EDEN research papers: OERs (inc. MOOCs), quality/assessment, social media, analytics and research methods

EDEN has now published a second report on my review of papers submitted to the EDEN research workshop in Oxford a couple of weeks ago. All the full papers for the workshop can be accessed here.

Main lessons (or unanswered questions) I took away:

OERs and MOOCs

• what does awarding badges of certificates for MOOCs or other OER actually mean? For instance will institutions give course exemption or credits for the awards, or accept such awards for admission purposes? Or will the focus be on employer recognition? How will participants who are awarded badges know what their ‘currency’ is worth?
• can MOOCs be designed to go beyond comprehension or networking to develop other critical 21st century skills such as critical thinking, analysis and evaluation? Can they lead to ‘transformational learning’ as identified by Kumar and Arnold (see Quality and Assessment below)
• are there better design models for open courses than MOOCs as currently structured? If so what would they look like?
• is there a future for learning object repositories when nearly all academic content becomes open and online?

Quality and assessment

• research may inform but won’t resolve policy issues
• quality is never ‘objective’ but is value-driven
• the level of intervention must be long and significant enough to result in significant learning gains
• there’s lots of research already that indicates the necessary conditions for successful use of online discussion forums but if these conditions are not present then learning will not take place
• the OU’s traditional model of course design constrains the development of successful collaborative online learning.

Use of social media in open and distance learning

There were surprisingly few papers on this topic. My main takeaway:

• the use of social media needs to be driven by sound pedagogical theory that takes into account the affordances of social media (as in Sorensen’s study described in an earlier post under course design)

Data analytics and student drop-out

• institutions/registrars must pay attention to how student data is tagged/labeled for analytic purposes, so there is consistency in definitions, aggregation and interpretation;
• when developing or applying an analytics software program, consideration needs to be given to the level of analysis and what potential users of the data are looking for; this means working with instructional designers, faculty and administrators from the beginning
• analytics need to be integrated with action plans to identify and support early at risk students

Research methods

• see my earlier post: The dissemination of research in online learning: a lesson from the EDEN Research Workshop

Next

If these bullets interest you at all, then I strongly recommend you go and read the original papers in full – click here. My summary is of necessity personal and abbreviated and the papers provide much greater richness of context.