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Updated: 2016-09-02T15:55:12Z

 



Being Human in a Digital Age

2016-09-02T15:55:12Z

I’m exploring what it means to be human in a digital age and what role universities play in developing learners for this experience. Against the backdrop of everything is changing, we aren’t paying enough attention to what we are becoming. The Becoming is the central role of education in a machine learning, artificial intelligence era. [...]

I’m exploring what it means to be human in a digital age and what role universities play in developing learners for this experience. Against the backdrop of everything is changing, we aren’t paying enough attention to what we are becoming. The Becoming is the central role of education in a machine learning, artificial intelligence era. It’s great to see people like Michael Wesch exploring the formative aspect of education. Randy Bass’s work on Formation by Design is also notable and important.

I spent a few weeks in Brisbane recently working with the Faculty of Health on digital learning and how to prepare the higher education system for this new reality. On my final presentation, I focused on the needs of learners in this environment and what we need to focus on to help develop their capabilities to be adaptive and respond to continual changes. Slides are below.

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Adaptive Learners, Not Adaptive Learning

2016-07-20T21:00:48Z

Some variation of adaptive or personalized learning is rumoured to “disrupt” education in the near future. Adaptive courseware providers have received extensive funding and this emerging marketplace has been referred to as the “holy grail” of education (Jose Ferreira at an EdTech Innovation conference that I hosted in Calgary in 2013). The prospects are tantalizing: [...]Some variation of adaptive or personalized learning is rumoured to “disrupt” education in the near future. Adaptive courseware providers have received extensive funding and this emerging marketplace has been referred to as the “holy grail” of education (Jose Ferreira at an EdTech Innovation conference that I hosted in Calgary in 2013). The prospects are tantalizing: each student receiving personal guidance (from software) about what she should learn next and support provided (by the teacher) when warranted. Students, in theory, will learn more effectively and at a pace that matches their knowledge needs, ensuring that everyone masters the main concepts. The software “learns” from the students and adapts the content to each student. End result? Better learning gains, less time spent on irrelevant content, less time spent on reviewing content that the student already knows, reduced costs, tutor support when needed, and so on. These are important benefits in being able to teach to the back row. While early results are somewhat muted (pdf), universities, foundations, and startups are diving in eagerly to grow the potential of new adaptive/personalized learning approaches. Today’s technological version of adaptive learning is at least partly an instantiation of Keller’s Personalized System of Instruction. Like the Keller Plan, a weakness of today’s adaptive learning software is the heavy emphasis on content and curriculum. Through ongoing evaluation of learner knowledge levels, the software presents next step or adjacent knowledge that the learner should learn. Content is the least stable and least valuable part of education. Reports continue to emphasize the automated future of work (pfdf). The skills needed by 2020 are process attributes and not product skills. Process attributes involve being able to work with others, think creatively, self-regulate, set goals, and solve complex challenges. Product skills, in contrast, involve the ability to do a technical skill or perform routine tasks (anything routine is at risk for automation). This is where adaptive learning fails today: the future of work is about process attributes whereas the focus of adaptive learning is on product skills and low-level memorizable knowledge. I’ll take it a step further: today’s adaptive software robs learners of the development of the key attributes needed for continual learning – metacognitive, goal setting, and self-regulation – because it makes those decisions on behalf of the learner. Here I’ll turn to a concept that my colleague Dragan Gasevic often emphasizes (we are current writing a paper on this, right Dragan?!): What we need to do today is create adaptive learners rather than adaptive learning. Our software should develop those attributes of learners that are required to function with ambiguity and complexity. The future of work and life requires creativity and innovation, coupled with integrative thinking and an ability to function in a state of continual flux. Basically, we have to shift education from focusing mainly on the acquisition of knowledge (the central underpinning of most adaptive learning software today) to the development of learner states of being (affect, emotion, self-regulation, goal setting, and so on). Adaptive learners are central to the future of work and society, whereas adaptive learning is more an attempt to make more efficient a system of learning that is no longer needed. [...]



Doctor of Education: Athabasca University

2016-07-15T14:36:29Z

Athabasca University has the benefit of offering one of the first doctor of education programs, fully online, in North America. The program is cohort-based and accepts 12 students annually. I’ve been teaching in the doctorate program for several years (Advanced Research Methods as well as, occasionally, Teaching & Learning in DE) and supervise 8 (?!) [...]

Athabasca University has the benefit of offering one of the first doctor of education programs, fully online, in North America. The program is cohort-based and accepts 12 students annually. I’ve been teaching in the doctorate program for several years (Advanced Research Methods as well as, occasionally, Teaching & Learning in DE) and supervise 8 (?!) doctoral students currently.

