Education 2020


image credit: Dragan CC BY

Post Factual Times of Magical Thinking –Dec. 14, 2016

[This is the tag line that will begin writings that fall into the strange world after Nov.7th, 2016.]


How do you address the possible futures of education in 2020?  We have a hard time figuring out what’s going to be happening next quarter, let along next year, or four years into the future. This was the task I was given by Campus Consortium for a webinar that took place today.

My preparation was some days of personal reflection, engaging in some email back and forth with colleagues, and reading.  I had 20 min after which there was some Q&A. It’s an odd format to do webinars. Depending on the platform your ability to have any sense of the audience at all is highly variable. In this case I’m told there were 112 logins from all over the world. Not bad, though perhaps not up to my colleague Bryan Alexander’s Future Trends Forum lofty standards 😉  After the presentation some questions from the chat room and some audio questions were fielded and I tried to address them.

Below are the talking points that I used to guide my speaking.


image credit: mayeesherr. future CC BY


  • Post-course Era & Inter-disciplinarity – Problems of today are solved within disciplinary boundaries. This will lead to an increase in inter-disciplinarity of the formal learning experience.
    • We are seeing colleges, schools and collections of departments being reorganized around “challenges”. ASU is a prime example. They might be simply re-instantiated as new business units but the dynamic nature of what are challenges worth addressing will change with much greater fluidity than what we’ve previously had in terms of disciplinary categories. In effect, the infrastructure of units and more importantly the membership of the items or elements in them is an aggregate property defined by the collection or resources and people that have that designation. It’s the inverse of the re-factoring underway today of the learning environment where courses are an emergent characteristic of the individual students who select a topic of study, not a bucket into which students are poured.
    • The emergence of interest in systems like Salesforce is driven in part by the realization of the learner as the organizing principle of university systems, learning or otherwise. The “course” as the organizational unit of learning is fading. It’s still important to be able to have that lens available, but it will no longer be a fundamental building block of the architecture. LMSs that don’t figure this out soon will be relics.
  • Academic Learning/Post-graduation Earning – Institutions, particularly public institutions, have increasing pressures to demonstrate value and accountability. This is leading to the pressure for greater clarity in the connection between the academic learning experience and the collection of capabilities that are developed which map into productive working & earning opportunities post-graduation – this is in the context of the pressure toward the ‘gig’ economy that will be met and shaped by the concerns for social well-being too often sacrificed in this trajectory. Where does this lead? It leads to a reversal of what we have called the ‘hard’ vs. ‘soft skills’
    • It also leads to a recognition that an individual learner must be considered a part of the institution’s student body, if you will, from the time they enroll and continuing for the rest of their lives. Transitioning their role from undergraduate student to ‘alumni’ may make certain marketing sense, but their increasing need to top up their skills, expand their capabilities with recognized certifications or even new degrees, means we need to treat them like core members of the learning community who simply have different tags associated with their current lifecycle status. That might be what we think we’re doing today, but the ease with which these individuals can transition in roles, participate in on-going learning opportunities of varying duration, with and without and accreditation will challenge this notion.
  • Recognition of Learning Achievements (RLA) – learning happens in many places, and in many contexts, not just the classroom. We know that, but we have

    image credit: Phillip Long, Bolonga_University3 CC BY NC SA

    failed to recognize it in sharable, transportable ways. The rise of micro-credentials backed by metadata developed from the badging world provides a pathway towards an “Open Architecture for the Recognition of Learning Achievements”.Behind this is the drive toward extended transcripts and various forms of recognition of achievement collectively referred to as badges, a synonym for the representation of micro-credentials. Like all of these activities, there is a technology component and an even larger instructional delivery and faculty culture components

    • Core elements RLA are:
      • the description of the learning outcome,
      • the rubric by which the achievement is judged or assessed, and
      • the evidence that the learner submits by which the rubric is applied.

      Extending the transcript is in effect transparently giving some insight into the decision rules and evidence of how the summative score or grade was actually determined, in a way that an independent outsider can understand and reasonably judge. Linking to this data is what the extended transcript is all about, and badging systems provide a ready infrastructure to accomplish this, needing only attention to integration.

