The New York Times published an article on an edX course (Introduction to Mao Zedong Thought) offered by Tsinghua University. Inside Higher Ed (IHE) wrote about it, too. The following quote from IHE articles summarizes the articles:
“That course is raising eyebrows because, despite hours of video lectures and supplemental material in the course, students would still have to tab over to Wikipedia to learn about the millions who died as a result of Mao’s land reforms or that his economic initiatives led to what may have been the greatest famine in human history, which killed tens of millions. Introduction to Mao Zedong Thought references those events glancingly in passing as “mistakes,” and generally heaps praise on Mao and his philosophies.”
I was asked to provide commentary for the New York Times article, and since it wasn’t included in the writeup, I thought it would be a good idea to share it publicly rather than leave it hidden away in my email inbox. Here is what I said:
Open courses are transparent, and that’s one of their positive aspects. They allow anyone to examine the ways that course creators think about a topic. The instructional materials from the Mao course are available to anyone to examine and study. One can look at the materials and ask: How do these materials position Mao Zedong? What are the elements of Mao’s thought that the creators of this course want to highlight? What elements of Mao’s thoughts are left behind and what are the elements that are being highlighted? What is the story that is being told here, and who stands to benefit from this story?
Stephen Downes made a similar argument in the IHE article: ““courses that might have been offered behind closed doors are offered for everyone to see.”
Now, that’s parsimonious :)
We recently published a special issue for Educational Media International by asking authors to submit papers focusing on the following question: What is it like to learn and participate in MOOCs? This has now been published.
We developed this special issue to enhance our collective understanding of learner experiences and participation in MOOCs because the scholarly community still has an incomplete mosaic of students’ learning experiences with open online learning.
The following papers are included:
Editorial: Contributions to the mosaic describing learners’ experiences with open online learning (pdf)
George Veletsianos and Vrasidas Charalambos
Learning from MOOCs: a qualitative case study from the learners’ perspectives
Yeonjeong Park, Insung Jung and Thomas C. Reeves
A classroom at home: children and the lived world of MOOCs
Yin Yin, Catherine Adams, Erika Goble and Luis Francisco Vargas Madriz
What makes a cMOOC community endure? Multiple participant perspectives from diverse cMOOCs
Maha Bali, Maureen Crawford, Rhonda Jessen, Paul Signorelli and Mia Zamora
Fulfilling the promise: do MOOCs reach the educationally underserved?
Lorrie Schmid, Kim Manturuk, Ian Simpkins, Molly Goldwasser and Keith E. Whitfield
Examining learners’ perspective of taking a MOOC: reasons, excitement, and perception of usefulness
M. Liu, J. Kang and E. McKelroy
- Note: While the journal is not open access, a number of the authors above have self-archived copies of their paper, like I am doing above.
Spoiler: We’ve been toying with automating the collection of literature on MOOCs (and other topics). Interested? Read further.
Researchers use different ways to keep updated with the literature on a topic. On a daily basis for example, I use Table of Content (TOC) alerts, RSS feeds, and Google Scholar alerts. Many colleagues have sought to keep track of literature on a topic and share it. For example, danah boyd maintained this list of papers on Twitter and microblogging; Tony Bates shared a copy of the MOOC literature he collected on his blog; Katy Jordan also kept a collection of MOOC literature.
A Google Scholar Alert
The problem with maintaining an updated list of relevant literature on a topic is that it quickly becomes a daunting and time-consuming task, especially for popular topics (like MOOCs or social media or teacher training).
In an attempt to automate the collection and sharing of literature, my research team and I created a python script that goes through the Google Scholar alert emails that I receive (see above), parses the content of the emails, and places it in an html page on my server, from where others can access it. The script runs daily and any new literature is added to the page.
We aren’t there just yet, but here is the output for the MOOC literature going back to November 2012. All 400 pages. I placed it in a Google Document because the html file is 2.5mb (and its easier for people to just download it in a format that they prefer)
In theory this is supposed to work quite well, but there’s a couple of problems with it:
- The output is as good as the input. Google Scholar (and its associated alerts) are a black box – meaning there’s no transparency of what is and isn’t indexed.
- It’s automated – which means it’s not clean and some “mooc literature” may not really be mooc literature because Google Scholar alerts work on keywords in the body of papers/text rather than keywords describing the papers/text.
We plan on to make the source code available and describe the process to install this so that others can use it for their own literature needs. My question is: How can the output be more helpful to you? Is there anything else that we can do to improve this?
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.
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.
What do learning experiences in MOOCs look like? Amy Collier, Emily Schneider and I have just published a paper that provides some in-depth answers to this question. Here is a copy of the paper in pdf. The paper is part of a special issue published by the British Journal of Educational Technology which can be found here (there are many excellent pieces in that issue, so be sure to read them).
In addition to trying to understand learner experiences, in the paper we describe that we did this study because “ease of access to large data sets from xMOOCs offered through an increasing number of centralized platforms has shifted the focus of MOOC research primarily to data science and computational methodologies, giving rise to a discourse suggesting that teaching and learning can be fully analyzed, understood and designed for by examining clickstream data”
Our abstract reads:
Researchers describe with increasing confidence what they observe participants doing in massive open online courses (MOOCs). However, our understanding of learner activities in open courses is limited by researchers’ extensive dependence on log file analyses and clickstream data to make inferences about learner behaviors. Further, the field lacks an empirical understanding of how people experience MOOCs andwhy they engage in particular activities in the ways that they do. In this paper, we report three findings derived by interviewing 13 individuals about their experiences in MOOCs. We report on learner interactions in social networks outside of MOOC platforms, notetaking, and the contexts that surround content consumption. The examination and analysis of these practices contribute to a greater understanding of the MOOC phenomenon and to the limitations of clickstream-based research methods. Based on these findings, we conclude by making pragmatic suggestions for pedagogical and technological refinements to enhance open teaching and learning.
We reported 3 main findings:
1. Interactions in social networks outside of the MOOC platform
A number of learners alluded to interactions they have had with individuals who are part of their social networks. These include digital connections with other participants in a MOOC, face-toface interactions with friends and family, and face-to-face interactions with new connections in a MOOC.
Despite the fact that none of the popular MOOC platforms support integrated notetaking at the time of writing this paper, nearly all interviewees reported taking notes while watching lecture videos. Only one interviewee never took notes. However, the tools used to take notes and the subsequent use of notes varied substantially by learner.
3. Consuming content
All individuals participating in this study discussed factors that shaped the ways they consumed MOOC content, shedding light on the context surrounding their participation. Scholars in the learning sciences have long highlighted the critical role of the environment, arguing that learning must be understood as a sociocultural phenomenon situated in context and culture (Brown, Collins & Duguid, 1989). Patterns of MOOC content consumption can be examined by clickstream data, but these contextual factors help explain why learners exhibit particular patterns of participation.
Veletsianos, G., Collier, A., & Schneider, E. (2015). Digging Deeper into Learners’ Experiences in MOOCs: Participation in social networks outside of MOOCs, Notetaking, and contexts surrounding content consumption. British Journal of Educational Technology 46(3), 570-587.
At AERA this week, Amy Collier, Emily Schneider, and I will be presenting a paper that makes a series of arguments regarding learner activities and experiences in MOOCs in relation to clickstream-based MOOC research. One of the implications of our work is the following: learners’ participation and experiences in these courses resist binary and monolithic interpretations as they appear to be mediated by a digital-analog continuum as well as a social-individual continuum. In other words, learning and participation in MOOCs are both distributed and individually-socially negotiated. The following visual (which provides some hints on our results) makes this point clearer:
* and since the work of peer reviewers often goes unrecognized, let it be known, that this insight was prompted by a comment from one anonymous reviewer. So, whoever you are, thank you for your input.