25 Years of Ed Tech: A Reflection on Chapters 9-18

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In this article, I reflect on Chapters nine through eighteen of author Martin Weller’s book “25 Years of Ed Tech. Throughout these Chapters, Weller continues to chronicle the histories of various education technologies. My goal for this post is to identify two central themes from these Chapters: one that reflects relevancy to today’s higher education industry, and another that reflects contradiction by today’s higher education standards. To help illustrate my points, I first need to establish the context in which I read this third of the book.

This past week I was absolutely swamped with grading final exams and prepping fall courses; time was at a premium to say the least. After long and sometimes trying workdays, I turned to Weller’s chapters for some much needed “me” time. However, as I read, and perhaps triggered by my own exhaustion, I found myself thinking “I already know there is major resistance to change within the education industry and that many innovative technologies are tossed or forgotten because of a lack of adoption by the end user”. My entire educational experience, from middle school on, has demonstrated that many learning institutions struggle to keep up with innovative technology and learning practices – it is frustrating as a student, and perhaps even more frustrating as an educator. After contemplating Weller’s writing, I started to think about “why” rather than focusing on the specific technologies covered in each Chapter. Why is widespread adoption of ed tech so difficult to achieve? Why do some students struggle with learning environments while others thrive? Why do some schools continue to use out-dated content and delivery methods? Taking all of this into consideration, I have extracted two central themes that I would like address.

There is no “one size fits all” in education

By chronicling the many shortcomings and successes in the ed tech industry, one could argue that Weller views end-user satisfaction as an underpinning factor that impacts the adoption of education technologies, a perspective, of which, I see as highly relevant in my current work as a post-secondary educator. But how do we effectively accommodate the various competencies of learners, educators, and content creators simultaneously? Is it even possible? Learners, much like educators, possess unique personality traits that predict education and organizational preferences, and it is my opinion that these preferences affect our openness to adopt and implement new education technologies. As Weller points out, current technologies can be implemented to individualize the learning experience. For example, personalized learning environments (PLE’s) can be used to deliver a unique and individualized learning experience to students. Imagine a learning environment that generates specific learning objects and components in accordance to the user’s exact learning preferences. The problem is, nobody has the time to modify the learning environment once courses begin, not the students, nor the teachers. I am no expert in programming, but perhaps artificial intelligence and some form of automation can be developed to aggregate various learning management system components to automatically meet the needs of the student. For example, based on an assessment prior to the course start date, the LMS automatically populates the student’s PLE with all the learning tools they desire to use. Until such automated features come to light in mainstream education, the concept of time will continue to inhibit our ability to truly adopt an individualized learning approach. Until educators and students are afforded the time or capacities to customize the learning experience to truly suit their needs and lifestyles, the failures of the ed tech industry are destined to repeat.

Software Sedimentation is not Universal

Weller suggests many institutions experience great difficulty adapting from one education technology to the next, a phenomenon he refers to as sedimentation (p.65). For example, during my undergraduate program, I experienced first-hand how some colleges use outdated learning management systems and learning software and express absolutely no interest in deviating from such standards – once a practice becomes a norm within an organization, it becomes very difficult to introduce and implement new ideas or concepts. It was evident to me at the time that this was likely due to a lack of resources or personnel, and now that I have had a chance to investigate this from the instructional designer’s perspective, it is clear that this was likely the case.

However common software sedimentation may be, the institution of which I currently teach actively encourages innovation and deviation from previous versions of the school. We conduct weekly meetings with faculty and members of the design team to plan and implement new approaches to content delivery and learning management systems. Whether it be through student accommodation design, user interface updates, additions of new collaborative tools, and so on, we are always striving to implement up-to-date strategies that effectively enhance the learning experiences of our students. The fundamental challenge we are continually faced with, however, is that students and instructors have very little time to customize the experience, which again supports the need to develop automated implementation strategies. In response to the COVID-19 pandemic and resulting serge of online learner enrolment, my institution has taken precedence to remove or update all outdated content and learning objects from our system – we are now, more than ever, focused on moving forwards with innovative technology, a theme, of which, I predict will become more widespread over the next year.

 

References

Weller, M. (2020). 25 years of ed tech. Athabasca University Press. https://doi.org/10.15215/aupress/9781771993050.01

 

 

 

 

3 thoughts on “25 Years of Ed Tech: A Reflection on Chapters 9-18

  1. Jon,

    Let me preface this reply by stating that I will focus on your statement about AI and PLEs.

    I have attempted to use AI to create individualize learning strands (that is what I called them as they worked more like a strand than an all-encompassing environment). I can say the technology is there (for the most part), but we do not have a strong enough understanding of the learning process to implement them effectively. The amount of variables and elements that defined one’s learning experience is astronomical, and even when I attempted to simplify the process to be based on learning objective achievement, I found a few errors. One example is that we can never account for user effort (of course, we could create a coefficient that simulates and predicts possible effort levels, but we need a perfect baseline). This effort coefficient becomes a big problem because the AI views each mark as their possible achievement or giving it their all. Effort is not a static variable and has so many underlining variables that directly or indirectly impact it. The ironic part is that the system can work for consistent students, but, at least in my testing, will most likely fail for inconsistent students who should benefit most from it. I concluded that learning is too messy and that Maslow’s Hierarchy of Needs should be met before any meaningful learning can occur.

    1. Thanks for your reply, Michael. I was hoping someone with a CS background would reply!
      I would really like to explore this topic further as I’m continually faced with the need to automate processes, but am unsure of the current technology. If I could find some time I’d love to go through the literature pertaining to AI and LMS. I am curious to know what areas of the learning process we need to define to help developers, such as yourself, deliver productivity-based AI in Ed tech. How can we package or shrink down the learning variables to become a viable option for AI?

      1. I am most definitely not an expert in AI; I dabble with it, so take what I say with a grain of salt. An AI needs concrete, constant (as in well defined) inputs to help define its actions. The problem with learning is a lot of it is based on variables that are intangible or at least hard to quantify. For example, how do you genuinely quantify learning? Often we attempt to do that through a grade, but is that accurate? Does a grade take into the complex process of learning and even the application of the needed knowledge? Most educators view a grade as a snapshot of an individual at a given moment in time, but even in this view, it becomes hard to accept that as an accurate depiction of learning. Now take all those answers in your head and quantify them for computational consumption, and you can see we have a long way to go. I am not saying it is impossible, but an AI that accurately depicts learning for each individual is more advanced than all the minds put together in educational research, including the past, present, and the foreseeable future.

        Now that may not be what you mean; perhaps you think if a student gets the concept “A” wrong, we reinforce it by requiring them to do lesson “B.” In this case, this would be a simple algorithm that can be handled through a traditional program, but an AI most likely could improve it by helping link like concepts and removing some of the educator’s workload. Which, now that I wrote everything is most likely what you mean. Oh well, my bad!

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