Comment Discovery in FutureLearn MOOCs [18-74] Home : ALT Annual Conference 2018

Public Group Active 4 years, 4 months ago

Tue, Sep 11 2018, 1:30pm – 2:30pm

Authors: Philip Tubman, and Tim Ellis

Room: Rm 4.206

Theme: Collaboration for Learning Technology

Type: Short presentation – 20mins

Description

Session Description

MOOCs pedagogy asks 2 questions: what can we achieve BECAUSE OF scale? and what can we achieve DESPITE scale? MOOC platforms are currently designed primarily with a ‘stand and deliver’ pedagogy, which runs contrary to learner centred approaches advocated in the learning sciences community. That is to say, the course is divided into individuals, and the approach is for expert faculty at the centre who hold the knowledge and learners who replicate or duplicate.

We examine an intervention engineered into the learning environment intended to increase collaboration at larger scales and across all courses: The Comment Discovery Tool.

We have used a design based research method to develop the tool, and have developed a taxonomy which can be used for analysis. Results from the initial iteration of this tool suggest positive impact; a comparative analysis of the same courses with and without the tool demonstrate more conversations with higher degrees of continuity and more unique participants when the tool is used. Iteration of the design will be based on participant feedback and consideration of learning activities which work more effectively at scale (e.g. crowdsourcing).

We hope that this session will allow participants to consider collaboration within a frame of learning design and platform affordances, and introduce concepts of ‘crowdsourcing’ and ‘learner centred design’.

Session content: evaluation and reflection

Based on PhD research by the submitter. Lancaster University have developed an interactive visualisation tool which plugs into the FutureLearn platform and enables learners to discover comments based on interest, rather than chronology or ‘likes’. A taxonomy of conversational structures based on sociocultural learning theory is used to evaluate the effectiveness of the tool. Please note: datasets can be represented to participants in the session, but due to new data protection (GDPR) restrictions, FutureLearn suggest that all individual comments are treated as identifiable data, so therefore will be anonymised or redacted from the demonstration of the tool.

References

Chua, S. M., Tagg, C., Sharples, M., & Rienties, B. (2017). Discussion Analytics: Identifying Conversations and Social Learners in FutureLearn MOOCs, http://ceur-ws.org/Vol-1967/FLMOOCS_Paper3.pdf (accessed: April 2018).

Elliott, M. (2016). Stigmergic collaboration: A framework for understanding and designing mass collaboration. In Mass collaboration and education (pp. 65–84). Springer.

Paulin, D., & Haythornthwaite, C. (2016). Crowdsourcing the curriculum: Redefining e-learning practices through peer-generated approaches. The Information Society, 32(2), 130–142. https://doi.org/10.1080/01972243.2016.1130501

Siemens, G. 2012. What is the theory that underpins our moocs? http://www.elearnspace.org/blog/2012/06/03/whatis-the-theory-that-underpins-our-moocs (accessed: June 2015)