Visual Analytics: Design Study for Exploratory Analytics on Peer Profiles, Activity and Learning Performance for MOOC Forum Activity Assessment

Mohammad Fadhli Asli, Muzaffar Hamzah, Ag Asri Ag Ibrahim, Alter Jimat Embug


The massively open online course (MOOC) has become an increasingly popular alternative platform for education due to its open concept and free features. Due to its features that allow enrolment on a massive scale and participation across the globe, it presented new analytic challenges. The vast amount and variety of data generated pose challenges for the learning analytics community to analyse especially concerning peer presence and peer learning. Forum activity data offers the opportunity to assess the relationship between forum activities and user backgrounds with the learner’s progression and retention rate. Furthermore, there are several challenges in implementing data visualization in real-world scenarios such as different task characterisation compared to the existing analytics, along with varied factors on the usability of visualization among the domain analysts. Despite many research on learning analytics, most of the approaches were data-driven and there were only a handful of studies that were focused on interactive visualization design to facilitate MOOC forum user activity assessment using real-world scenarios and educational theories-driven. Our design study aims to investigate and formulate a visual analytic design to facilitate enriched visual analysis towards assessing forum activity in Malaysian MOOC, particularly in pattern and relationship exploration on the user diverse background and activities with the learning performance. This paper presents our review on visual learning analytics and current MOOC practice in Malaysia, our design study methodology and proposed conceptual visual analytics design on visualizing forum activity data.


visual analytics; information visualization; design study; learning analytics; MOOC.

Full Text:



K. F. T. Chiu and K. F. T. Hew, “Factors influencing peer learning and performance in MOOC asynchronous online discussion forum,” Australas. J. Educ. Technol., 2017.

K. M. Alraimi, H. Zo, and A. P. Ciganek, “Understanding the MOOCs continuance: The role of openness and reputation,” Comput. Educ., vol. 80, pp. 28–38, 2015.

R. Kop and H. Fournier, “New dimensions to self-directed learning in an open networked learning environment,” Int. J. Self-Dir. Learn., vol. 7, no. 2, pp. 1–18, 2011.

D. Yang, M. Wen, I. Howley, R. Kraut, and C. Rose, “Exploring the effect of confusion in discussion forums of massive open online courses,” in Proceedings of the Second (2015) ACM Conference on Learning@ Scale, 2015, pp. 121–130.

C. Vieira, P. Parsons, and V. Byrd, “Visual learning analytics of educational data: A systematic literature review and research agenda,” Comput. Educ., 2018.

S. Johnson, “Knowledge Anywhere :: The Advantages and Disadvantages of a Virtual Learning Environment,” Knowledge Anywhere, 26-Sep-2017. [Online]. Available: [Accessed: 13-Apr-2018].

T. Atapattu, K. Falkner, and H. Tarmazdi, “Topic-wise Classification of MOOC Discussions: A Visual Analytics Approach.,” in EDM, 2016, pp. 276–281.

C. Romero and S. Ventura, “Educational data mining: a review of the state of the art,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 40, no. 6, pp. 601–618, 2010.

D. Baneres, S. Caballé, and R. Clarisó, “Towards a learning analytics support for intelligent tutoring systems on MOOC platforms,” in Complex, Intelligent, and Software Intensive Systems (CISIS), 2016 10th International Conference on, 2016, pp. 103–110.

K. L. Vavra, V. Janjic-Watrich, K. Loerke, L. M. Phillips, S. P. Norris, and J. Macnab, “Visualization in science education,” Alta. Sci. Educ. J., vol. 41, no. 1, pp. 22–30, 2011.

J. Prpic, J. Melton, A. Taeihagh, and T. Anderson, “MOOCs and crowdsourcing: Massive courses and massive resources,” ArXiv Prepr. ArXiv170205002, 2017.

K. A. Cook and J. J. Thomas, “Illuminating the path: The research and development agenda for visual analytics,” 2005.

G. Ellis and F. Mansmann, “Mastering the information age solving problems with visual analytics,” in Eurographics, 2010, vol. 2, p. 5.

J. J. Van Wijk, “The value of visualization,” in Visualization, 2005. VIS 05. IEEE, 2005, pp. 79–86.

D. A. Keim, F. Mansmann, J. Schneidewind, and H. Ziegler, “Challenges in visual data analysis,” in Information Visualization, 2006. IV 2006. Tenth International Conference on, 2006, pp. 9–16.

J. J. Thomas and K. A. Cook, “A visual analytics agenda,” IEEE Comput. Graph. Appl., vol. 26, no. 1, pp. 10–13, 2006.

D. Keim, G. Andrienko, J.-D. Fekete, C. Görg, J. Kohlhammer, and G. Melançon, “Visual analytics: Definition, process, and challenges,” in Information visualization, Springer, 2008, pp. 154–175.

W. Dou, X. Wang, D. Skau, W. Ribarsky, and M. X. Zhou, “Leadline: Interactive visual analysis of text data through event identification and exploration,” in Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on, 2012, pp. 93–102.

J.-D. Fekete, “Progressivis: a toolkit for steerable progressive analytics and visualization,” in 1st Workshop on Data Systems for Interactive Analysis, 2015, p. 5.

B. C. Kwon, J. Verma, and A. Perer, “Peekquence: Visual analytics for event sequence data,” in ACM SIGKDD 2016 Workshop on Interactive Data Exploration and Analytics, 2016.

I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2016.

A. Endert et al., “The state of the art in integrating machine learning into visual analytics,” in Computer Graphics Forum, 2017.

M. Al-Atabi, “Entrepreneurship: The First MOOC in Malaysia,” in Sixth Conference of MIT’s Learning International Networks Consortium, Boston, 2013.

M. Fadzil, L. A. Latif, and T. A. M. T. M. Azzman, “MOOCs in Malaysia: A preliminary case study,” MOOCs Malays. Prelim. Case Study, 2015.

J.-M. Lee, “Different types of human interaction in online discussion: An examination of using online discussion forum,” Proc. Assoc. Inf. Sci. Technol., vol. 43, no. 1, pp. 1–11, 2006.

K. Perumal and G. Hirst, “Semi-supervised and unsupervised categorization of posts in Web discussion forums using part-of-speech information and minimal features,” in Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2016, pp. 100–108.

W. Johnston, “Model visualization,” Inf. Vis. Data Min. Knowl. Discov., pp. 223–228, 2001.

B. Shneiderman, “The eyes have it: A task by data type taxonomy for information visualizations,” in Visual Languages, 1996. Proceedings., IEEE Symposium on, 1996, pp. 336–343.

M. Tory and T. Moller, “Rethinking visualization: A high-level taxonomy,” in Information Visualization, 2004. INFOVIS 2004. IEEE Symposium on, 2004, pp. 151–158.

J. Bertin, Graphics and graphic information processing. Walter de Gruyter, 1981.

M. Sedlmair, M. Meyer, and T. Munzner, “Design study methodology: Reflections from the trenches and the stacks,” IEEE Trans. Vis. Comput. Graph., vol. 18, no. 12, pp. 2431–2440, 2012.

P. Checkland and J. Scholes, Soft systems methodology: a 30-year retrospective. John Wiley Chichester, 1999.

M. Meyer, “Contributions, Methods, and Unique Characteristics of Design Studies,” 2012.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development