The op-ed below first appeared on Inside Higher Ed. This is a first glimpse of a partnership with HarvardX intended to examine learners’ experiences in open courses.
The Invisible Learners Taking MOOCs
“Anyone, anywhere, at any point in time will be able to take advantage of high quality education.”
That could be a tagline from just about any enthusiast or provider of open online courses (often called MOOCs). The intention certainly seems laudable and, if not transformational, at least desirable.
What are the caveats?
Recent research suggests that the majority of people enrolled in these open online courses are highly educated. As far as US participants are concerned, a large percentage also live in high-income neighborhoods.
And yet, despite the extensive research and data on open online courses, we really do not know much about these millions of learners engaged in everything from courses on computer science to poetry to physiotherapy to gender studies to bioinformatics.
In fact, apart from a few anecdotes of extraordinary individuals who overcome insurmountable struggles to succeed (e.g., the exceptional Nigerian man who completed 250 courses) or abstract descriptions of learners and their activity (e.g., “less than 10% complete courses,” “auditors,” or “latecomers”) these learners might as well be invisible.
And thus, my fellow researchers and I are asking more questions. We want to better understand open courses and their learners (and their successes and their failures). How do these people experience open courses? Why they do they things that they do in these courses?
We are currently in the midst of conducting the largest series of interview studies in open courses, and we have just released our first study. Our research is motivated by the fact that very few commentators and researchers to date have paused to talk to learners and to listen to them describe their experiences and activities.
In fact, what researchers know about MOOCs is largely the result of analyzing the data trails that learners leave behind as they navigate digital learning environments.
So far we have interviewed more than 70 individuals who have completed a range of MOOCs. Three of our initial findings question the initial excitement that surrounded MOOCs and contradict the initial hope that these types of courses can help anyone, anywhere, at any point in time to succeed.
Successful online learners have sophisticated study skills. For example, nearly every individual that we have interviewed described his or her notetaking strategies. Learners described how they combine notes across multiple courses and how they arrange notes in order to use them in their exams or future studies.
Learners also described an array of strategies to deal with unfamiliar content, such as using resources external to MOOCs to clarify their understanding of what they learned.
Bjorn, one of the learners we interviewed, reported watching all lecture videos twice. He said: “I read an article about how priming really helps the mind cement content.” And then he applied that insight to his studies: “Instead of watching the videos and taking notes and pausing constantly,” he “watched the video in fast speed first, just really concentrating on the content, and then afterwards, watched it through while taking notes.” This strategy was aimed at improving the processing of new information and demonstrates the sophistication with which some learners approach studying.
Such complex approaches to studying are neither innate nor universal, and throw a cast of doubt over the claim that “anyone” can equally participate in and benefit from these courses.
Flexibility and a flexible life are often essential for engaged participation. A significant proportion of the learners we interviewed either live flexible lives that enable them to participate or appear to be exceptional in their abilities to create time to participate in these courses.
Individuals that live flexible lives are often retirees who frequently tell us that they have time available to explore topics that interest them. Numerous others shared with us that they create time to participate.
For example, a British engineer goes to work an hour earlier every morning in order to work on MOOCs, and an American mother watches MOOC videos when she is not busy caring for her newborn.
Personal and professional circumstances structure the ways that people participate in MOOCs. And here is the conundrum: While online learning experiences can generally be more flexible than face-to-face ones, time is a limited resource, and the individuals who have the privilege of time and flexibility are not necessarily the ones that the quest for the democratization of education via MOOCs aspired to serve.
Online learning is an emotional experience. Somewhere between enrollment numbers, statistics describing completion rates, and the fascination with big data, we forgot that learning experiences are deeply emotional.
Anxiety, appreciation, embarrassment, and pleasure are some of the emotions that learners used to describe their experience in these courses to us.
