The writing of my book, Teaching in a Digital Age, has been interrupted for nearly two weeks by my trip to England for the EDEN Research Workshop. As part of the first draft of the book, I have already published three posts on MOOCs:
- the key design features of MOOCs (‘What is a MOOC?’)
- the differences between cMOOCs and xMOOCs
- the strengths and weaknesses of MOOCs, Part 1, focusing on:
- their contribution to open and free education
- the audience that MOOCs mainly serve
- persistence and commitment of MOOC participants
In this post, I ask (and try to answer) what do participants learn from MOOCs, and I also evaluate their assessment methods.
What do students learn in MOOCs?
This is a much more difficult question to answer, because so little of the research to date (2014) has tried to answer this question. (One reason, as we shall see, is that assessment of learning in MOOCs remains a major challenge). There are at least two kinds of study: quantitative studies that seek to quantify learning gains; and qualitative studies that describe the experience of learners within MOOCs, which indirectly provide some insight into what they have learned.
At the time of writing, the most well conducted study of learning in MOOCs has been by Colvin et al. (2014), who investigated ‘conceptual learning’ in an MIT Introductory Physics MOOC. They compared learner performance not only between different sub-categories of learners within the MOOC, such as those with no physics or math background with those such as physic teachers who had considerable prior knowledge, but also with on-campus students taking the same curriculum in a traditional campus teaching format. In essence, the study found no significant differences in learning gains between or within the two types of teaching, but it should be noted that the on-campus students were students who had failed an earlier version of the course and were retaking it.
This research is a classic example of the no significant difference in comparative studies in educational technology; other variables, such as differences in the types of students, were as important as the mode of delivery. Also, this MOOC design represents a behaviourist-cognitivist approach to learning that places heavy emphasis on correct answers to conceptual questions. It doesn’t attempt to develop the skills needed in a digital age as identified in Chapter 1.
There have been far more studies of the experience of learners within MOOCs, particularly focusing on the discussions within MOOCs (see for instance, Kop, 2011). In general (although there are exceptions), discussions are unmonitored, and it is left to participants to make connections and respond to other students comments.
However, there are some strong criticisms of the effectiveness of the discussion element of MOOCs for developing the high-level conceptual analysis required for academic learning. To develop deep, conceptual learning, there is a need in most cases for intervention by a subject expert, to clarify misunderstandings or misconceptions, to provide accurate feedback, to ensure that the criteria for academic learning, such as use of evidence, clarity of argument, etc., are being met, and to ensure the necessary input and guidance to seek deeper understanding (see Harasim, 2013).
Furthermore, the more massive the course, the more likely participants are to feel ‘overload, anxiety and a sense of loss’, if there is not some instructor intervention or structure imposed (Knox, 2014). Firmin et al. (2014) have shown that when there is some form of instructor ‘encouragement and support of student effort and engagement’, results improve for all participants in MOOCs. Without a structured role for subject experts, participants are faced with a wide variety of quality in terms of comments and feedback from other participants. There is again a great deal of research on the conditions necessary for the successful conduct of collaborative and co-operative group learning (see for instance, Dillenbourg, 1999, Lave and Wenger, 1991), and these findings certainly have not been generally applied to the management of MOOC discussions to date.
One counter argument is that at least cMOOCs develop a new form of learning based on networking and collaboration that is essentially different from academic learning, and MOOCs are thus more appropriate to the needs of learners in a digital age. Adult participants in particular, it is claimed by Downes and Siemens, have the ability to self-manage the development of high level conceptual learning. MOOCs are ‘demand’ driven, meeting the interests of individual students who seek out others with similar interests and the necessary expertise to support them in their learning, and for many this interest may well not include the need for deep, conceptual learning but more likely the appropriate applications of prior knowledge in new or specific contexts. MOOCs do appear to work best for those who already have a high level of education and therefore bring many of the conceptual skills developed in formal education with them when they join a MOOC, and therefore contribute to helping those who come without such skills.
Over time, as more experience is gained, MOOCs are likely to incorporate and adapt some of the findings from research on smaller group work for much larger numbers. For instance, some MOOCs are using ‘volunteer’ or community tutors (Dillenbourg, 2014).The US State Department has organized MOOC camps through US missions and consulates abroad to mentor MOOC participants. The camps include Fulbright scholars and embassy staff who lead discussions on content and topics for MOOC participants in countries abroad (Haynie, 2014). Some MOOC providers, such as the University of British Columbia, pay a small cohort of academic assistants to monitor and contribute to the MOOC discussion forums (Engle, 2014). Engle reported that the use of academic assistants, as well as limited but effective interventions from the instructors themselves, made the UBC MOOCs more interactive and engaging. However, paying for people to monitor and support MOOCs will of course increase the cost to providers. Consequently, MOOCs are likely to develop new automated ways to manage discussion effectively in very large groups. The University of Edinburgh is experimenting with automated ‘teacherbots’ that crawl through online discussion forums and direct predetermined comments to students identified as needing help or encouragement (Bayne, 2014).
