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A new approach to observing different learners’ interactive behavior and learning performance in cMOOCs

Jinju Duan

With the development of web 2.0 technology and social media, learners can share, collect, and edit content with peers or friends to create knowledge. In these social spaces, learning approaches are moving away from ‘one-size-fits-all, content-centric models’ and towards ‘learner-centric models’ (Chatti et al 2010: 74). Connectivist massive open online courses (cMOOCs) are one of these social spaces that have been gaining in popularity in recent years. Consequently, cMOOCs have brought new opportunities for extending learning opportunities to the general public. As a teacher, I have had the opportunity to use this approach in my own practice, and I found it is really interesting. This learning approach is less reliant on teachers and rather more dependent on the connections that people make, both to the content and to each other (Mackness et al 2013).

However, I find that there are currently two major problems that affect learning within cMOOCs:

  • high learner dropout rates
  • the high demand on instructors’ workload (Reilly and Von Munkwitz-Smith 2013; Mackness et al 2013).

As the number of learners in a single cMOOC course increases, it requires high workload for me (for example) to monitor every learner’s interaction and provide them with timely feedback. As a result, it is vital to apply advanced tools and approaches to reveal connectivist interaction behaviors, and to portray learners’ performance during learning process in cMOOCs (Khalil and Ebner 2017). My colleagues and I have addressed this issue in our recent study published in the British Journal of Educational Technology (Duan et al 2018). Our study designed and developed a detection approach called PSKN (personal social knowledge network) to aid in the differentiation of students’ behaviour and performance dynamically, which was tested in a cMOOC environment.

‘We have designed and developed a detection approach called PSKN that aids in the differentiation of students’ behaviour and performance dynamically in a cMOOC environment.’

Our main finding was that an individual’s degree of interactive behavior and learning performance can be observed throughout the entire learning process in PSKNs.

Our research provides evidence that the PSKN could aid the observation of connectivist interaction behaviors and performance for different learners in cMOOCs. Specifically, the greater number of connections a learner built the greater their knowledge contribution and also, to some degree, the higher their learning performance, which further establishes the link between connectivity and learning performance. A PSKN therefore has the potential to be a useful tool to distinguish behavior patterns for different learners, as well as to aid the observation of learning performance in large-scale learning contexts, especially for cMOOCs.

Learning in cMOOCs is mainly focussed on knowledge-sharing, collection, co-editing and creation, and is moved forward through connection interaction (Li et al 2016: 2). Correspondingly, PSKN was designed and developed according to the learning characteristics of cMOOCs. As an observation-oriented approach, PSKN overcomes some barriers, such as the time-consuming nature of cMOOCs for teachers/instructors (Li et al 2016), and partly reduces the instructor workload involved in detecting learning in large-scale contexts.

Therefore this approach can be used by instructors to monitor learning in these massive connectivist learning contexts. For example, PSKN can predict learner performance by observing the learning process, or teachers can judge learners’ participation by observing their interaction behaviors (see the full article, Duan et al 2018, for full details). This may enable them to intervene with individuals before they give up completely and drop out of the course (Hughes and Dobbins 2015). This approach can also be used to compare the same groups of learners throughout different stages of the course, as well as to compare different groups of learners at the same time from either the same or a or a different course – for example, in terms of their participation and interactivity.

This blog post is based on the article ‘Exploring a Personal Social Knowledge Network (PSKN) to aid the observation of connectivist interaction for high‐ and low‐performing learners in connectivist massive open online courses’ by Jinju Duan, Kui Xie, Nathan A Hawk, Shengquan Yu and Minjuan Wang, which is published in the British Journal of Educational Technology and is free-to-view for a time-limited period, courtesy of the journal’s publisher, Wiley. 


Chatti M A, Jarke M and Specht M (2010) ‘The 3P Learning Model’, Educational Technology & Society 13(4): 74–85

Duan J, Xie K, Hawk N A, Yu S and Wang M (2018) ‘Exploring a Personal Social Knowledge Network (PSKN) to aid the observation of connectivist interaction for high- and low-performing learners in cMOOCs’, British Journal of Educational Technology.

Hughes G and Dobbins C (2015) ‘The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs)’, Research and Practice in Technology Enhanced Learning 10(1): 10.

Khalil M and Ebner M (2017) ‘Clustering patterns of engagement in Massive Open Online Courses (MOOCs): the use of learning analytics to reveal student categories’, Journal of computing in higher education 29(1): 114–132

Li S, Tang Q and Zhang Y (2016) ‘A Case Study on Learning Difficulties and Corresponding Supports for Learning in cMOOCs’, Canadian Journal of Learning and Technology 42(2): n2

Mackness J, Waite M, Roberts G and Lovegrove E (2013) ‘Learning in a small, task-oriented, connectivist MOOC: Pedagogical issues and implications for higher education’, International Review of Research in Open & Distance Learning 14(4): 140–159

Reilly M and Von Munkwitz-Smith J (2013) ‘Helping to take the disruptive out of MOOCs’, EDUCAUSE Review 48(1): 8–9