One of our interviewees, Maria, lives in Greece and works for the public sector. She was “pleasantly surprised” with her experience, especially because she “never thought [she] would be able to study the subject.” She continued: “The most important thing for me is that I can actually learn about the things I have wanted to learn about ever since I was a child. It’s really a dream come true. I will never be able to use it for work or I will never be able to change my profession under the circumstances right now – but I really like and I really want to learn about astronomy and cosmology just for the – just for the joy of it. And that’s why I am going to keep on taking classes.”
Understanding why learners had these emotions is significant in improving digital learning initiatives. More importantly, innovation that lacks care and appreciation for the human condition is not an aspirational strategy to get behind for a bright future.
Our research is providing a better understanding of open online learning and the learners that participate in such endeavors. We are finding that the democratization of education and knowledge are noble goals, but free access to content can only go so far in eliminating societal and global inequities.
What’s the value of a course that features high completion rates but perpetuates gender stereotypes?
What’s the value of a course that is freely available but cannot be accessed by people in remote areas of East Texas or remote areas of British Columbia because of language or technological barriers?
Alternatively, isn’t a course that helps people explore their passions desirable, even if only a small minority participate for the duration of it?
We’ve interviewed learners in Australia, Canada, El Salvador, France, Greece, India, Ireland, the Netherlands, Puerto Rico, the United Kingdom, and the United States. These individuals are not mere statistics to which phrases like “any” “always” and “anywhere” can apply.
Ultimately, our research calls into question whether open courses, in their current form, are the democratizing forces they are sometimes depicted to be—and even whether “educating a billion” with MOOCs is a laudable goal.
By getting to know these invisible learners, we think we can build a better foundation for online learning, the design of digital learning experiences, and the use of technology in education. It is already clear from our initial interviews that in order to create more egalitarian structures for education, we need to start peeling away the multitude of barriers that prevent the most vulnerable populations from participating. And that’s a good goal for all of us who care about learning, teaching, and education.
Acknowledgements: Numerous colleagues, research associates, and students contributed to the research reported here, including: Amy Collier (Stanford), Emily Schneider (Stanford), Peter Shepherdson (University of Zurich), Laura Pasquini (Royal Roads University), and Rich McCue (University of Victoria & Royal Roads University). Special thanks to Justin Reich and Rebecca Petersen (Harvard University) and the rest of the HarvardX research team.
Multidisciplinary, interdisciplinary, and crossdisciplinary research represent promising approaches for studying digital learning. Prior research however, discovered that research efforts directed at digital learning via MOOCs were dominated by individuals affiliated with education (Gašević, Kovanović, Joksimović, and Siemens, 2014). In their assessment of proposals submitted for funding under the MOOC research initiative (MRI), Gašević and colleagues show that more than 50% of the authors in all phases of the MRI grants were from the field of education. This result was interesting because a common perception in the field is that the MOOC phenomenon is “driven by computer scientists” (p. 166).
We were curious to understand whether this was the case with research conducted on MOOCs (as opposed to grant proposals) and used a dataset of author affiliations publishing MOOC research in 2013-2015 to examine the following questions:
RQ 1: What are the disciplinary backgrounds of the authors who published empirical MOOC research in 2013-2015?
RQ 2: How does the disciplinary distribution of the authors who published MOOC research in 2013-2015 compare to that of the submissions to the MRI reported by Gašević et al. (2014)?
RQ 3: Is the 2013-2015 empirical research on MOOCs more or less interdisciplinary than was previously the case?
Results from our paper (published in IRRODL last week) show the following:
- In 2013-2015, Education and Computer Science (CS) were by far the most common affiliations for researchers writing about MOOCs to possess
- During this time period, the field appears to be far from monolithic, as more than 40% of papers written on MOOCs are from authors not affiliated with Education/CS.
- The corpus of papers that we examined (empirical MOOC papers published in 2013-2015) was less dominated by authors from the field of education than were the submissions to the MOOC Research Initiative.