These results and approaches are consistent with prior research on the importance of instructor presence for successful for-credit online learning. In the meantime, though, there is much work still to be done if MOOCs are to provide the support and structure needed to ensure deep, conceptual learning where this does not already exist in students. The development of the skills needed in a digital age is likely to be an even greater challenge when dealing with massive numbers. However, we need much more research into what participants actually learn in MOOCs and under what conditions before any firm conclusions can be drawn.
Assessment of the massive numbers of participants in MOOCs has proved to be a major challenge. It is a complex topic that can be dealt with only briefly here. However, Chapter 5.8 provides a general analysis of different types of assessment, and Suen (2014) provides a comprehensive and balanced overview of the way assessment has been used in MOOCs to date. This section draws heavily on Suen’s paper.
Computer marked assignments
Assessment to date in MOOCs has been primarily of two kinds. The first is based on quantitative multiple-choice tests, or response boxes where formulae or ‘correct code’ can be entered and automatically checked. Usually participants are given immediate automated feedback on their answers, ranging from simple right or wrong answers to more complex responses depending on the type of response they have checked, but in all cases, the process is usually fully automated.
For straight testing of facts, principles, formulae, equations and other forms of conceptual learning where there are clear, correct answers, this works well. In fact, multiple choice computer marked assignments were used by the UK Open University as long ago as the 1970s, although the means to give immediate online feedback were not available then. However, this method of assessment is limited for testing deep or ‘transformative’ learning, and particularly weak for assessing the intellectual skills needed in a digital age, such as creative or original thinking.
The second type of assessment that has been tried in MOOCs has been peer assessment, where participants assess each other’s work. Peer assessment is not new. It has been successfully used for formative assessment in traditional classrooms and in some online teaching for credit (Falchikov and Goldfinch, 2000; van Zundert et al., 2010). More importantly, peer assessment is seen as a powerful way to improve deep understanding and knowledge through the rating process, and at the same time, it can be useful for developing some of the skills needed in a digital age, such as critical thinking, for those participants assessing the work of others.
However, a key feature of the successful use of peer assessment has been the close involvement of an instructor or teacher, in providing benchmarks, rubrics or criteria for assessment, and for monitoring and adjusting peer assessments to ensure consistency and a match with the benchmarks set by the instructor. Although an instructor can provide the benchmarks and rubrics in MOOCs, close monitoring of the multiple peer assessments is difficult if not impossible with the very large numbers of participants in MOOCs. As a result, MOOC participants often become incensed at being randomly assessed by other participants who may not and often do not have the knowledge or ability to give a ‘fair’ or accurate assessment of a participant’s work.
Various attempts to get round the limitations of peer assessment in MOOCs have been tried such as calibrated peer reviews, based on averaging the all the peer ratings, and Bayesian post hoc stabilization, but although these statistical techniques reduce the error (or spread) of peer review somewhat they still do not remove the problems of systematic errors of judgement in raters due to misconceptions. This is particularly a problem where a majority of participants fail to understand key concepts in a MOOC, in which case peer assessment becomes the blind leading the blind.
Automated essay scoring
This is another area where there have been attempts to automate scoring. Although such methods are increasingly sophisticated they are currently limited in terms of accurate assessment to measuring primarily technical writing skills, such as grammar, spelling and sentence construction. Once again they do not measure accurately essays where higher level intellectual skills are peerdemonstrated.
Badges and certificates
Particularly in xMOOCs, participants may be awarded a certificate or a ‘badge’ for successful completion of the MOOC, based on a final test (usually computer-marked) which measures the level of learning in a course. The American Council on Education (ACE), which represents the presidents of U.S. accredited, degree-granting institutions, recommended offering credit for five courses on the Coursera MOOC platform. However, according to the person responsible for the review process:
‘what the ACE accreditation does is merely accredit courses from institutions that are already accredited. The review process doesn’t evaluate learning outcomes, but is a course content focused review thus obviating all the questions about effectiveness of the pedagogy in terms of learning outcomes.’ (Book, 2013)
Indeed, most of the institutions offering MOOCs will not accept their own certificates for admission or credit within their own, campus-based programs. Probably nothing says more about the confidence in the quality of the assessment than this failure of MOOC providers to recognize their own teaching.
