In an earlier piece on this blog1, Professor Neil Morris gave an excellent overview of the current landscape of blended, open and online learning in Higher Education. If this represents a perspective of what is, or will soon be, and what needs to be done to change our teaching and learning practices, I want to build on this as my contribution by thinking about what it is going to take to achieve this.
The answer, of course, depends to some extent on where you start from: an institution’s context, culture, focus and history will all influence the route forwards. So perhaps specification of an exact solution is not practical, but here I offer three areas where I think all institutions need to ensure they receive the required attention, and if necessary, resources to make changes. They are, I believe, necessary, but perhaps not sufficient conditions to support not just surviving in a digital future, but thriving. At their simplest, these three ideas are about the people who enable and support learning, the digital tools that realize or enhance learning opportunities and the data that is generated from learning interactions.
The teaching and learning side of our roles as academics is getting increasingly complex. Class sizes are getting larger and at the same time more heterogeneous, particularly in the early years of degree programs; expectations of students and employers of graduates are shifting as education becomes more globalized; technology has changed the way we access and interact with information and communicate, but brings with it both challenges to learning as well as opportunities for enhancement. However, it is not just the context and environment around teaching that is changing. The last two decades or so of learning sciences research have helped us understand more about the conditions and practices that support effective learning across disciplines. We know more now than ever before about effective ways to engage students, how to activate their prior knowledge, generate and sustain motivation and interest by providing goal-directed and timely feedback, and strategies to enable and encourage them to develop as independent learners. But addressing these challenges and incorporating these advances can be a continually shifting challenge, not to mention a full-time activity.
I recently spent some time thinking about what are the skills, habits and values an educator in this environment needs to possess to excel in this environment; the anatomy of an educator in a digital age, if you like. Whilst this is certainly not a complete list of attributes, I offer these as a starting point as in conversation with colleagues across various disciplines they seem to have some currency:
- Collaborator: committed to sharing and enhancing one’s own educational approaches within and between disciplines. 3
- Teacher for learning: an understanding of how people learn and how to design effective activities for learning. 2
- Experimenter: an openness to try, reflect on and learn from new approaches, pedagogies and technologies to support student learning.
- Scholar: an awareness and appreciation of effective, research-based and discipline-appropriate pedagogic strategies.
- Technologist: fluency using learning technology in educationally effective ways.
- Curator as well as producer: a producer and consumer of educational resources, through sharing and co-development.
You may have colleagues or collaborators who you think fit this description perfectly: they are the ‘go to’ educators and champions in your Department or on your campus who have made this route their career path, whether or not an official institutional track for doing so exists. But the clearly evident fact is that not every member of academic staff can devote the time and / or have the skills or aptitude to be all of these. That is, in my view, perfectly fine, as long as the support and resource expertise in these areas exists within the institution, is of a quality that matches the institution’s expectations and aspirations, can be easily accessed and that the local culture (institutional, but probably most influentially at the Department level) is willing and open to accept such support from academic service units. Most institutions employ individuals whose job it is to focus on one (or more) of these areas: learning technology professionals who understand how best to leverage various technologies to support student learning; instructional designers who know the literature on principles that support learning that lasts; teaching-focused academic or support specialists who can keep abreast of the literature and developments in and across disciplinary teaching areas, and likely many similar roles.
Of course, the provisioning and organization of such support and resources in a way that is both close to the point of need (the academic staff at the teaching and learning coalface) and is also efficient for the institution (not every department will have a full range of support personnel in-house) is still non-trivial. But I believe it is crucial that institutions seek to develop a complement of teaching staff who embody these skills and values, immersed in a culture of collaboration, supported by high-quality professional support services. Embracing this view has profound implications at all levels within the institution: from hiring committees as they recruit and appoint the next generation of academic and support staff; Heads of Department and Deans setting the strategic direction of their Department and Faculties over their term of office; to institutional leadership in the way that they recognize, reward and celebrate excellence in teaching as a core part of the mission of the institution.