- A comparison of affiliations with past published papers shows that recent MOOC research appears to be more interdisciplinary than was the case in research published in 2008–2012.
We draw 2 implications from these results:
1. Current research on MOOCs appears to be more interdisciplinary than in the past, suggesting that the scientific complexity of the field is being tackled by a greater diversity of researchers. This suggests that even though xMOOCs are often disparaged for their teacher-centric and cognitivist-behaviorist approach, empirical research on xMOOCs may be more interdisciplinary than research on cMOOCs.
2. These results however, also lead us to wonder whether the trend toward greater interdisciplinarity of recent research might reflect (a) the structure and pedagogical model used in xMOOCs, (b) the greater interest in the field of online learning, and (c) the hype and popularity of MOOCs. Could it be that academics’ familiarity with the xMOOC pedagogical model make it a more accessible venue in which researchers from varying disciplines can conduct studies? Or, is increased interdisciplinary attention to digital education the result of media attention, popularity, and funding afforded to the MOOC phenomenon?
We conclude by arguing that “The burgeoning interest in digital learning, learning at scale, online learning, and other associated innovations presents researchers with the exceptional opportunity to convene scholars from a variety of disciplines to improve the scholarly understanding and practice of digital learning broadly understood. To do so however, researchers need to engage in collaborations that value their respective expertise and recognize the lessons learned from past efforts at technology-enhanced learning. Education and digital learning researchers may need to (a) take on a more active role in educating colleagues from other disciplines about what education researchers do and do not know about digital learning from the research that exists in the field and, (b) remain open to the perspectives that academic “immigrants” can bring to this field (cf. Nissani, 1997).”
For more on this, here’s our paper.
This just in: My book, Networked Scholars, is (mostly) complete. It’s out of my hands – as much as book that hasn’t yet been printed is out of anyone’s hands – and I am happy that I have had the experience of writing it.
One of the conclusions/implications of the book that I believe deserves more conversation is the fact that a parallel, even “shadow,” scholarly environment is arising – this is the environment in which networked scholarship is operating. It behooves scholars and institutions to make better sense of it. Shadow educational systems are not new – the private tutoring industry in Cyprus is a prime example of how such systems operate. However, “shadow” or parallel systems take many forms. Siemens argued that a shadow education system has arisen, one in which individuals use the Internet to learn without the support of educational institutions. He argues that this has occurred as a result of institutions of learning having failed to recognize the demand for the unique needs of complex contemporary societies. While this argument focuses on learners, a similar situation is occurring in terms of scholarly practice: The shadow education system that Siemens sees arising encompasses a scholarly environment that runs parallel to the traditional one. This environment, facilitated and encouraged by online social networks, serves scholarly functions and features and supports the development, sharing, negotiation, and evaluation of knowledge. It also functions as an environment where scholars do scholarly things that have little to do with knowledge creation. In this parallel environment, scholars have,
- supported peers and students regardless of hierarchy and institutional affiliation;
- provided advice and care in time of need;
- commented on peers’ in-progress manuscripts;
- delivered guest lectures or have taught open courses, and
- created and shared videos and other media summarizing their scholarship.
Many of these activities have occurred with little or no institutional support and in many instances with little or no institutional oversight.
This is not to say that the emerging parallel scholarly environment is always effective and fair. Many of the power relations and inequities that exist in the traditional scholarly environment are reproduced in networks. For instance, replacing citation/journal metrics with social media metrics does little to resist reductionist agendas.
This parallel environment also appears to encompass (some) alternative signals of influence, prestige, and impact: Follower counts. Presence. But, as Stewart notes, recognizable signals – such as Oxford - are still powerful.
Will this environment replace the traditional one? It’s doubtful, but scholarly environments evolve with the cultures that house them, and as such, I expect that both the traditional environment and this parallel one will converge.