The intent behind assessment
To evaluate assessment in MOOCs requires an examination of the intent behind assessment. As identified earlier in another chapter of my book, there are many different purposes behind assessment. Peer assessment and immediate feedback on computer-marked tests can be extremely valuable for formative assessment, enabling participants to see what they have understood and to help develop further their understanding of key concepts. In cMOOCs, as Suen points out, learning is measured as the communication that takes place between MOOC participants, resulting in crowdsourced validation of knowledge – it’s what the sum of all the participants come to believe to be true as a result of participating in the MOOC, so formal assessment is unnecessary. However, what is learned in this way is not necessarily academically validated knowledge, which to be fair, is not the concern of cMOOC proponents such as Stephen Downes.
Academic assessment is a form of currency, related not only to measuring student achievement but also affecting student mobility (e.g. entrance to grad school) and perhaps more importantly employment opportunities and promotion. From a learner’s perspective, the validity of the currency – the recognition and transferability of the qualification – is essential. To date, MOOCs have been unable to demonstrate that they are able to assess accurately the learning achievements of participants beyond comprehension and knowledge of ideas, principles and processes (recognizing that there is some value in this alone). What MOOCs have not been able to demonstrate is that they can either develop or assess deep understanding or the intellectual skills required in a digital age. Indeed, this may not be possible within the constraints of massiveness, which is their major distinguishing feature from other forms of online learning, although the lack of valid methods of assessment will not stop computer scientists from trying to find ways to analyze participant online behaviour to show that such learning is taking place.
I hope the next post will be my last on this chapter on MOOCs. It will cover the following topics:
- the cost of MOOCs and economies of scale
- the political, economic and social factors that explain the rise of MOOCs.
Over to you
As regular readers know, this is my way of obtaining peer review for my open textbook (so clearly I am not against peer review in principle!). So if I have missed anything important on this topic, or have misrepresented people’s views, or you just plain disagree with what I’ve written, please let me know. In particular, I am hoping for comments on:
- comprehensiveness of the sources used that address learning and assessment methods in MOOCs
- arguments that should have been included, either as a strength or a weakness
- errors of fact
Yes, I’m a glutton for punishment, but you need to be a masochist to publish openly on this topic.
Bayne, S. (2014) Teaching, Research and the More-than-Human in Digital Education Oxford UK: EDEN Research Workshop (url to come)
Book, P. (2103) ACE as Academic Credit Reviewer–Adjustment, Accommodation, and Acceptance WCET Learn, July 25
Colvin, K. et al. (2014) Learning an Introductory Physics MOOC: All Cohorts Learn Equally, Including On-Campus Class, IRRODL, Vol. 15, No. 4
Dillenbourg, P. (ed.) (1999) Collaborative-learning: Cognitive and Computational Approaches. Oxford: Elsevier
Dillenbourg, P. (2014) MOOCs: Two Years Later, Oxford UK: EDEN Research Workshop (url to come)
Engle, W. (2104) UBC MOOC Pilot: Design and Delivery Vancouver BC: University of British Columbia
Falchikov, N. and Goldfinch, J. (2000) Student Peer Assessment in Higher Education: A Meta-Analysis Comparing Peer and Teacher Marks Review of Educational Research, Vol. 70, No. 3
Firmin, R. et al. (2014) Case study: using MOOCs for conventional college coursework Distance Education, Vol. 35, No. 2
Haynie, D. (2014). State Department hosts ‘MOOC Camp’ for online learners. US News,January 20
Harasim, L. (2012) Learning Theory and Online Technologies New York/London: Routledge
Ho, A. et al. (2014) HarvardX and MITx: The First Year of Open Online Courses Fall 2012-Summer 2013 (HarvardX and MITx Working Paper No. 1), January 21
Knox, J. (2014) Digital culture clash: ‘massive’ education in the e-Learning and Digital Cultures Distance Education, Vol. 35, No. 2
Kop, R. (2011) The Challenges to Connectivist Learning on Open Online Networks: Learning Experiences during a Massive Open Online Course International Review of Research into Open and Distance Learning, Vol. 12, No. 3
Lave, J. and Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge: Cambridge University Press
Milligan, C., Littlejohn, A. and Margaryan, A. (2013) Patterns of engagement in connectivist MOOCs, Merlot Journal of Online Learning and Teaching, Vol. 9, No. 2
Suen, H. (2104) Peer assessment for massive open online courses (MOOCs) International Review of Research into Open and Distance Learning, Vol. 15, No. 3
van Zundert, M., Sluijsmans, D., van Merriënboer, J. (2010). Effective peer assessment processes: Research findings and future directions. Learning and Instruction, 20, 270-279