Applications for the fall 2017 start are now being accepted with a January 15, 2017 deadline. Just in case you’re looking to get your doctorate (image) . It really is a top program. Terrific faculty and tremendous students.




Digital Learning Research Network Conference 2016

2016-06-21T17:35:45Z

As part of the Digital Learning Research Network, we held our first conference at Stanford last year. The conference focused on making sense of higher education. The discussions and prsentations addressed many of the critical challenges faced by learners, educators, administrators, and others. The schedule and archive are available here. This year, we are hosting [...]

As part of the Digital Learning Research Network, we held our first conference at Stanford last year.

The conference focused on making sense of higher education. The discussions and prsentations addressed many of the critical challenges faced by learners, educators, administrators, and others. The schedule and archive are available here.

This year, we are hosting the 2nd dLRN conference in downtown Fort Worth, October 21-22 The conference call for papers is now open. I’m interested in knowledge that exists in the gaps between domains. For dLRN15, we wanted to socialize/narrativize the scope of change that we face as a field.

The framework of changes can’t be understood through traditional research methods. The narrative builds the house. The research methods and approaches furnish it. Last year we started building the house. This year we are outfitting it through more traditional research methods. Please consider a submission (short, relatively pain free). Hope to see you in Fort Worth, in October!

We have updated our dLRN research website with the current projects and related partners…in case you’d like an overview of the type of research being conducted and that will be presented at #dLRN16. The eight projects we are working on:

1. Collaborative Reflection Activities Using Conversational Agents
2. Onboarding and Outcomes
3. Mindset and Affect in Statistical Courses
4. Online Readiness Modules and Student Success
5. Personal Learning Graphs
6. Supporting Team-Based Learning in MOOCs
7. Utilizing Datasets to Collaboratively Create Interventions
8. Using Learning Analytics to Design Tools for Supporting Academic Success in Higher Education




Announcing: aWEAR Conference: Wearables and Learning

2016-06-09T15:15:23Z

Over the past year, I’ve been whining about how wearable technologies will have a bigger impact on how we learn, communicate, and function as a society than mobile devices have had to date. Fitness trackers, smart clothing, VR, heart rate monitors, and other devices hold promising potential in helping understand our learning and our health. [...]

Over the past year, I’ve been whining about how wearable technologies will have a bigger impact on how we learn, communicate, and function as a society than mobile devices have had to date. Fitness trackers, smart clothing, VR, heart rate monitors, and other devices hold promising potential in helping understand our learning and our health. They also hold potential for misuse (I don’t know the details behind this, but the connection between affective states with nudges for product purchases is troubling).

Over the past six months, we’ve been working on pulling together a conference to evaluate, highlight, explore, and engage with prominent trends in wearable technologies in the educational process. The http://awear.interlab.me“>aWEAR conference will be held Nov 14-15 at Stanford. The call for participation is now open. Short abstracts, 500 words, are due by July 31, 2016. We are soliciting conceptual, technological, research, and implementation papers. If you have questions or are interested in sponsoring or supporting the conference, please send me an email

From the site:

The rapid development of mobile phones has contributed to increasingly personal engagement with our technology. Building on the success of mobile, wearables (watches, smart clothing, clinical-grade bands, fitness trackers, VR) are the next generation of technologies offering not only new communication opportunities, but more importantly, new ways to understand ourselves, our health, our learning, and personal and organizational knowledge development.

Wearables hold promise to greatly improve personal learning and the performance of teams and collaborative knowledge building through advanced data collection. For example, predictive models and learner profiles currently use log and clickstream data. Wearables capture a range of physiological and contextual data that can increase the sophistication of those models and improve learner self-awareness, regulation, and performance.

When combined with existing data such as social media and learning management systems, sophisticated awareness of individual and collaborative activity can be obtained. Wearables are developing quickly, including hardware such as fitness trackers, clothing, earbuds, contact lens and software, notably for integration of data sets and analysis.

The 2016 aWEAR conference is the first international wearables in learning and education conference. It will be held at Stanford University and provide researchers and attendees with an overview of how these tools are being developed, deployed, and researched. Attendees will have opportunities to engage with different wearable technologies, explore various data collection practices, and evaluate case studies where wearables have been deployed.




What does it mean to be human in a digital age?

