Exploring Students’ Engagement Patterns in SPOC Forums and their Association with Course Performance
Zhi Liu 1, 2  
Hai Liu 1
Sannyuya Liu 1, 2
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National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, CHINA
National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, CHINA
Department of Computer Science, Humboldt University of Berlin, Berlin, GERMANY
Online publish date: 2018-05-14
Publish date: 2018-05-14
EURASIA J. Math., Sci Tech. Ed 2018;14(7):3143–3158
With the popularity of Small Private Online Courses (SPOCs) in higher education, a plentiful of discussion data has been increasingly generated in SPOC forums. With 752 undergraduates’ discussion posts, this study aims to investigate students’ engagement patterns within SPOC forums in terms of engagement behaviors and emotions. Firstly, we designed a behavioral code rule to identify posting- and content-level behaviors, and examined their association with course performance. Secondly, we built an emotion lexicon including positivity, negativity and confusion word sets, and adopted an emotion calculation approach to visualize emotional evolutionary trends and to examine emotional differences in registration types and course performance. The results show that, (1) the high-performing group was more active in the most engagement behaviors except for interactive postings. (2) The registered group delivered more threads and wrote richer vocabulary in post content. (3) Whether students were registered for a course or not did not have a significant effect on their emotional expressions, but the registered group exhibited more confusion in forum interactions at the end of the semester. (4) Positive emotion was prevailing for the entire population. Furthermore, compared with the low-achieving group, the high-performing group had higher emotion densities in three types of emotions.
Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2015). Predicting students’ emotions using machine learning techniques. Paper presented at the 17th Conference on Artificial Intelligence in Education, Madrid, Spain.
Anderson, A., Huttenlocher, D., Kleinberg, J., & Leskovec, J. (2014). Engaging with massive online courses. Paper presented at the 23rd international conference on World Wide Web, ACM press, Seoul, Korea.
Arguel, A., Lockyer L., Lipp, O. V., Lodge J. M., & Kennedy G. (2017). Inside Out: Detecting Learners’ Confusion to Improve Interactive Digital Learning Environments. Journal of Educational Computing Research, 55(4), 526–551.
Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: Using learning analytics to increase student success. Paper presented at the 2nd International Conference on Learning Analytics and Knowledge, ACM press, Leuven, Belgium.
Austin, J. L. (1975). How to do things with words. Oxford university press.
Broekens, J., & Brinkman, W. P. (2013). AffectButton: A method for reliable and valid affective self-report. International Journal of Human-Computer Studies, 71(6), 641-667.
Cerezo, R., Sánchez-Santillán, M., Paule-Ruiz, M. P., & Núñez, J. C. (2016). Students’ LMS interaction patterns and their relationship with achievement: A case study in higher education. Computers & Education, 96, 42-54.
Chaplot, D. S., Rhim, E., & Kim, J. (2015). Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks. Paper presented at the Workshops at the 17th International Conference on Artificial Intelligence in Education, Madrid, Spain.
Cheng, H. N. H., Liu, Z., Sun, J., Liu, S., & Yang, Z. (2017). Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs. Interactive Learning Environments, 25(2), 1-13.
Chiu, K. F. T., & Hew, K. F. T. (2018). Factors influencing peer learning and performance in MOOC asynchronous online discussion forum. Australasian Journal of Educational Technology, 34(4), 16-28.
Combéfis, S., Bibal, A., & Van Roy, P. (2014). Recasting a traditional course into a MOOC by means of a SPOC. Paper presented at the Proceedings of the 2nd European MOOCs Stakeholders Summit (EMOOCs 2014), Lausanne, Switzerland.
D’Mello, S. K., Craig, S. D., Witherspoon, A., Mcdaniel, B., & Graesser, A. (2008). Automatic detection of learner’s affect from conversational cues. User Modeling and User-Adapted Interaction, 18(1), 45-80.
De Wever, B., Schellens, T., Valcke, M., & Van Keer, H. (2006). Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review. Computers & education, 46(1), 6-28.
Ekahitanond, V. (2014). Promoting university students’ critical thinking skills through peer feedback activity in an online discussion forum. Alberta Journal of Educational Research, 59(2), 247-265.
Ertmer, P. A., Richardson, J. C., Belland, B., Camin, D., Connolly, P., Coulthard, G., Lei, K., & Mong, C. (2007). Using peer feedback to enhance the quality of student online postings: An exploratory study. Journal of Computer-Mediated Communication, 12(2), 412-433.
