698
Search in Google Scholar Back to article
Chametzky, B. (2014). Andragogy and engagement in online learning: Tenets and solutions. Creative education, 5(10), 813–821. DOI: 10.4236/ce.2014.510095
Search in Google Scholar Back to article
Chen, Y. (2014). Investigating MOOCs through blog mining. The International Review of Research in Open and Distributed Learning, 15(2). DOI: 10.4236/ce.2014.510095
Search in Google Scholar Back to article
Criollo-C, S., Luján-Mora, S., & Jaramillo-Alcázar, A. (2018). Advantages and disadvantages of M-learning in current education. 2018 IEEE World Engineering Education Conference (EDUNINE). DOI: 10.1109/EDUNINE.2018.8450979
Search in Google Scholar Back to article
Coccari, G. (2020, 21 May). Udemy’den Haberler – 2020 [Video file]. https://teach.udemy.com/tr/state-of-udemy-2020/
Search in Google Scholar Back to article
Cohen, J., & Kupferschmidt, K. (2020). Countries test tactics in ‘war’against COVID-19. Science, 367(6484). DOI: 10.1126/science.367.6484.1287
Search in Google Scholar Back to article
Cretchley, J., Rooney, D., & Gallois, C. (2010). Mapping a 40-year history with Leximancer: Themes and concepts in the Journal of Cross-Cultural Psychology. Journal of Cross-Cultural Psychology, 41(3), 318–328. DOI: 10.1177/0022022110366105
Search in Google Scholar Back to article
Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management & E-Learning-an International Journal, 11(2), 201–214. DOI: 10.34105/j.kmel.2019.11.010
Search in Google Scholar Back to article
Denis, B., Watland, P., Pirotte, S., & Verday, N. (2004). Roles and competencies of the e-tutor. Paper presented at the Networked learning 2004: A research based conference on networked learning and lifelong learning: Proceedings of the fourth international conference, Lancaster.
Search in Google Scholar Back to article
edX. (2020, 21 May). School Partners. https://www.edx.org/schools-partners
Search in Google Scholar Back to article
Ekwunife-Orakwue, K. C., & Teng, T.-L. (2014). The impact of transactional distance dialogic interactions on student learning outcomes in online and blended environments. Computers & Education, 78, 414–427. DOI: 10.1016/j.compedu.2014.06.011
Search in Google Scholar Back to article
Fisk, K., Cherney, A., Hornsey, M., & Smith, A. (2012). Using computer-aided content analysis to map a research domain: A case study of institutional legitimacy in postconflict East Timor. Sage Open, 2(4). DOI: 10.1177/2158244012467788
Search in Google Scholar Back to article
Gameel, B. G. (2017). Learner satisfaction with massive open online courses. American Journal of Distance Education, 31(2), 98–111. DOI: 10.1080/08923647.2017.1300462
Search in Google Scholar Back to article
Gao, J. (2018). Analysis on the Development Trend and Disadvantages of College English MOOC in the Information Age. International Conference on Contemporary Education, Social Sciences and Ecological Studies (CESSES 2018). DOI: 10.2991/cesses-18.2018.40
Search in Google Scholar Back to article
Guo, P. (2017). MOOC and SPOC, which one is better? Eurasia Journal of Mathematics, Science and Technology Education, 13(8), 5961–5967. DOI: 10.12973/eurasia.2017.01044a
Search in Google Scholar Back to article
Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of emerging technologies in web intelligence, 1(1), 60–76. DOI: 10.4304/jetwi.1.1.60-76
Search in Google Scholar Back to article
Hew, K. F. (2016). Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS. British Journal of Educational Technology, 47(2), 320–341. DOI: 10.1111/bjet.12235
Search in Google Scholar Back to article
Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 103724. DOI: 10.1016/j.compedu.2019.103724
Search in Google Scholar Back to article
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 and Distributed Learning, 19(3). DOI: 10.19173/irrodl.v19i3.3596
Search in Google Scholar Back to article
Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157–168. DOI: 10.1016/j.compedu.2016.03.016
Search in Google Scholar Back to article
Impey, C. D., Wenger, M. C., & Austin, C. L. (2015). Astronomy for astronomical numbers: A worldwide massive open online class. International Review of Research in Open and Distributed Learning, 16(1), 57–79. DOI: 10.19173/irrodl.v16i1.1983
Search in Google Scholar Back to article