One of the main arguments that we made in our recent paper on MOOCs, which is also the argument that I continue in this op ed piece published in Inside Higher Ed, is that the field needs to embrace diverse research methods to understand and improve digital learning. The following passage is from our paper, and given that the paper is quite long, I thought that posting it here might be helpful:
By capturing and analyzing digital data, the field of learning analytics promises great value and potential in understanding and improving learning and teaching. The focus on big data, log file analyses, and clickstream analytics in MOOCs is reflective of a broader societal trend towards big data analytics (Eynon, 2013; Selwyn, 2014) and toward greater accountability and measurement of student learning in higher education (Leahy, 2013; Moe, 2014). As technology becomes integrated in all aspects of education, the use of digital data and computational analysis techniques in education research will increase. However, an over-reliance on log file analyses and clickstream data to understand learning leaves many learner activities and experiences invisible to researchers.
While computational analyses are a powerful strategy for making a complex phenomenon tractable to human observation and interpretation, an overwhelming focus on any one methodology will fail to generate a complete understanding of individuals’ experiences, practices, and learning. The apparent over-reliance on MOOC platform clickstream data in the current literature poses a significant problem for understanding learning in and with MOOCs. Critics of big data in particular question what is missing from large data sets and what is privileged in the analyses of big data (e.g., boyd & Crawford, 2012). For instance, contextual factors such as economic forces, historical events, and politics are often excluded from clickstream data and analyses (Carr, 2014; Selwyn 2014). As a result, MOOC research frequently examines learning as an episodic and temporary event that is divorced from the context which surrounds it. While the observation of actions on digital learning environments allows researchers to report activities and behaviors, such reporting also needs an explanation as to why learners participate in MOOCs in the ways that they do. For example, in this research, participants reported that their participation in MOOCs varies according to the daily realities of their life and the context of the course. Learners’ descriptions of how these courses fit into their lives are a powerful reminder of the agency of each individual.
To gain a deeper and more diverse understanding of the MOOC phenomenon, researchers need to use multiple research methods. While clickstream data generates insights on observable behaviors, interpretive research approaches (e.g., ethnography, phenomenology, discourse analysis) add context to them. For example, Guo, Kim, and Rubin (2014), analyzed a large data set of MOOC video-watching behaviors, found that the median length of time spent watching a video is six minutes, and recommended that “instructors should segment videos into short chunks, ideally less than 6 minutes.” While dividing content into chunks aligns with psychological theories of learning (Miller, 1956), this finding does not explain why the median length of time learners spent watching videos is six minutes. Qualitative data and approaches can equip researchers to investigate the reasons why learners engage in video-watching behaviors in the ways that they do. For example, the median watching length of time might be associated with learner attention spans. On the other hand, multiple participants in this study noted that they were fitting the videos in-between other activities in their lives – thus shorter videos might be desirable for practical reasons: because they fit in individuals’ busy lives. Different reasons might be uncovered that explain why learners seem to engage with videos for six minutes, leading to different design inspirations and directions. Because the MOOC phenomenon, and its associated practices, are still at a nascent stage, interpretive approaches are valuable as they allow researchers to generate a refined understanding of meaning and scope of MOOCs. At the same time, it is significant to remember that a wholly interpretive approach to understanding learning in MOOCs will be equally deficient. Combining methods and pursuing an understanding of the MOOC phenomenon from multiple angles, while keeping in mind the strengths and weaknesses of each method, is the most productive avenue for future research.
A computational analysis and data science discourse is increasingly evident in educational technology research. This discourse posits that it is possible to tell a detailed and robust story about learning and teaching by relying on the depth and breadth of clickstream data. However, the findings in our research reveal meaningful learner activities and practices that evade data-capturing platforms and clickstream-based research. Off-platform experiences as described above (e.g., notetaking) call into question claims that can be made about learning that are limited to the activities that are observable on the MOOC platform. Further, the reasons that course content is consumed in the ways that it is exemplifies the opportunity to bring together multiple methodological approaches to researching online learning and participation.