If we look back about a decade or so, the canonical view of the way in which learning technology was used to support teaching and learning revolved around the institutional virtual learning environment (VLE, or Learning Management System (LMS) as it was/ is called across the other side of the pond). A major drive across institutions was rolling out institutionally-supported systems, and persuading and supporting academic staff to make use of the plethora of tools and options within a single monolithic platform that could do it all: deliver content, promote interaction, manage assessment and feedback opportunities. VLE’s have been described as “both ‘it’ but not ‘it’”: useful in some ways yet deficient in others; widespread uptake and adoption mirrored with continual restlessness 4. If physical classrooms are built instances of pedagogy, then VLEs are digital instances of the same, influencing and some would say severely constraining the spaces for digital interaction. Colourful analogies abound: “the walled garden” 5 with respect to integration of outside tools, services and learners; “the minivan of Higher Education” 5 with reference to the basic utility but lack of flair and innovation (“Everyone has them and needs them, but there is a certain shame having one in the driveway”). The North American acronym - LMS - tells the story of what these tools were really good at: the management of learning. Looking back, by far the most prevalent use was as a digital filing cabinet (“The lecture slides are online”).
The relatively short intervening time period has brought enormous changes in the way that technology is now irreversibly woven into the fabric of our personal, social and professional lives. Over 50% of the world’s population now own a powerful mobile computer (that should need to, you can also make phone calls from) 6. This has profoundly shaped their relationship with information (some may say ‘knowledge’) and the ways in which they communicate. Education at all levels has been impacted and deeply affected, in many positive ways but also in troubling ones too. Information is not the same as understanding, and to learn, to really learn and master elements of the higher levels of academic disciplines requires effort, struggle and persistence. This is sharply at odds with the instant delivery of digestible chunks, and some academics have made cogent arguments for making space to really think , free from distractions by leaving laptops closed in class 7. These challenges aside, in the current technology landscape, the monolithic VLE is looking increasingly anachronistic. In many institutions, including my own, we are seeing the overall footprint of learning technology tools and applications expand, but that of the VLE - as the sole provider of a given functionality - decrease over time.
Academic staff have more and more choice over a growing range of tools and applications that recreate elements of existing functionality within a VLE, or provide additional capability not present in the VLE. Interoperability is key, with established mechanisms and accepted standards to be able to manage identities and learning data (e.g. grades) between applications. We have seen dramatic changes in patterns of student engagement in online discussions by swapping out one discussion tool for another that is more richly featured, with content easily structured and navigable: the user-friendliness and functionality of tools really matters. Likewise, we have been able to expand functional capability by consuming external tools within a VLE course space. The walls of the walled garden are breaking down, allowing greater choice and differentiation of tools and approaches by different disciplines. A popular metaphor is now ‘the learning technology ecosystem’: tools and applications are brought in, grow in uptake, may be superseded by others or simply die off. Add in the human element of the people who use these tools (academic staff and students) and those who provide the infrastructure or support for them and the metaphor becomes even more apposite.
However as rapidly as technology and tools change, academic institutions often change much more gradually. These changes present major challenges to models of governance and leadership that might have been put in place a decade ago when the VLE was finding its feet on campus. One aspect I have observed in more than one institution I have worked at is the reinforcement of learning technology as a core part of the academic enterprise, requiring academic, not just technical, leadership. These are tools and services to support faculty and students to achieve teaching and learning goals, an activity that is deeply related to our identity as academics. An ability to be agile and responsive to needs and new applications or approaches is also needed, at a faster pace than many institutions are accustomed to moving. Failure to devote sufficient time to think about how to address these challenges might leave institutions still driving the learning technology equivalent of a minivan.
We are increasingly awash with data and information from the way students interact with learning tools, applications, their peers and their instructors. All of these interactions leave a digital trail, which quickly adds up to an eye-watering amount of data. This ‘big data’ can be utilized in different ways by institutions: academic analytics can provide input into operational decision making processes and is to be distinguished from learning analytics, the main focus here, which aims to directly impact and improve instruction, curriculum and support for students. The data can be used in different ways: asking questions that range from descriptive (‘what is happening?’), through diagnostic (‘why did it happen?’), to predictive (‘what will happen?’)
