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New Project: Digitizing Higher Education

In fall, I’ll be running a course on edX with a few colleagues on Digitizing Higher Education. This course is part of a larger initiative that I’ll be rolling out later this month focused on helping universities transition into digital systems: University Networks.

Here’s the pitch:

Higher education faces tremendous change pressure and the resulting structures that are now being formed will alter the role of universities in society for the next several generations. The best time to change systems is when it is already experiencing change. A growing number of consulting agencies and service providers are starting to enter the higher education space, bringing visions that are not tightly focused on learner development and service to knowledge advancement in research domains – i.e. a shift to utilitarian views of education. I’m concerned that in the process, universities will lose control over their enterprise and will become some version of corporate lite.

I recognize that universities need to change. They need to start with a basic question: If we were to create a model of higher education today that serves the needs of learners and society, what would it look like given our networked and technologically infused society? . The answer is not pre-existing. It’s something that we need to explore together. Societies and regions that make this change will benefit from increased employment opportunities for citizens, higher quality of life, and greater control over their future.

The project, University Networks, involves working with a small number of universities, or specific faculties and departments, that are committed to rethinking and redesigning how they operate. My goal is to bring on 30 universities and over a period of 4 years, rethink and redesign university operations to align with the modern information and knowledge ecosystem. The intent is to impact 1 million learners over the next four years through offering innovative teaching and learning opportunities, utilizing effective learning analytics models, integrating learning across all spaces of life, and creating a digital and networked mindset to organization operations.

A few details:

  • This is a cohort model where universities learn from each other and share those resources and practices that can be shared – for example, shared curriculum and shared quality rubrics. The cohort model enables more rapid change since the investments of all universities in the network will increase the value of the resources for everyone.
  • We provide centralized consultancy (this is a non-profit) where we enter a university for two weeks of in-depth analysis of existing practices and work with leadership to plan future investments and goals. Once this analysis is done, each university will enter one of ten modules based on their current progress. For example, a university without an LMS will enter module one whereas a university with advanced infrastructure but looking to develop online programs will enter at module four.
  • The shared consultancy and cohort model results in universities working with a fraction of the investment needed in working with a traditional corporation or consultancy firm. Clearly enabling partners will be needed and we’ll support and advise in that area as well. Our focus, however, is on rapid innovation owned and controlled by the university.
  • My motivation for this is twofold: 1. to serve the advancement of science through modern universities that reflect the information age and the changing economy. 2. to actively research systemic transformation in higher education.
  • As partners in university innovation, we (through Interlab) have deep expertise in machine learning, systemic innovation, networked learning, online learning, and digitization of organizations. More on our group here: http://interlab.me/collaboration/. What does this mean? Basically that we are committed to repositioning higher education for the modern era and that we have the skillsets to deliver on that commitment.
  • If you are interested in learning more, please email me: contact me. We are hosting an information event on June 30. We’ll provide more information at that time about the project, getting involved, and our expectation of university partners.

    We have an excellent advisory board directing this project:

  • John Galvin (Intel)
    Dror Ben-Naim (Smart Sparrow)
    Katy Borner (Indiana University)
    Al Essa (McGraw-Hill)
    Casey Green (Campus Computing Project)
    Sally Johnstone (NCHEMS)
    Mark Milliron (Civitas)
    Catherine Ngugi (Open Education Africa)
    Deborah Quazzo (GSV Advisors)
    Matt Sigelman (Burning Glass)

Handbook of Learning Analytics (open)

When we started the learning analytics conference in 2011, we aligned with ACM. We received a fair bit of criticism for not pursuing fully open proceedings. Some came from our sister organization, IEDMS, that has open proceedings. We made a difficult choice to go with the traditional route of quality, indexed proceedings, largely in order to ensure that colleagues from Europe and Latin America could receive funds for their travels. It’s often not understood by advocates for openness that a key challenge for researchers is to publish for impact or publish for prestige. Prestige, as defined by so called “reputable” journals, is often a requirement for getting government funding for travel.

To ensure broader dissemination, and cope with our guilt, of our research, we set up an open journal: Journal for Learning Analytics.

I’m very excited about a new project that started as an idea during LAK13 in Leuven and is another commitment to openness by the Society for Learning Analytics Research: The Handbook of Learning Analytics. The book, CC-licensed, weighs in at 356 pages and provides a good snapshot of the status of learning analytics as a field. It’s a free download (both the book and the chapters). Given the number of masters programs that now incorporate learning analytics courses, or a growing number of LA masters programs, we felt it was important to get a research document into the public space.