Fox, A. (2013). From MOOCs to SPOCs. Communications of the ACM, 56(12), 38–40.
Goldberg, L. R., Bell, E., King, C., O’Mara, C., McInerney, F., Robinson, A., & Vickers, J. (2015). Relationship between participants’ level of education and engagement in their completion of the Understanding Dementia Massive Open Online Course. BMC medical education, 15(1), 60.
Guo, P. (2017). MOOC and SPOC, Which One is Better?. Eurasia Journal of Mathematics, Science and Technology Education, 13(8), 5961-5967.
Hollands, F., & Tirthali, D. (2014). MOOCs—expectations and reality. Retrieved from http://www.academicpartnership....
Huang, J., Dasgupta, A., Ghosh, A., Manning, J., & Sanders, M. (2014, March). Superposter behavior in MOOC forums. Paper presented at the Proceedings of the first ACM conference on Learning@ scale conference (pp. 117-126). ACM.
Kaplan, A. M., & Haenlein, M. (2016). Higher education and the digital revolution: About MOOCs, SPOCs, social media, and the Cookie Monster. Business Horizons, 59(4), 441-450.
Kelley, T. L. (1939). The selection of upper and lower groups for the validation of test items. Journal of Educational Psychology, 30(1), 17–24.
Kent, C., Laslo, E., & Rafaeli, S. (2016). Interactivity in online discussions and learning outcomes. Computers & Education, 97, 116-128.
Khalil, M., Kastl, C., & Ebner, M. (2016). Portraying MOOCs learners: A clustering experience using learning analytics. Paper presented at the Conference of the 4th European Stakeholder Summit on experiences and best practices in and around MOOCs (EMOOCS 2016), Graz, Austria.
Kim, W. (2007). Towards a definition and methodology for blended learning. Paper presented at the Proceedings of Workshop on Blended Learning 2007, Edinburgh, United Kingdom.
Klisc, C., McGill, T., & Hobbs, V. (2017). Use of a post-asynchronous online discussion assessment to enhance student critical thinking. Australasian Journal of Educational Technology, 33(5), 63-76.
Kursun, E. (2016). Does Formal Credit Work for MOOC-Like Learning Environments? The International Review of Research in Open and Distributed Learning, 17(3), 75-91.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.
Liu, Z., Cheng, H. N., Liu, S., & Sun, J. (2017). Discovering the two-step lag behavioral patterns of learners in the college SPOC platform. International Journal of Information and Communication Technology Education (IJICTE), 13(1), 1-13.
Liu, Z., Pataranutaporn, V., Ocumpaugh, J., & Baker, R. S. J. d. (2013) Sequences of Frustration and Confusion, and Learning. Paper presented at the 6th International Conference on Educational Data Mining, Memphis, TN.
Liu, Z., Zhang, W., Cheng, H. N., Sun, J., & Liu, S. (2018). Investigating relationship between discourse behavioral patterns and academic achievements of students in SPOC discussion forum. International Journal of Distance Education Technologies (IJDET), 16(2), 37-50.
Liu, Z., Zhang, W., Sun, J., Cheng, H. N., Peng, X., & Liu, S. (2016, September). Emotion and associated topic detection for course comments in a MOOC platform. Paper presented at the Conference of Educational Innovation through Technology (EITT 2016), Tainan, Taiwan.
O’Connor, B., Balasubramanyan, R., Routledge, B. R., & Smith, N. A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. ICWSM, 11(122-129), 1-2.
Phan, T., McNeil, S. G., & Robin, B. R. (2016). Students’ patterns of engagement and course performance in a Massive Open Online Course. Computers & Education, 95, 36-44.
Piccioni, M., Estler, C., & Meyer, B. (2014, June). SPOC-supported introduction to programming. Paper presented at the 2014 Conference on Innovation & technology in Computer Science Education, Uppsala, Sweden.
Qiu, J., Tang, J., Liu, T. X., Gong, J., Zhang, C., Zhang, Q., & Xue, Y. (2016, February). Modeling and predicting learning behavior in MOOCs. Paper presented at the Ninth ACM International Conference on Web Search and Data Mining (WSDM’16), ACM press, San Francisco, CA.
