Category: learner experience
Last week, a reporter from EdSurge reached out to me to shed some light on what Pearson called their Learning Design Principles. The EdSurge article is here, but below is a more detailed rough draft of the points that I made to share. I am posting them here for a fuller picture of some of my thoughts.
- Nothing proprietary (yet, perhaps). I saw a number of sources note that Pearson released their proprietary learning design principles. There’s not much proprietary in the principles. All of these ideas are well-documented in the literature pertaining to educational technology found in cognitive psychology, learning sciences, instructional design, and education literature.
- It’s good to see that Pearson is using findings from the education literature to guide its design and development. Some of these principles should be standard practice. If you are creating educational technology products without considering concepts like instructional alignment, feedback, and scaffolding, authentic learning, student-centered learning environments, and inquiry-based learning, you are likely creating more educational harm than good. The point is that using research to guide educational technology should be applauded and emulated. More educational technology companies should be using research to inform their designs and product iterations.
- BUT, since around 2011, the educational technology industry has promoted the narrative that education has not changed since the dawn of time. With a few exceptions, the industry has ignored the history, theory, and research of the academic fields associated with improving education with technology. The industry has ignored this at its own peril because we have a decent – not perfect, but decent – understanding of how people learn and how we can help improve the ways that people learn. But, the industry has developed products and services starting from scratch, making the same mistakes that other have done in the past, while claiming that their products and services will disrupt education.
- Not all of the items released are principles. For example, “pedagogical agents” is on the list but that’s not a principle. Having studied the implementation of pedagogical agents for more than 7 years, it’s clear that what Pearson is attempting to do is figure our how to better design pedagogical agents for learning. Forgive me while I link to some pdfs of my past work here, but, should amagent’s representation match the content area that they are supporting (should a doctor look like a doctor or should she have a blue mohawk?). Table 1 in this paper provides more on principles for designing pedagogical agents (e.g., agents should establish their role so that learners have a clear anticipation of what the agent can and cannot do: Does the agent purport to know everything or is the agent intended to ask questions but provide no answers?)
- As you can tell from the above, I firmly believe that industry needs research/researchers in developing, evaluating, and refining innovations.
But more importantly, happy, merry, just, and peaceful holidays to everyone!
Over the last year or so, we’ve interviewed more than 200 individuals who have participated in a number of open courses. We are working on a project in which we are using learner narratives and vignettes from these interviews to help administrators, faculty, researchers, and learning designers understand learners and improve their learning experience. Though there are many ways that are used to understand learners (e.g., dashboards) we believe that in-depth vignettes of typical experiences may allow for greater sensitivity of the learners’ lifeworld and realities. We will be using these stories to problematize various aspects of digital learning. Each story will be followed by a longer analysis of the issues raised in the story. For now, below is one such (DRAFT!) story. What do you think? Is there anything else that you’d like to see in this narrative? Is it interesting? If you are an administrator, faculty, researcher, or learning designer, does this story add anything valuable?
Title: Why not?
Theme: Open learning opportunities are oftentimes costless and relatively risk-free.
Mary and her demanding Pomeranian, Kylie, live deep in the heart of Texas. “I have a passion for the law!” the thirty-year-old exclaimed when we called her on her landline. She had seriously considered going to law school and had even passed her LSATS, the law school entrance exams used for US Universities. But having just finished four intense years of a bachelor’s degree, she decided to wait a bit. “Law school just didn’t seem like a good choice at the time,” she reflected. Five years later, Mary has settled into her work as a business consultant. Her interest in the law is still keen, and she’s never completely given up the dream of law school, but it’s been tempered with a bit of realism. “I don’t know if I can afford to spend another three years in the classroom,” she confided to us, “I don’t know if I still have the same passion for the legal industry as I did five years ago.”
During an afternoon enjoying frozen mango margaritas with a friend, trying to cope with the scorching sun, Mary learned about MOOCs. Shortly thereafter, she signed up for a number of courses, dabbling in some and promptly forgetting about others. One day, ContractsX, a course on contract law taught by a Harvard professor, popped up on her screen and she decided to “give it a shot”. What had she got to lose? “It’s a free class, taught at one of the more well-respected institutions. Why not?!” she laughed.
The course was flexible and fit into her busy life. On Saturday mornings she would sit in her office, with Kylie by her side and a warm cup of dark roast coffee in her hand, and use her trusted iPad to watch Harvard Law lectures. These weren’t just any lectures. Professor Fried was a masterful storyteller, a king of his trade. It was through these short, interesting, and memorable stories that Professor Fried taught concepts relating to contract law. “I can’t believe that I’m sitting here, I’m learning this material from Harvard law!” The fast pace and cramped content made the course challenging, Mary acknowledged, and she didn’t always do as well as she would have liked on the course tests. But, as she was able to go back to review the answers and re-watch the videos, this didn’t stress her too much, and she ended up passing the course with flying colours. Proud of her certificate of accomplishment, Mary enthused, “It makes me want to keep coming back for more!”
Even though it was a personal interest in the law that led her to sign up for this course, Mary has found what she learned in ContractsX helpful when she has to deal with contracts in her own job. She has enthusiastically recommended the course to co-workers and friends. She’s currently taking a number of other open courses and is anxiously awaiting the second version of the Contracts course. While Mary’s dream of attending law school, may not have changed, her confidence in herself has: “I never thought of applying to Harvard. There was no way I would be getting in. But then, five years later, I’m taking a course from Harvard. I wouldn’t say that I’m a Harvard law student, but at least now I could sit across from a Harvard law student and have a clear conversation with them. It’s very rewarding to know that.”