Being Human in a Digital Age

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.

Adaptive Learners, Not Adaptive Learning

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

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 :) . It really is a top program. Terrific faculty and tremendous students.

Digital Learning Research Network Conference 2016

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

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?

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 essentially guarantees growing inequality. While it’s painful to think about, I expect a future of greater racial violence, public protests, and radicalized politicians and religious leaders and institutions. Essentially the economic makeup of our society is one where higher education now prevents, rather than enables, improving one’s lot in life.

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

Last year, we settled on a defining question: What does it mean to be human in a digital age? So much of the discussion in society today is founded in a fetish to talk about change. The narrative in media is one of “look what’s changing”. Rarely is the surface level assessment explored to begin looking at “what are we becoming?”. It’s clear that there is much that is changing today: technology, religious upheaval, radicalization, social/ethnic/gender tensions, climate, and emerging super powers. It is an exciting and a terrifying time. The greatest generation created the most selfish generation. Public debt, failing social and health systems, and an eroding social fabric suggest humanity is entering a conflicted era of both turmoil and promise.

We can better heal than any other generation. We can also better kill, now from the comfort of a console. Globally, less people live in poverty than ever before. But income inequality is also approaching historical levels. This inequality will explode as automated technologies provide the wealthiest with a means to use capital without needing to pay for human labour. Technology is becoming a destroyer, not enabler, of jobs. The consequences to society will be enormous, reflective of the “spine of the implicit social contract” being snapped due to economic upheaval. The effects of uncertainty, anxiety, and fear are now being felt politically as reasonably sane electorates turn to solutionism founded in desire rather than reality (Middle East, Austria, Trump in the US to highlight only a few).

In this milieu of social, technology, and economic transitions, I’m interested in understanding our humanity and what we are becoming. It is more than technology alone. While I often rant about this through the perspective of educational technology, the challenge has a scope that requires thinking integratively and across boundaries. It’s impossible to explore intractable problems meaningfully through many of the traditional research approaches where the emphasis is on reducing to variables and trying to identify interactions. Instead, a complex and connected view of both the problem space and the research space is required. Trying to explore phenomena through single variable relationships is not going to be effective in planning

Complex and connected explorations are often seen to be too grandiose. As a result, it takes time for individuals to see the value of integrative, connected, and complex answers to problems that also possess those attributes. Too many researchers are accustomed to working only within their lab or institutions. Coupled with the sound-bite narrative in media, sustained and nuanced exploration of complex social challenges seems almost unattainable. At LINK we’ve been actively trying to distribute research much like content and teaching has become distributed. For example, we have doctoral and post-doctoral students at Stanford, Columbia, and U of Edinburgh. Like teaching, learning, and living, knowledge is also networked and the walls of research need the same thinning that is happening to many classrooms. Learning to think in networks is critical and it takes time, especially for established academics and administrators. What I am most proud of with LINK is the progress we have made in modelling and enacting complex approaches to apprehending complex problems.

In the process of this work, we’ve had many successes, detailed below, but we’ve also encountered failures. I’m comfortable with that. Any attempt to innovate will produce failure. At LINK, we tried creating a grant writing network with faculty identified by deans. That bombed. We’ve put in hundreds of hours writing grants. Many of which were not funded. We were involved in a Texas state liberal arts consortium. That didn’t work so well. We’ve cancelled workshops because they didn’t find the resonance we were expecting. And hosted conferences that didn’t work out so well financially. Each failure though, produced valuable insight in sharpening our focus as a lab. While the first few years were primarily marked by exploration and expansion, we are now narrowing and focusing on those things that are most important to our central emphasis on understanding being human in a digital age.

Grants and Projects

It’s been hectic. And productive. And fun. It has required a growing team of exceptionally talented people – we’ll update bios and images on our site in the near future, but for now I want to emphasize the contributions of many members of LINK. It’s certainly not a solo task. Here’s what we’ve been doing:

1. Digital Learning Research Network. This $1.6m grant (Gates Foundation) best reflects my thinking on knowing at intersections and addressing complex problems through complex and nuanced solutions. Our goal here is to create research teams with R1 and state systems and to identify the most urgent research needs in helping under-represented students succeed.

2. Inspark Education. This $5.2m grant (Gates Foundation) involves multiple partners. LINK is researching the support system and adaptive feedback models required to help students become successful in studying science. The platform and model is the inspiration of the good people at Smart Sparrow (also the PIs) and the BEST Network (medical education) in Australia and the Habworlds project at ASU.