      • The portability of this record of achievement in the future will be a major issue. Workers are working on average 4.4 years before changing jobs. They will work in something like 15 or more different jobs over the course of their working lifetime. Having to come back to every institution from whom they’ve earned a degree, certificate, CEUs, CMEs, etc., is a nightmare and can’t stand.
      • Enter the blockchain….
  • Growth of Learner Agency – Learners need to build their knowledge, literally and figuratively to be successful across their lifespan. To achieve that institutions will need to provide more integrated and connected experiences that enable students to ‘do the discipline’ instead of either hearing about what the discipline is, or listening to what others have done in it. The results of their achievements need to be associated with the learner, not solely the institution. This is in alignment with greater independence of the future work environment, and the need to construct their view of themselves and their learning achievements to employers and collaborators. It’s absurd that the demonstrating one’s achievements today requires contacting every degree, certificate, and learning or professional program to have those entities send ‘authentic records’ of your learning achievements to potential employers. As mentioned about given today’s average job duration of 4.4 years, this is crazy.

image credit: Phillip Long, CC BY

  • Continued advance & Ethical Challenges of Big Data and Analytics – there is no doubt that the computational capability to analyze big data is just beginning in higher ed. It’s really not “big” in comparison to astronomical data, nuclear physics, or economics. But it is qualitatively large step up in terms of educational data sets.  Serious concerns will need to be met and addressed in terms of privacy, security, and the ethics of the use of this big data. IMSGLOBAL Global Learning Data & Analytics Key Principles.
    • These principles include clarity of ownership of the data of learners. A challenge to many institutions is the assertion that learners own the data generated in the course of interacting with university systems. This a challenge because we act like the institution owns it, but we often say the student or learner owns it. Ownership without the ability to do anything to the data, however, is meaningless.
    • Other principles include
      • stewardship
      • governance,
      • access,
      • interoperability,
      • efficacy,
      • security and privacy, and
      • transparency
    • Team-based Course Development & the Learning Engineer: The collaborative design and development of the technology mediated learning experience is becoming an essential element of group course development and design. Whether in the digital surround to the residential learning environment or a more fully online distributed learning environment the demands of the design process are creating the need for the role of the “Learning Engineer”.
      • This change in design practice is predicated on the recognition that the role a faculty can only be stretched so far. It’s less and less realistic to believe an instructor can be the domain expert in their discipline, a productive researcher in that domain, an instructional designer, a UI expert, a learning scientist and a dynamic presenter. People may have many of these attributes but having them all is unreasonable to assume and difficult to find in practice.
      • What does that mean? It means functions need to be segregated into roles that support the faculty. One of the roles is the learning engineer – that is someone with the multidisciplinary skills of learning sciences, cognitive psychology, learning design along with the computational skills to bring these to a digital learning environment.

image credit: Andrés García, Isolation, CC BY NC

  • Personalized Learning & Social Context – A trend is emerging to meet the learner where they are, not in the mythical median represented by the average student. The ability to gather data, analyze it increasingly in real-time frameworks to provide relevant timely and predictively guided personalized learning pathways is both a holy grail and a chimera. It is appealing to provide desirable difficulties that are framed by the strengths and deficiencies of the learner’s current mind state but we have evolved over tens of thousands of years to be exquisitely social creatures. We have to retain and emphasize the social dimension of learning even in distributed online and so-called personalized learning environments.
    • The challenge here is personalization without isolation. Technology must expose and allow learners to be aware of where other learners are in their learning journeys and facilitate ways for ad hoc group formation that allow peer interaction and study to occur in the context of the intersection of their personalized journey with others.
  • Rise of Openness – the expansion of “open” is now moving beyond its roots in open source software and encroaching on open access (journals/publications), open science, open data, open educational resources and textbooks/publishing, more generally open scholarship. What is emerging is that transparency is an essential element of advancing knowledge, and the network effects of open sharing accelerate discovery, innovation and progress.  This is not a battle between commercial practices and open sharing. It’s about leveraging the two for sustainable strategies that leverage the power of “open”.
  • Exploitation of open: security and identity in an age of evil actors – this is the converse and threat to the power of open. This both a technical challenge and even more a cultural challenge. Protecting one’s identity and avoiding data theft has gotten much harder with the advent of the sloppiness in design of rushed to market IoT devices. The latest exploitation in the DDOS attack on Dyn exposes the fragility of our online infrastructure. Universities can continue to lockdown their services and built virtual moats around their campuses, or they can integrate more sophisticated defenses into the devices that connect them while remaining engaged with the world.

Some examples of technologies in support of these future trends: (these are NOT endorsements but illustrations)

About longpd

I'm a senior scholar at Georgetown University, in CNDLS, a technologist and lapsed evolutionary biologist with an incandescent passion for new modes of seeing and learning.
This entry was posted in Analytics, badges, big_data, blockchains, Futures, innovation, Uncategorized and tagged , , , . Bookmark the permalink.

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