Bear with me. This work-in-progress is a bit raw. I’d love any feedback that you might have.
Back in 2008, my colleagues and I wrote a short paper arguing that social justice is a core element of good instructional design. Good designs were, and still are, predominantly judged upon their effectiveness, efficiency, and engagement (e3 instruction). Critical and anti-opressive educators and theorists have laid the foundations of extending educational practice beyond effectiveness a long time ago.
I’m not convinced that edtech, learning design, instructional design, digital learning, or any other label that one wants to apply to the “practice of improving digital teaching and learning” is there yet.
I’ve been thinking more and more about compassion with respect to digital learning. More specifically, I’ve been reflecting on the following question:
What does compassion look like in digital learning contexts?
I’m blogging about this now, because my paper journal is limiting and there is an increasing recognition within various circles in the field that are coalescing around similar themes. For instance,
- The CFP for Learning with MOOCs III asks: What does it mean to be human in the digital age?
- Our research questions reductionist agendas embedded in some approaches to evaluating and enhancing learning online. Similar arguments are made by Jen Ross, Amy Collier, and Jon Becker.
- Kate Bowles says “we have a capacity to listen to each other, and to honour what is particular in the experience of another person.”
- Lumen Learning’s personalized pathways recognize learner agency (as opposed to dominant personalization paradigms that focus on system control)
Compassion is one commonality that these initiatives, calls to action, and observations have in common (and, empowerment, but that’s a different post).
This is not a call for teaching compassion or empathy to the learner. That’s a different topic. I’m more concerned here with how to embed compassion in our practice – in our teaching, in our learning design processes, the technologies that we create, in the research methods that we use. At this point I have a lot of questions and some answers. Some of my questions are:
- What does compassionate digital pedagogy look like?
- What are the purported and actual relationships between compassion and various innovations such as flexible learning environments, competency-based learning, and open education?
- What are the narratives surrounding innovations [The work of Neil Selwyn, Audrey Watters, and David Noble is helpful here]
- What does compassionate technology look like?
- Can technologies express empathy and sympathy? Do students perceive technologies expressing empathy? [Relevant to this: research on pedagogical agents, chatbots, and affective computing]
- What does compassion look like in the design of algorithms for new technologies?
- What does compassionate learning design look like?
- Does a commitment to anti-oppressive education lead to compassionate design?
- Are there any learning design models that explicitly account for compassion and care? Is that perhaps implicit in the general aim to improve learning & teaching?
- In what ways is compassion embedded in design thinking?
- What do compassionate digital learning research methods look like?
- What are their aims and goals?
- Does this question even make sense? Does this question have to do with the paradigm or does it have to do with the perspective employed in the research? Arguing that research methods informed by critical theory are compassionate is easy. Can positivist research methods be compassionate? Researchers may have compassionate goals and use positivist approaches (e.g., “I want to evaluate the efficacy of testing regimes because I believe that they might be harmful to students”).
- What does compassionate digital learning advocacy look like?
- Advocating for widespread adoption of tools/practices/etc without addressing social, political, economic, and cultural contexts is potentially harmful (e.g., Social media might be beneficial but advocating for everyone to use social media ignores the fact that certain populations may face more risks when doing so)
There’s many other topics here (e.g., adjunctification, pedagogies of hope, public scholarship, commercialization….) but there’s more than enough in this post alone!
A number of literature reviews have been published on MOOCs. None has focused exclusively on the empirical literature. In a recent paper, we analyzed the empirical literature published on MOOCs in 2013-2015 to make greater sense of who studies what and how. We found that:
- more than 80% of this literature is published by individuals whose home institutions are in North America and Europe,
- a select few papers are widely cited while nearly half of the papers are cited zero times,
- researchers have favored a quantitative if not positivist approach to the conduct of MOOC research,
- researchers have preferred the collection of data via surveys and automated methods
- some interpretive research was conducted on MOOCs in this time period, but it was often basic and it was the minority of studies that were informed by methods traditionally associated with qualitative research (e.g., interviews, observations, and focus groups)
- there is limited research reported on instructor-related topics, and
- even though researchers have attempted to identify and classify learners into various groupings, very little research examines the experiences of learner subpopulations (e.g., those who succeed vs those who don’t; men vs women).
We believe that the implications arising from this study are important for research on educational technology in general and not jut MOOC research. For instance, given the interest on big data and automated collection/analysis of the data trails that learners leave behind on digital learning environments, a broader methodological toolkit is imperative in the study of emerging digital learning environments.
Here’s a copy of the paper:
Veletsianos, G. & Shepherdson, P. (2016). A Systematic Analysis And Synthesis of the Empirical MOOC Literature Published in 2013-2015. The International Review of Research in Open and Distributed Learning, 17(2).
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.
Recently, I had the privilege of organizing a workshop for the Faculty of Humanities and Social Sciences at Athabasca University. The goal was to help the organization work through what they might need to do to put in practice a new strategic plan which calls for student-centered and open digital learning. I used the slides below to assist faculty, instructors, and instructional designers translate theory into practice.
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.
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.