Joo, Y. J., So, H.-J., & Kim, N. H. (2018). Examination of relationships among students’ self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260–272. DOI: 10.1016/j.compedu.2018.01.003
Search in Google Scholar Back to article
Kara, M., Kukul, V., & Çakır, R. (2020). Self-regulation in three types of online interaction: How does it predict online pre-service teachers’ perceived learning and satisfaction? The Asia-Pacific Education Researcher, 30(1), 1–10. DOI: 10.1007/s40299-020-00509-x
Search in Google Scholar Back to article
Kember, D., & Ginns, P. (2012). Evaluating teaching and learning: A practical handbook for colleges, universities and the scholarship of teaching. Routledge. DOI: 10.4324/9780203817575
Search in Google Scholar Back to article
Korableva, O., Durand, T., Kalimullina, O., & Stepanova, I. (2019, March). Studying user satisfaction with the MOOC platform interfaces using the example of coursera and open education platforms. In Proceedings of the 2019 International Conference on Big Data and Education (pp. 26–30). DOI: 10.1145/3322134.3322139
Search in Google Scholar Back to article
Kuo, Y. C., Belland, B. R., Schroder, K. E., & Walker, A. E. (2014a). K-12 teachers’ perceptions of and their satisfaction with interaction type in blended learning environments. Distance Education, 35(3), 360–381. DOI: 10.1080/01587919.2015.955265
Search in Google Scholar Back to article
Kuo, Y. C., Walker, A. E., Belland, B. R., & Schroder, K. E. (2013). A predictive study of student satisfaction in online education programs. The International Review of Research in Open and Distributed Learning, 14(1), 16–39. DOI: 10.19173/irrodl.v14i1.1338
Search in Google Scholar Back to article
Kuo, Y. C., Walker, A. E., Schroder, K. E., & Belland, B. R. (2014b). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35–50. DOI: 10.1016/j.iheduc.2013.10.001
Search in Google Scholar Back to article
Leximancer. (2018). Leximancer User Guide Release 4.5. https://doc.leximancer.com/doc/LeximancerManual.pdf
Search in Google Scholar Back to article
Li, K. (2019). MOOC learners’ demographics, self-regulated learning strategy, perceived learning and satisfaction: A structural equation modeling approach. Computers & Education, 132, 16–30. DOI: 10.1016/j.compedu.2019.01.003
Search in Google Scholar Back to article
Li, N., Marsh, V., & Rienties, B. (2016). Modelling and managing learner satisfaction: Use of learner feedback to enhance blended and online learning experience. Decision Sciences Journal of Innovative Education, 14(2), 216–242. DOI: 10.1111/dsji.12096
Search in Google Scholar Back to article
Li, S., Zhang, J., Yu, C., & Chen, L. (2017). Rethinking distance tutoring in e-learning environments: A study of the priority of roles and competencies of Open University Tutors in China. International Review of Research in Open and Distributed Learning, 18(2), 189–212. DOI: 10.19173/irrodl.v18i2.2752
Search in Google Scholar Back to article
Li, Y., Zhang, M., Bonk, C. J., & Guo, Y. (2015). Integrating MOOC and Flipped Classroom Practice in a Traditional Undergraduate Course: Students’ Experience and Perceptions. International Journal of Emerging Technologies in Learning, 10(6). DOI: 10.3991/ijet.v10i6.4708
Open DOI Search in Google Scholar Back to article
Liaw, S.-S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864–873. DOI: 10.1016/j.compedu.2007.09.005
Search in Google Scholar Back to article
Lowenthal, P. R., & Hodges, C. B. (2015). In search of quality: Using Quality Matters to analyze the quality of massive, open, online courses (MOOCs). International Review of Research in Open and Distributed Learning, 16(5), 83–101. DOI: 10.19173/irrodl.v16i5.2348
Search in Google Scholar Back to article
Mackness, J., Mak, S., & Williams, R. (2010, May). The ideals and reality of participating in a MOOC. In Proceedings of the 7th international conference on networked learning, 10, pp. 266–274.
Search in Google Scholar Back to article
Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning, 22(1), 205–222. DOI: 10.24059/olj.v22i1.1092
Search in Google Scholar Back to article
Moore, J. C. (2005). The Sloan Consortium quality framework and the five pillars. The Sloan Consortium. DOI: 10.4018/978-1-59140-555-9.ch245
Search in Google Scholar Back to article
Mulik, S., Srivastava, M., & Yajnik, N. (2020). Flow Experience and MOOC Acceptance: Mediating Role of MOOC Satisfaction. Nmims Management Review, 38(1), 52–68.
