Although I’m trying to retire, or at least ease up a little, 2018 turned out to be a really busy work year for me. In the end, my various activities did allow me to get a pretty good view of what’s going on in online learning, at least in Canada, although any report such as this is going to be partial and personal.

The basis for my review of 2018

During the year, I gave

  • 11 keynotes at conferences in BC, Alberta, Ontario, Québec and the U.K
  • 5 webinars (for organizations in BC, Québec, Europe, Nova Scotia and across Canada)
  • and attended two other conferences/workshops (One in BC, the other in Ontario)

I also met with instructors and faculty from two universities, two colleges and one university college in Manitoba for Contact North’s Pockets of Innovation, and was heavily engaged in the 2018 national survey of online learning in Canadian post-secondary institutions. I also did 68 blog posts in the last 12 months.

These various activities covered a range of different topics and enabled me to meet and talk with many engaged in online learning in Canada.

Instead though of just giving a list of ‘events’, I thought I would try to summarise what I saw as the most significant developments in 2018, including those that did not come up to my initial expectations. For each, I give three ‘scores’, each out of 5, with 1 being low:

  • the hype factor (as the ‘media’ see it)
  • what I think is the significance for online learning
  • level of engagement about the topic on the part of readers of my blog.

I’ll start with the developments that have had in my view the least impact on online learning in 2018, ending with the most significant.

Artificial intelligence

Despite the hype around artificial intelligence, nothing really significant occurred with regard to the use of AI in online learning during 2018. Where it was used, it was in areas such as student services or administrative applications, but did not really affect much actual teaching.

AI was hardly mentioned by the institutions that responded to the 2018 survey, and a recent report on AI in higher education came up with few significant applications. The most read blog post on this topic came 25th out of 68 in terms of ‘hits’, despite my having done several posts on the topic during 2018.

However, I think there is a sleeping giant here. Applications of AI in online learning are probably not going to come from the mainstream universities and colleges, but from outside the formal post-secondary system, through organizations such as LinkedIn, or Coursera, that have access to large data sets that make the applications of AI scalable and worthwhile (to them).

These developments could in the long run turn out to be highly significant for post-secondary education. This is not to say that such developments will be ‘a good thing.’ We need in 2019 to look much more carefully and knowledgeably at the ethical and value-laden aspects of AI in education, but I suspect that AI is still a few years away from really impacting on mainstream online education – but it will.

Hype: 5; Significance: 3; Reader engagement: 1

Synchronous online learning

As online learning expands in Canada, so does the use of synchronous online teaching, mainly in the form of online video lectures. Roughly 60% of institutions now are using online, live lectures for both fully online and blended courses, according to the 2018 survey.This is a significant move away from asynchronous learning based primarily on the use of learning management systems. This is not to say that LMSs are not also significant, they are, with nearly all institutions making use of them, but their role is now becoming more of a support technology to live lectures.

This too is not necessarily a good thing. The quality course design that often went into fully online asynchronous courses goes out the window, with overlong lectures and poor quality teaching replacing structured, well-organized asynchronous courses, and result in less flexibility for students, at least in terms of time. The growth of synchronous online teaching is however a response to the increasing use of online learning both for distance education and to support classroom teaching, enabling it to be scaled up.

Next year, institutions need to look carefully at how students are responding to this tendency. Wouldn’t some comparative research on this be useful? However, I am not too concerned about this. I am banking on instructors learning from experience that 50 minute lectures or longer won’t work and will gradually move to more pedagogically sound online designs – but then I am just a naive optimist. 

What is interesting is the interest of my blog readers in this area. A post discussing the future of Blackboard came 25th in the number of hits, and another post on the limitations of LMSs received over 1,000 hits. I wrote though virtually nothing myself on synchronous lectures – a note to myself for next year.

Hype: 2; significance: 4; reader engagement: 3

Up next

I will need at least two more posts to cover the significance of developments in the following areas during 2018:

  • open universities and OER
  • virtual reality
  • serious games
  • weak leadership
  • blended and hybrid learning
  • pedagogies for digital learning

In the meantime, use the comment box to let me know your views on the significance of AI and online live lectures during 2018. Do you agree with my conclusions? What has been your experience in 2018?


  1. I’m interested to see how ‘AI’ or ‘ML’ (machine learning) techniques might be applied to the ‘overly long 2 hour lecture capture’ style videos.

    In (some) regular asynchronous online courses a lot of effort and time is put into crafting engaging instructional video that synthesises subject material, adding additional content using animation or captioning, translation, as well as in-video quizzing and so on. Obviously, this can be time consuming and costly.

    Whereas, the lengthy traditional lecture contains a wide range of valuable contextual information that generally only makes for good learning when it’s on-point and the learner is engaged throughout, a tall ask.

    However, a suitably designed and smart system that can chunk video into ‘digestible’ pieces or components (LO’s) could be one way to accomplish more with less, aside of course from the design and development of the system in the first place!

    Right now lecture capture systems such as Panopto allow quite comprehensive in-video text search, slide OCR, and some forms of chunking and indexing. Taking this type of capability further by performing a more semantically aware analysis of the content, on-the-fly contents lists could be created as well as on-demand chunking or chaptering that doesn’t merely bring the learner directly to a keyword within a 1 hour lecture, but somehow manages to extract a segment from the same 1 hour video that can provide usable context to the learner. Taking this idea further still, on-the-fly segments could also surface text blocks or hyperlinks taking the user backwards or forwards to relevant contextual points of reference within a video lecture. On demand content surfacing or instructor provided content navigation created in a quick and easy manner would be one key objective of such a system.

    The huge corpus’ of lecture and instructional video that universities are now capturing, live or synchronous material, must be set for deep learning or machine learning techniques to take advantage of the rich material usually lost through excessive summarization in the shape of short attention span 5-8 minute videos, or through lack of attention due to the tediousness of watching entire re-run video lectures. I believe an emerging capability of this sort equally applies to audio, to podcast materials and audio only learning episodes. Universities could field synchronous online lectures while simultaneously capturing video for intelligent repurposing later by asynchronous users.

    Further, it will require that leading UI and UX experts are included in the design of such systems in order to create the intuitive mobile, desktop and audio interfaces that will be needed to easily navigate through subject and content knowledge, with the aim of bringing to the learner (just-in-time?) tailored video and audio content. Ultimately such ‘smart’ video and audio corpus could and should be open, further enabling intelligent re-use and mash up of these learning materials, episodes or segments for intuitive use by learners at all levels.

    Ambitious? I guess so ! Better get started 🙂 lol.


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