Ramesh, A., Dan, G., Huang, B., Daume, H., & Getoor, L. (2014). Understanding MOOC Discussion Forums using Seeded LDA. Paper presented at the Workshop on Innovative Use of NlP for Building Educational Applications, Baltimore, Maryland USA.
Ramesh, A., Goldwasser, D., Huang, B., Daumé, H., & Getoor, L. (2013). Modeling learner engagement in MOOCs using probabilistic soft logic. Paper presented at NIPS Workshop on Data Driven Education, Sierra Nevada, USA.
Ramesh, A., Kumar, S. H., Foulds, J. R., & Getoor, L. (2015). Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums. Paper presented at the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China,.
Rodriguez P, Ortigosa A, Carro R M. (2012). Extracting Emotions from Texts in E-learning Environments. Paper presented at the Sixth International Conference on Complex, Intelligent, and Software Intensive Systems, Palermo, Italy.
Romero, C., López, M. I., Luna, J. M., & Ventura, S. (2013). Predicting students' final performance from participation in on-line discussion forums. Computers & Education, 68, 458-472.
Rooney, J. E. (2003). Blending learning opportunities to enhance educational programming and meetings. Association Management, 55(5), 26–32.
Rothkrantz, L. J. M. (2016). New didactical models in open and online learning based on social media. Paper presented at the International Conference on e-Learning: e-learning’14, La Laguna, Spain.
Shaw, C. S., & Irwin, K. C. (2017). Forum Quality or Quantity: What is Driving Student Engagement Online? School of Business, 14, 1-30.
Tella, A., & Isah, A. (2011). Pattern of undergraduate’s participation in the online discussion forum at the University of Ilorin, Nigeria. Journal of Information Technology Management, 22(3), 59-76.
Tofade, T., Elsner, J., & Haines, S. T. (2013). Best practice strategies for effective use of questions as a teaching tool. American journal of pharmaceutical education, 77(7), 155.
Tucker, C., Pursel, B. K., & Divinsky, A. (2014). Mining student-generated textual data in MOOCs and quantifying their effects on student performance and learning outcomes. Computers in Education Journal, 5(4), 84-95.
Wang, X., Wen, M., & Rosé, C. P. (2016). Towards triggering higher-order thinking behaviors in MOOCs. Paper presented at the Sixth International Conference on Learning Analytics & Knowledge, Edinburgh, UK.
Wang, X., Yang, D., Wen, M., Koedinger, K., & Rosé, C. P. (2015). Investigating how student’s cognitive behavior in MOOC discussion forums affect learning gains. Paper presented at the 8th International Conference on Educational Data Mining, Madrid, Spain.
Ward, J., & LaBranche, G. A. (2003). Blended learning: The convergence of e-learning and meetings. Franchising World, 35(4), 22–23.
Wei, H., Peng, H., & Chou, C. (2015). Can more interactivity improve learning achievement in an online course? Effects of college students’ perception and actual use of a course-management system on their learning achievement. Computers & Education, 83, 10-21.
Wen, M., Yang, D., & Rosé, C. P. (2014). Sentiment analysis in MOOC discussion forums: What does it tell us? Paper presented at the 7th International Conference on Educational Data Mining, London, UK.
Wise, A. F. (2014, March). Designing pedagogical interventions to support student use of learning analytics. Paper presented at the Fourth International Conference on Learning Analytics and Knowledge, ACM press, Indianapolis, IN.
Yang, D., Kraut, R., & Rosé, C. P. (2016). Exploring the Effect of Student Confusion in Massive Open Online Courses. Journal of Educational Data Mining, 8(1), 52-83.
Yang, D., Wen, M., Howley, I., Kraut, R., & Rose, C. (2015). Exploring the effect of confusion in discussion forums of massive open online courses. In Proceedings of the Second (2015) ACM Conference on Learning@ Scale (pp. 121-130). ACM.
Yang, Z., Liu, Z., Liu, S., Min, L., & Meng, W. (2014). Adaptive multi-view selection for semi-supervised emotion recognition of posts in online student community. Neurocomputing, 144, 138-150.
Zhang, H. (2018). NLPIR Chinese word segmentation system. Retrieved from
Zhang, M., Zhu, J., Zou, Y., Yan, H., Hao, D., & Liu, C. (2015). Educational Evaluation in the PKU SPOC Course “Data Structures and Algorithms”. Proceedings of the Second (2015) ACM Conference on Learning @ Scale (pp.237-240). ACM.