Many course designers trying to evaluate the experience of participants in a MOOC will find it difficult to track and analyse the online actions and interactions of students because there may be thousands of learners enrolled in courses that sometimes last only a few weeks. This study explores the use of automated sentiment analysis in assessing student experience in a beginner computer programming MOOC. A dataset of more than 25,000 online posts made by participants during the course was analysed and compared to student feedback. The results were further analysed by grouping participants according to their prior knowledge of the subject: beginner, experienced, and unknown. In this study, the average sentiment expressed through online posts reflected the feedback statements. Beginners, the target group for the MOOC, were more positive about the course than experienced participants, largely due to the extra assistance they received. Many experienced participants had expected to learn about topics that were beyond the scope of the MOOC. The results suggest that MOOC designers should consider using sentiment analysis to evaluate student feedback and inform MOOC design.
- teaching programming,
- sentiment analysis,
- target group,
- learner analytics
- Adamopoulos, P. (2013). What makes a great MOOC? An interdisciplinary analysis of student retention in online courses. Proceedings of the 34th International Conference on Information Systems, 1-21. Retrieved from https://aisel.aisnet.org/icis2013/proceedings/BreakthroughIdeas/13/
- Breslow, L., Pritchard, D.E., DeBoer, J., Stump, G.S., Ho, A.D., & Seaton, D.T. (2013). Studying learning in the worldwide classroom research into edX’s first MOOC. Research & Practice in Assessment, 8, 13-25. Retrieved from https://eric.ed.gov/?id=EJ1062850
- Calder, K. (1953). Statistical inference. New York: Holt.
- Crossley, S., McNamara, D.S., Baker, R., Wang, Y., Paquette, L., Barnes, T., & Bergner, Y. (2015). Language to completion: Success in an educational data mining massive open online class. Proceedings of the 8th International Educational Data Mining Society, 388-391. Retrieved from https://eric.ed.gov/?id=ED560771
- Donath, J.S. (1999). Identity and deception in the virtual community. In M.H. Smith & P. Kollock (Eds.), Communities in Cyberspace (pp. 29-59). London: Routledge.
- Downes, S. (2015). The quality of massive open online courses. In B.H. Khan & M. Ally (Eds.), International handbook of e-learning, volume 1: Theoretical perspectives and research (pp. 65-77). Abingdon: Routledge.
- Gonçalves, P., Dalip, D.H., Costa, H., Gonçalves, M.A., & Benevenuto, F. (2016). On the combination of “off-the-shelf” sentiment analysis methods. Proceedings of the 31st Annual ACM Symposium on Applied Computing, 1158-1165. doi: 10.1145/2851613.2851820
- Hong, Y., & Skiena, S. (2010). The wisdom of bookies? Sentiment analysis vs. the NFL point spread. Proceedings of the International Conference on Weblogs and Social Media (IcWSm-2010), 251-254. Retrieved from https://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewPaper/1527
- Huang, H. (2002). Toward constructivism for adult learners in online environments. British Journal of Educational Technology (BJET), 33, 27-37. doi: 10.1111/1467-8535.00236
- Hutto, C.J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Eighth International AAAI Conference on Weblogs and Social Media, 216-225. Retrieved from https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/viewPaper/8109
- Kop, R., Fournier, H., & Mak, J.S.F. (2011). A pedagogy of abundance or a pedagogy to support human beings? Participant support on massive open online courses. The International Review of Research in Open and Distributed Learning, 12(7), 74-93. doi: 10.19173/irrodl.v12i7.1041
- Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167. doi: 10.2200/S00416ED1V01Y201204HLT016
- Liyanagunawardena, T. R., Parslow, P., & Williams, S. A. (2017). Exploring “success” in MOOCs: Participants’ perspective. Massive Open Online Courses and Higher Education: Where to Next?, 92-108. Retrieved from http://centaur.reading.ac.uk/68956/1/BookChapter%207.pdf
- Margaryan, A., Bianco, M., & Littlejohn, A. (2015). Instructional quality of massive open online courses (MOOCs). Computers & Education, 80, 77-83. doi: 10.1016/j.compedu.2014.08.005
- McAuley, A., Stewart, B., Siemens, G., & Cormier, D. (2010). The MOOC model for digital practice. Retrieved from http://www.davecormier.com/edblog/wp-content/uploads/MOOC_Final.pdf
- Merrill, M.D. (2002). First principles of instruction. Educational Technology Research and Development, 50(3), 43-59. doi: 10.1007/BF02505024
- Miller, M., Sathi, C., Wiesenthal, D., Leskovec, J., & Potts, C. (2011). Sentiment flow through hyperlink networks. Proceedings of the Fifth International Conference on Weblogs and Social Media, 550-553. Retrieved from https://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/viewPaper/2883
- Moreno-Marcos, P.M., Alario-Hoyos, C., Merino, P.J., Estévez-Ayres, I., & Kloos, C.D. (2018). Sentiment analysis in MOOCs: A case study. IEEE Global Engineering Education Conference (EDUCON), 1489-1496. Retrieved from https://ieeexplore.ieee.org/abstract/document/8363409
- Pérez, R.C., Jurado, F., & Villen, A. (2019). Moods in MOOCs: Analyzing emotions in the content of online courses with edX-CAS. IEEE Global Engineering Education Conference (EDUCON), 1467-1474. Retrieved from https://ieeexplore.ieee.org/abstract/document/8725107
- Perkins, D.N., Hancock, C., Hobbs, R., Martin, F., & Simmons, R. (1986). Conditions of learning in novice programmers. Journal of Educational Computing Research, 2(1), 37-55. doi: 10.2190/GUJT-JCBJ-Q6QU-Q9PL
- Pratt, J.W. (1959). Remarks on zeros and ties in the Wilcoxon signed rank procedures. Journal of the American Statistical Association, 54(287), 655-667. doi: 10.1080/01621459.1959.10501526
- Shapiro, S.S., & Wilk, M.B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591-611. doi: 10.2307/2333709
- Sharples, M. (2013). Social learning and large scale online learning. Retrieved from https://about.futurelearn.com/blog/massive-scale-social-learning
- Swinnerton, B., Hotchkiss, S., & Morris, N. (2017). Comments in MOOCs: Who is doing the talking and does it help? Journal of Computer Assisted Learning, 33(1), 51-64. doi: 10.1111/jcal.12165
- Tumasjan, A., Sprenger, T.O., Sandner, P.G., & Welpe, I.M. (2010). Predicting elections with Twitter: What 140 characters reveal about political sentiment. Proceedings of the Fourth International Conference on Weblogs and Social Media, 178-185. Retrieved from https://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/viewPaper/1441
- Wen, M., Yang, D., & Rosé, C. (2014). Sentiment analysis in MOOC discussion forums: What does it tell us? Proceedings of Educational Data Mining, 130-137. Retrieved from http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/130_EDM-2014-Full.pdf