3. Intel Education. This grant ($120k annually) funds several post doctoral students and evaluates effectiveness of adaptive learning as well as the research evidence that supports algorithms that drive adaptive learning.

4. Language in conflict. This project is being conducted with several universities in Israel and looks at how legacy conflict is reflected in current discourse. The goal is to create a model for discourse that enables boundary crossing. Currently, the pilot involves dialogue in highly contentious settings (Israeli and Palestinian students) and builds dialogue models in order to reduce legacy dialogue on impacting current understanding. Sadly, I believe this work will have growing relevance in the US as race discourse continues to polarize rather than build shared spaces of understanding and respect.

5. Educational Discourse Research. This NSF grant ($254k) is conducted together with University of Michigan. The project is concerned with evaluating the current state of discourse research and to determine where this research is trending and what is needed to support this community.

6. Big Data: Collaborative Research. This NSF grant ($1.6m), together with CMU, evaluates the impact of how different architectures of knowledge spaces impacts how individuals interact with one another and build knowledge. We are looking at spaces like wikipedia, moocs, and stack overflow. Space drives knowledge production, even (or especially) when that space is digital.

7. aWEAR Project. This project will evaluate the use of wearables and technologies that collect physiological data as learners learn and live life. We’ll provide more information on this soon, in particular a conference that we are organizing at Stanford on this in November.

8. Predictive models for anticipating K-12 challenges. We are working with several school systems in Texas to share data and model challenges related to school violence, drop out, failure, and related emotional and social challenges. This project is still early stages, but holds promise in moving the mindset from one of addressing problems after they have occurred to one of creating positive, developmental, and supportive skillsets with learners and teachers.

9. A large initiative at University of Texas Arlington is the formation of a new department called University Analytics (UA). This department is lead by Prof Pete Smith and is a sister organization to LINK. UA will be the central data and learning analytics department at UTA. SIS, LMS, graduate attributes, employment, etc. will be analyzed by UA. The integration between UA and LINK is one of improving the practice-research-back to practice pipeline. Collaborations with SAS, Civitas, and other vendors are ongoing and will provide important research opportunities for LINK.

10. Personal Learning/Knowledge Graphs and Learner profiles. PLeG is about understanding learners and giving them control over their profiles and their learning history. We’ve made progress on this over the past year, but are still not at a point to release a “prototype” of PLeG for others to test/engage with.

11. Additional projects:
- InterLab – a distributed research lab, we’ll announce more about this in a few weeks.
- CIRTL – teaching in STEM disciplines
- Coh-Metrix – improving usability of the language analysis tool

Going forward

I know I’ve missed several projects, but at least the above list provides an overview of what we’ve been doing. Our focus going forward is very much on the social and affective attributes of being human in our technological age.

Human history is marked by periods of explosive growth in knowledge. Alexandria, the Academy, the printing press, the scientific method, industrial revolution, knowledge classification systems, and so on. The rumoured robotics era seems to be at our doorstep. We are the last generation that will be smarter than our technology. Work will be very different in the future. The prospect of mass unemployment due to automation is real. Technology is changing faster than we can evolve individually and faster than we can re-organize socially. Our future lies not in our intelligence but in our being.

But.

Sometimes when I let myself get a bit optimistic, I’m encouraged by the prospect of what can become of humanity when our lives aren’t defined by work. Perhaps this generation of technology will have the interesting effect of making us more human. Perhaps the next explosion of innovation will be a return to art, culture, music. Perhaps a more compassionate, kinder, and peaceful human being will emerge. At minimum, what it means to be human in a digital age has not been set in stone. The stunning scope of change before us provides a rare window to remake what it means to be human. The only approach that I can envision that will help us to understand our humanness in a technological age is one that recognizes nuance, complexity, and connectedness and that attempts to match solution to problem based on the intractability of the phenomena before us.

The Godfather: Gardner Campbell

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:

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

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:

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 that influence how education systems are structured, even when we don’t like all of the players in the network.

I’m worried that those who have the greatest passion for an equitable world and a just society are not involved in the conversations that are shaping the future of learning. I continue to hear about the great unbundling of education. My fear is the re-bundling where new power brokers enter the education system with a mandate of profit, not quality of life.

We must be integrative thinkers, integrative doers. I’m interested in working and thinking with people who share my values, even when we have different visions of how to realize those values.

Slides from my talk today are below: