Category: moocs Page 2 of 6

Automating the collection of literature – or, keeping up to date with the MOOC literature

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.

gscholar

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:

  1. 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.
  2. 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 Invisible Learners Taking MOOCs

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.

Now what?

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 cross-disciplinary research on MOOCs and digital learning

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.

Why do we need diverse methodologies to understand/improve digital learning?

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.

Learners’ experiences in MOOCs: Notetaking, social networks, and content consumption

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.

2. Notetaking

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.

Mediated learning experiences and activities in MOOCs and open courses

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:

digital-analog-social-individual

* 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.

 

Emerging Practices in Open Online Learning Environments

I joined Audrey Watters, Philipp Schmidt, Stephen Downes, and Jeremy Friedberg in Toronto last week, to give a talk at Digital Learning Reimagined, an event hosted and organized by Ryerson University’s Chang School. I presented some of our latest research, and tried to highlight research findings and big ideas in 15 minutes. Below are my slides and a draft of my talk.

Welcome everyone! It’s a pleasure and an honor to be here. Even though I’m the person giving this talk, I’d like to acknowledge my collaborators. A lot of the work that I am going to present is collaborative and it  wouldn’t have been possible without such amazing colleagues. These are: Royce Kimmons from the University of Idaho, Amy Collier and Emily Schneider from Stanford University, and Peter Shepherdson from the University of Zurich. The Canada Research Chairs program, the National Science Foundation and Royal Roads University have funded this work.

I want to start my talk by telling a story.

This castle that you see here is one of the most recognizable parts of Royal Roads University (RRU). But, don’t let the castle fool you. RRU was created in 1985. It’s purpose was to serve the needs of a changing society by serving working professionals through graduate digital education and multidisciplinary degrees. It has grown since 1985. It has matured, developed a social learning model that is now infused in all courses, developed new areas of focus, forged global partnerships, and continues to explore how to improve what it does through pedagogical and technological approaches.

Why am I sharing this short story about RRU?

Because this story, minus the specific details, is a common story. It’s also a Ryerson story, a story that is played out at the University of Southern New Hampshire, a story that Open Universities around that world have gone through. It is a story that repeats itself over and over for years and years.

What is the essence of the story?

It is often assumed that universities have been static, unchanging since the dawn of time. The short story I shared illustrates that universities are, and have always been, part of the society that houses them, and as societies change, universities change to reflect those societies. The economic, sociocultural, and technological pressures that universities are facing are sizable, and for better or for worse, usually for both, there’s a continuous re-imagination of education throughout time. Throughout time. Universities have always been changing.

As universities are changing and exploring different ways to offer education, faculty, researchers, and administrators engage in a number of practices that I like to describe as emerging. Emerging practices & emerging technologies are those that are not necessarily new, not yet fully researched, but appear promising.

Online learning and openness are example of emerging practices.

Online learning has a long history. But it also has a new history, with the development of multimedia platforms, media that can be embedded across platforms, syndication technologies that enable learners to use their own platforms for learning and so on. So, even though some of the problems that online learners are facing in contenmporary situations are not new (eg dropout), learners abilities’ to congregate in online communities is expanded through newer technologies and that poses different sorts of challenges and opportunities.

Another emerging practice is openness. Openness refers to liberal policies for the use, re-use, adaptation, and redistribution of content. Openness is also a value: It refers to adopting an ethos of transparency with regards to access to information. And this ethos ranges from academics publishing their work in open formats, to teaching open courses, to creating open textbooks. And it doesn’t stop at individual academics or institutions. In 2014 the Premiers of Alberta, British Columbia, and Saskatchewan signed a Memorandum of Understanding to facilitate creation, sharing, and use of Open Educational Resources. In the same year, SSHRC, NSERC, and CIHR have drafted a tri-agency open access policy to improve access to and dissemination of research results (NSERC, 2014);

There is a growing interest in and exploration of online learning and openness, practices which are still emerging. Next, I will share four recent results from our research into these practices that I believe are interesting to consider because they reveal the tensions that exist when dealing with emerging topics.