2016-05-23T11:46:39Z

It has been about 30 months now since I took on the role to lead the LINK Research Lab at UTA. (I have retained a cross appointment with Athabasca University and continue to teach and supervise doctoral students there). It has taken a few years to get fully up and running – hardly surprising. I’ve [...]It has been about 30 months now since I took on the role to lead the LINK Research Lab at UTA. (I have retained a cross appointment with Athabasca University and continue to teach and supervise doctoral students there). It has taken a few years to get fully up and running – hardly surprising. I’ve heard explanations that a lab takes at least three years to move from creation to research identification to data collection to analysis to publication. This post summarizes some of our current research and other activities in the lab. We, as a lab, have had a busy few years in terms of events. We’ve hosted numerous conferences and workshops and engaged in (too) many research talks and conference presentations. We’ve also grown significantly – from an early staff base of four people to expected twenty three within a few months. Most of these are doctoral or post doctoral students and we have a terrific core of administrative and support staff. Finding our Identity In trying to find our identity and focus our efforts, we’ve engaged in numerous activities including book clubs, writing retreats, innovation planning meetings, long slack/email exchanges, and a few testy conversations. We’ve brought in well over 20 established academics and passionate advocates as speakers to help us shape our mission/vision/goals. Members of our team have attended conferences globally, on topics as far ranging as economics, psychology, neuroscience, data science, mindfulness, and education. We’ve engaged with state, national, and international agencies, corporations, as well as the leadership of grant funding agencies and major foundations. Overall, an incredible period of learning as well as deepening existing relationships and building new ones. I love the intersections of knowledge domains. It’s where all the fun stuff happens. As with many things in life, the most important things aren’t taught. In the past, I’ve owned businesses that have had an employee base of 100+ personnel. There are some lessons that I learned as a business owner that translate well into running a research lab, but with numerous caveats. Running a lab is an entrepreneurial activity. It’s the equivalent of creating a startup. The intent is to identify a key opportunity and then, driven by personal values and passion, meaningfully enact that opportunity through publications, grants, research projects, and collaborative networks. Success, rather than being measured in profits and VC funds, is measured by impact with the proxies being research funds and artifacts (papers, presentations, conferences, workshops). I find it odd when I hear about the need for universities to be more entrepreneurial as the lab culture is essentially a startup environment. Early stages of establishing a lab are chaotic. Who are we? What do we care about? How do we intersect with the university? With external partners? What are our values? What is the future that we are trying to create through research? Who can we partner with? It took us a long time to identify our key research areas and our over-arching research mandate. We settled on these four areas: new knowledge processes, success for all learners, the future of employment, and new knowledge institutions. While technologies are often touted as equalizers that change the existing power structure by giving everyone a voice, the reality is different. In our society today, a degree is needed to get a job. In the USA, degrees are prohibitively expensive to many learners and the result is a type of poverty lock-in that essentia[...]



The Godfather: Gardner Campbell

2016-05-18T21:52:38Z

Gardner Campbell looms large in educational technology. People who have met him in person know what I mean. He is brilliant. Compassionate. Passionate. And a rare visionary. He gives more than he takes in interactions with people. And he is years ahead of where technology deployment current exists in classrooms and universities. He is also [...]Gardner Campbell looms large in educational technology. People who have met him in person know what I mean. He is brilliant. Compassionate. Passionate. And a rare visionary. He gives more than he takes in interactions with people. And he is years ahead of where technology deployment current exists in classrooms and universities. He is also a quiet innovator. Typically, his ideas are adopted by other brash, attention seeking, or self-serving individuals. Go behind the bravado and you’ll clearly see the Godfather: Gardner Campbell. Gardner was an originator of what eventually became the DIY/edupunk movement. Unfortunately, his influence is rarely acknowledged. He is also the vision behind personal domains for learners. I recall a presentation that Gardner did about 6 or 7 years ago where he talked about the idea of a cpanel for each student. Again, his vision has been appropriated by others with greater self-promotion instincts. Behind the scenes, however, you’ll see him as the intellectual originator. Several years ago, when Gardner took on a new role at VCU, he was rightly applauded in a press release: Gardner’s exceptional background in innovative teaching and learning strategies will ensure that the critical work of University College in preparing VCU students to succeed in their academic endeavors will continue and advance…Gardner has also been an acknowledged leader in the theory and practice of online teaching and education innovation in the digital age And small wonder that VCU holds him in such high regard. Have a look at this talk: width="560" height="315" src="https://www.youtube.com/embed/FaYie0guFmg" frameborder="0" allowfullscreen> Recently I heard some unsettling news about position changes at VCU relating to Gardner’s work. In true higher education fashion, very little information is forthcoming. If anyone has updates to share, anonymous comments are accepted on this post. There are not many true innovators in our field. There are many who adopt ideas of others and popularize them. But there are only a few genuinely original people doing important and critically consequential work: Ben Werdmuller, Audrey Watters, Stephen Downes, and Mike Caulfield. Gardner is part of this small group of true innovators. It is upsetting that the people who do the most important work – rather than those with the loudest and greatest self-promotional voice – are often not acknowledged. Does a system like VCU lack awareness of the depth and scope of change in the higher education sector? Is their appetite for change and innovation mainly a surface level media narrative? Leadership in universities has a responsibility to research and explore innovation. If we don’t do it, we lose the narrative to consulting and VC firms. If we don’t treat the university as an object of research, an increasingly unknown phenomena that requires structured exploration, we essentially give up our ability to contribute to and control our fate. Instead of the best and brightest shaping our identity, the best marketers and most colourful personalities will shape it. We need to ensure that the true originators are recognized and promoted so that when narrow and short-sighted leaders make decisions, we can at least point them to those who are capable of lighting a path. Thanks for your work and for being who you are Gardner. [...]