Learning analytics has great potential for both students and instructors; empowering the former to be able to charge and act to improve their own learning, and allowing the latter to make improvements in course design and delivery. It is a key element if we are to truly realize personalisation of learning at the level of the individual student. All of these various uses require the systematic collection, curation, analysis, and re-presentation / visualization of learning data in ways that teachers and learners find useful, so as to be able to make decisions about how better to support learning and enhance the environment in which it occurs. The current state of most institutions is these requirements are a long way away from what is routinely done. If you stop to consider it, this is curious. We are, in effect, ignoring large quantities of information, which, if suitably analysed, could better enable us to achieve teaching and learning goals 8. Referring to a business context, a recent McKinsey report 9 has suggested that ‘in a big data world, a business that fails to develop its data capabilities will be left behind’.
One reason for the gap between the possible and the actual is that despite all the promise, learning analytics presents a significant implementation challenge, in moving from grass roots innovation projects to institutional strategies that are rolled out widely. The issues to grapple with are complex and cut across wide areas of an institution’s operations and governance:
- Data governance and ethics policies: who collects and has access to what data, and for what purpose?
- Technical infrastructure; secure and reliable, able to deliver on just-in-time querying rather than post hoc data reporting, and capable of capturing learning data from a variety of different sources and platforms (because, as noted above, it’s not all just about the VLE any more).
- Tools and support to query and visualize the data; including specialist data wranglers who can collaborate with academic staff to gain insights into course activities and outcomes.
- Telling the stories: communication of early adopter stories and their impact to build awareness and widen adoption.
This is an area where many institutions have significant catching up to do, but has the real potential to make valuable improvements to student learning. Some institutions that ploughed significant energy and resources into staking a claim in the recent MOOC gold rush may want to reflect on other priorities in the digital education space.
By the middle of this century, we may look back to these early decades of connected, digital learning to distil out the differentiating features and approaches that allowed institutions to thrive in this context. There will likely be different recipes for success, but I would not be surprised to find common ingredients. Paying attention to make sure academic staff have the right support to collaboratively design and deliver learning opportunities, selecting learning tools that align with pedagogical goals, and feeding back the data from learning experiences into the constant improvement cycle may well be three of them.
- Neil Morris (2015) http://addl.ulster.ac.uk/digitalfutures/view/blended-open-and-online-learning, accessed 4th January 2016
- An excellent summary, which I believe should be required reading for all new academic staff is ‘How Learning Works’ by Ambrose, Bridges, DiPietro, Lovett, & Norman, Jossey Bass, 2010;
- This is one way we can, in Lee Shulman’s words, ‘end pedagogical solitude’ by viewing teaching as ‘community property’. See LS Shulman, Change: The Magazine of Higher Learning 25(6) 6-7 (1993).
- M. Brown, J. Dehoney, N. Millichap “The Next Generation Digital Learning Environment: A report on research” (2015) Available online at https://net.educause.edu/ir/library/pdf/eli3035.pdf, accessed 21st Jan 2016.
- Both of these particular analogies come from blog postings by Phil Hill and Michael Feldstein, available at http://mfeldstein.com/opening-lms-walled-garden/ and http://mfeldstein.com/lms-is-the-minivan-of-education-and-other-thoughts-from-lili15/, accessed 3rd February 2016.
- ‘The Mobile Economy’ GSMA, (2015) http://www.gsmamobileeconomy.com/GSMA_Global_Mobile_Economy_Report_2015.pdf, accessed 21st Jan 2016.
- C. Shirky “Why I just asked my students to put their laptops away” (2014) https://medium.com/@cshirky/why-i-just-asked-my-students-to-put-their-laptops-away-7f5f7c50f368#.k09ew8jww, accessed 21st Jan 2016.
- P. Prinsloo and S. Slade, (2013). Learning analytics: ethical issues and dilemmas. American Behavioral Scientist, 57(10) pp. 1509–1528.
- McKinsey Global Institute (2011), “Big Data, the next frontier for innovation, competition and productivity” Available online http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-the-next-frontier-for-innovation, accessed 3rd Feb 2016.