First, research into online learning is becoming more interdisciplinary

Interdisciplinary research into online learning means that individuals from a diverse range of disciplines, not just education, are interested in making sense of online learning. It is hoped that more research into online learning and more research from multidisciplinary groups will help us learn more about online learning and about learning in general.

We have evidence to show that research into online learning is becoming more interdisciplinary. I won’t bore you with the statistics, but we measure diversity in published research using a nifty measure and found that the period 2013-2014 can be described as more interdisciplinary than the period 2008-2012.

This is a positive trend, but before I explain its significance, let me explain to you how I view technology.

My perspective on online learning centers around the idea that technology is socially shaped . That means that technology always embeds its developers’ worldviews, beliefs, and assumptions into its design and the activities it supports and encourages.

What does this mean for interdisciplinarity? This means that we have both an opportunity and a challenge.

Our opportunity: to use our respective expertise to improve education.

Our challenge: to actually do interdisciplinary thinking and to go into the study and design of future educational systems with an open mind and the realization that our own personal experiences of education may not be generalizable. A lot of educational technology is produced by people of privilege and to develop educational technology that matters and makes societal difference, we need diversity in thinking and experience.

Our second finding refers to the increasing desire to collect, mine, and analyze data trails to make inferences about human behavior and learning. This practice is often referred to as learning analytics and educational data mining. This practice is a reflection of a larger societal trend toward big data analytics. The idea is that by looking at what people do online one can understand how to improve education.

A couple of things that researchers discovered for example are:

-Students generally stop watching online videos after 4-5 minutes. This then encourages the creation of 4-5 minute lecture videos
-Students fall in discrete categories when they are in MOOCs. For example students who are just sampling content, students who are disengaged,  or they are on track for completing. Once you identify categories you can identify and support learner needs

Data trails. Nearly everything that learners do online is tracked. Can we understand learners and improve learning by analyzing their data trails?

While these approaches can help us explain what people do, they often don’t tell us why they do they things they do nor how they actually experience online education.

My colleagues and I are interviewing MOOC students to learn about their experiences in MOOCs.

I am now going to tell you about our third result. We find that learners schedule their learning, use of resources, and participation to fit their daily life. This is in stark contrast to the idea of undergraduate education situated at a university and happening at particular time periods.

One retired individual in Panama that we interviewed works on his class early in the morning every day. Why does he do that? He does that because at that time his daughter is asleep. She is homeschooled and once she wakes up she needs access to the 1 computer that they have in the household to do her own schoolwork. In this case a lack of resources necessitates this scheduling.

One individual that we interviewed moved from the UK to the USA to be with her partner. She is currently waiting for her work permit, driver’s license, and so on, and she was enrolled in multiple MOOCs at the same time. She would work on her courses on Monday because she just “wanted them out of the way,” and so she would work on these courses straight throughout the day.

The fourth and final finding that I have for you today, is that MOOC platforms to date have not offered learners the ability to keep notes, so that particular activity, by virtue of being unsupported by the platform goes undetected when researchers only look at data trails.

Unsurprisingly, learners keep notes. A number of students that we talked to described that they keep notes on paper, frequently keeping a notebook for particular courses and returning to them during exams or during times that they needed them. Learners of course also keep notes in digital format. Usually in word documents, but again documents are dedicated to particular courses, but sometimes they are dedicated to particular topics across courses.

To give you an example, of how we believe this activity could be supported in the future and how we believe innovations  can contribute to learning, we recommend designers support this practice by pedagogical innovations such as scaffolding notetaking, but also by technological innovations, by developing online systems for notetaking. What is important here is that such systems should support learning by being interoperable, by allow learners full and unrestricted access to their notes, supporting them to be able to import & export their notes between platforms. Such a design is in line with emerging ideas in the field which call for learners to own their data.

To summarize:

1. Online learning is becoming more interdisciplinary, but we need to work together and address our assumptions
2. There is excitement about learning analytics, but we also need to understand why people do the things that they do
3. For example, we see that online education needs to accommodate lives as opposed to the other way round
4. And we see that by interviewing people we can get a better sense of the things that they do that don’t get captured by the digital trails they leave behind.

Thank you for being a great audience. I am really excited to hear the speakers that follow me, as I am sure you are!

visual

A visualization of my talk, created by Giulia Forsythe

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