The Future of Learning: Digital, Distributed, Data-Driven

2016-05-12T13:07:30Z

Yesterday as I was traveling (with free wifi from the good folks at Norwegian Air, I might add), I caught this tweet from Jim Groom: @dkernohan @cogdog @mweller A worthwhile think piece for sure, almost up there with "China is My Analytics Co-Pilot" — Jim Groom (@jimgroom) May 11, 2016 The comment was in response [...]Yesterday as I was traveling (with free wifi from the good folks at Norwegian Air, I might add), I caught this tweet from Jim Groom: @dkernohan @cogdog @mweller A worthwhile think piece for sure, almost up there with "China is My Analytics Co-Pilot" — Jim Groom (@jimgroom) May 11, 2016 The comment was in response to my previous post where I detailed my interest in understanding how learning analytics were progressing in Chinese education. My first internal response was going to be something snarky and generally defensive. We all build in different ways and toward different visions. It was upsetting to have an area of research interest be ridiculed. Cause I’m a baby like that. But I am more interested in learning than in defending myself and my interests. And I’m always willing to listen to the critique and insight that smart people have to offer. This comment stayed with me as I finalized my talk in Trondheim. What is our obligation as educators and as researchers to explore research interests and knowledge spaces? What is our obligation to pursue questions about unsavoury topics that we disagree with or even find unethical? Years ago, I had a long chat with Gardner Campbell, one of the smartest people in the edtech space, about the role of data and analytics. We both felt that analytics has a significant downside, one that can strip human agency and mechanize the learning experience. Where we differed was in my willingness to engage with the dark side. I’ve had similar conversations with Stephen Downes about change in education. My view is that change happens on multiple strands. Some change from the outside. Some change from the inside. Some try to redirect movement of a system, others try to create a new system altogether. My accommodating, Canadian, middle child sentiment drives my belief that I can contribute by being involved in and helping to direct change by being a researcher. As such, I feel learning analytics can play a role in education and that regardless of what the naysayers say, analytics will continue to grow in influence. I can contribute by not ignoring the data-centric aspects in education and engage them instead and then attempting to influence analytics use and adoption so that it reflects the values that are important for learners and society. Then, during the conference today, I heard numerous mentions of people like Ken Robinson and the narrative of creativity. Other speaking-circuit voices like Sugata Mitra were frequently raised as well. This lead to reflection about how change happens and why many of the best ideas don’t gain traction and don’t make a systemic level impact. We know the names: Vygostky, Freire, Illich, Papert, and so on. We know the ideas. We know the vision of networks, of openness, of equity, and of a restructured system of learning that begins with learning and the learner rather than content and testing. But why doesn’t the positive change happen? The reason, I believe, is due to the lack of systems/network-level and integrative thinking that reflects the passion of advocates AND the reality of how systems and networks function. It’s not enough to stand and yell “creativity!” or “why don’t we have five hours of dance each week like we have five ours of math”. Ideas that change things require an integrative awareness of systems, of multiple players, and of the motivations of different agents. It is also required that we are involved in the power-shaping networks th[...]



Reflecting on Learning Analytics and SoLAR

2016-04-29T13:55:01Z

The Learning Analytics and Knowledge conference (LAK16) is happening this week in Edinburgh. I unfortunately, due to existing travel and other commitments, am not in attendance. I have great hope for the learning analytics field as one that will provide significant research for learning and help us move past naive quantitative and qualitative assessments of [...]The Learning Analytics and Knowledge conference (LAK16) is happening this week in Edinburgh. I unfortunately, due to existing travel and other commitments, am not in attendance. I have great hope for the learning analytics field as one that will provide significant research for learning and help us move past naive quantitative and qualitative assessments of research and knowledge. I see LA as a bricolage of skills, techniques, and academic/practitioner domains. It is a multi-faceted approach of learning exploration and one where anyone with a stake in the future of learning can find an amenable conversation and place to research. Since I am missing LAK16, and feeling nostalgic, I want to share my reflections of how LAK and the Society for Learning Analytics Research (SoLAR) became the influential agencies that they now are in learning research. Any movement has multiple voices and narratives so my account here is narrow at best. I am candid in some of my comments below, detailing a few failed relationships and initiatives. If anyone reading this feels I have not been fair, please comment. Alternatively, if you have views to share that broaden my attempt to capture this particular history, please add them below. How we got started On March 14, 2010, I sent the following email to a few folks in my network (Alec Couros, Stephen Downes, Dave Cormier, Grainne Conole, David Wiley, Phil Long, Clarence Fisher, Tony Hirst, and Martin Weller. A few didn’t respond and those that joined didn’t stay involved, with the exception of Phil): As more learning activities occur online, learners produce growing amounts of data. All that data cries out to be parsed, analyzed, interrogated, tortured, and visualized. The data being generated could provide valuable insight into teaching and learning practices. Over the last few years, I’ve been promoting data visualization as an important trend in understanding learners, the learning process, and as an indicator of possible interventions. Would you be interested in participating in a discussion on educational analytics (process, methods, technologies)? I imagine we could start this online with a few elluminate meetings, but I think a f2f gathering later this year (Edmonton is lovely, you know) would be useful. (Clarence, Alec, and I tackled this topic about three years ago, but we didn’t manage to push it much beyond a concept and a blog ). At the same time, I sent an email to colleagues in TEKRI (Rory McGreal, Kinshuk, and Dragan Gasevic) asking if this could be supported by Athabasca University. Dragan promptly replied stating that “I can say that most of the things we are doing with semantic technologies are pretty much related to analytics and I would be quite interest in such an event”. Then he told me that my plan for a conference in fall 2010 were completely unrealistic asking “[who] would be a potential participant? How we can get any audience in December?”. Dragan and Shane Dawson, who I connected with through a comment on this blog, are two critical connections and eventually friends. Except Shane. He is mean and has relationship issues. SoLAR would not exist without their involvement. Another important connect was Ryan Baker. Ryan started the International Educational Datamining Society a few years earlier. The fact that Ryan was willing to assist in the formation of a possibly competing organization speaks volumes about his desire to have rich scientific dis[...]



Open Learning Analytics. Again

2016-02-26T03:05:26Z

Several years ago, a group of us wrote a concept paper on Open Learning Analytics (.pdf). Our goal was to create openness as a foundation for the use of data and analytics in education. We have, it appears, largely failed to have our vision take root. Few things are more important in education today than [...]

Several years ago, a group of us wrote a concept paper on Open Learning Analytics (.pdf). Our goal was to create openness as a foundation for the use of data and analytics in education. We have, it appears, largely failed to have our vision take root.

Few things are more important in education today than the development of an open platform for analytics of learning data. It’s a data-centric world. Data, and the analysis of those data, are a rapidly emerging economic value layer. Most educators and students are unaware of how much algorithmic sorting happens in the educational process. Even before students apply to a university, the sorting has started (postal/ZIP codes can indicate chances of success). Recommender systems suggest next courses. Engagement with course content produces predictive models. Suggested help resources are generated for students identified to be at risk. And this all happens behind the scenes as the Wizard of Algorithms spins dials and outputs intimidating results (often with more smoke and noise than actual usefulness) that are starting to drive learning practices that cover the full range of a student’s engagement with higher education.

We are, as a field, facing an interesting time. The decisions that we make now will cast a long shadow into the future. And the best decision, in uncertain times, is the one that allows the greatest range of decisions in the future. It is here, in analytics and data use in education, that far more attention and awareness is needed than is currently evident. Algorithms will subsume most of our educational practices as they will embody certain pedagogies, support roles, and even faculty practices. Quite simply, the shape of tomorrow’s university is now actively being coded into analytics models. I’m generally fine with this as a concept, but quite nervous about this as an action. The future needs to be open. And yet, the exact opposite is happening.

The article in the Chronicle today on Big Data and Education is timely reminder of the importance of the work and the challenges of a closed learning analytics future. The work is rather urgent. And we as academics have been sleeping.