Search in Google Scholar Back to article
Paul, R. C., Swart, W., Zhang, A. M., & MacLeod, K. R. (2015). Revisiting Zhang’s scale of transactional distance: Refinement and validation using structural equation modeling. Distance Education, 36(3), 364–382. DOI: 10.1080/01587919.2015.1081741
Search in Google Scholar Back to article
Rabin, E., Kalman, Y. M., & Kalz, M. (2019). An empirical investigation of the antecedents of learner-centered outcome measures in MOOCs. International Journal of Educational Technology in Higher Education, 16(1), 14. DOI: 10.1186/s41239-019-0144-3
Search in Google Scholar Back to article
Rashidi, N., & Moghadam, M. (2014). The Effect of Teachers’ Beliefs and Sense of Self-Efficacy on Iranian EFL Learners’ Satisfaction and Academic Achievement. Tesl-Ej, 18(2), n2.
Search in Google Scholar Back to article
Rodgers, T. (2008). Student engagement in the e-learning process and the impact on their grades. International Journal of Cyber Society and Education, 1(2), 143–156.
Search in Google Scholar Back to article
Rudas, I. J. (2014). MOOC—The educational revolution of the century. 2014 IEEE 12th International Symposium on Intelligent Systems and Informatics (SISY). DOI: 10.1109/SISY.2014.6923578
Search in Google Scholar Back to article
Shah, D. (2021, December 28). A decade of MOOCs: A review of stats and trends for large-scale online courses in 2021. EdSurge. https://www.edsurge.com/news/2021-12-28-a-decade-of-moocs-a-reviewof-stats-and-trends-for-large-scale-online-courses-in-2021
Search in Google Scholar Back to article
Shea, J., Joaquin, M. E., & Wang, J. Q. (2016). Pedagogical design factors that enhance learning in hybrid courses: A contribution to design-based instructional theory. Journal of Public Affairs Education, 22(3), 381–397. DOI: 10.1080/15236803.2016.12002254
Search in Google Scholar Back to article
Sun, P.-C., Tsai, R. J., Finger, G., Chen, Y.-Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202. DOI: 10.1016/j.compedu.2006.11.007
Search in Google Scholar Back to article
Swart, W., MacLeod, K., Paul, R., Zhang, A., & Gagulic, M. (2014). Relative proximity theory: Measuring the gap between actual and ideal online course delivery. American Journal of Distance Education, 28(4), 222–240. DOI: 10.1080/08923647.2014.924721
Search in Google Scholar Back to article
Taneja, S., & Goel, A. (2014). MOOC providers and their strategies. International Journal of Computer Science and Mobile Computing, 3(5), 222–228.
Search in Google Scholar Back to article
Tang, J. K., Xie, H., & Wong, T.-L. (2015). A big data framework for early identification of dropout students in MOOC. International Conference on Technology in Education. DOI: 10.1007/978-3-662-48978-9_12
Search in Google Scholar Back to article
Travaglia, J. F., Westbrook, M. T., & Braithwaite, J. (2009). Implementation of a patient safety incident management system as viewed by doctors, nurses and allied health professionals. Health, 13(3), 277–296. DOI: 10.1177/1363459308101804
Search in Google Scholar Back to article
Vijayarani, S., & Janani, R. (2016). Text mining: open source tokenization tools-an analysis. Advanced Computational Intelligence: An International Journal (ACII), 3(1), 37–47. DOI: 10.5121/acii.2016.3104
Search in Google Scholar Back to article
Wang, Y., & Baker, R. (2018). Grit and intention: Why do learners complete MOOCs? The International Review of Research in Open and Distributed Learning, 19(3). DOI: 10.19173/irrodl.v19i3.3393
Search in Google Scholar Back to article
Zawacki-Richter, O., & Naidu, S. (2016). Mapping research trends from 35 years of publications in Distance Education. Distance Education, 37(3), 245–269. DOI: 10.1080/01587919.2016.1185079
Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.55982/openpraxis.14.3.490 | Journal eISSN: 2304-070X

Journal RSS Feed

Language: English

Page range: 230 - 241

Submitted on: Mar 3, 2022

Accepted on: Sep 12, 2022

Published on: Sep 30, 2022

Published by: International Council for Open and Distance Education (ICDE)

In partnership with: Paradigm Publishing Services

Keywords:

Learner Satisfaction,

MOOCs,

Text Mining,

Blog Mining

© 2022 Mehmet Ali Ustaoğlu, Volkan Kukul, published by International Council for Open and Distance Education (ICDE)
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 14 (2022): Issue 3