Virtual Reality/Augmented Reality

At the end of the semester we hosted a get together of people from across ASU and the local community who are interested in the potential of augmented and virtual reality in education. I started the evening with a talk entitled “The History and Future VR/AR in Education and Training”. The last time I gave a presentation on virtual reality was in 1993 when there was a wave of interest in the concept. Interest soon died, but in a time of much more sophisticated graphical processing power, it has now been reignited. It has also entered the commercial space with products from companies such as Oculus.

The theme of my presentation was almost the same as the one I gave in 1993, which was that there is a history to VR and in this history, the most successful example of its continuous use in learning is flight simulation. The success of flight simulation in learning is not only due soley to the technology, but to  the whole process that surrounds it; a process that parallels some influential theories of learning (cognitive load and deliberate practice).  You have to have the right preparation prior to entering the simulator and the learning requires the appropriate scenario selection, observation, feedback, all under the control of an expert human instructor.

Powerpoint Slides:

Front page of powerpoint in pdf

After the talk, we held a reception featuring several hands-on demonstrations of VR uses in learning. You can see some of the pictures from the event and more information about the demos here.

Picture of VR demo night

2018 ASU Learning Innovation Showcase

We held the third iteration of our ASU learning Innovation Showcase last month, our biggest ever event. The showcase demonstrated innovative work in teaching and learning occurring at ASU (voted the USA’s most innovative university for the last three years) and to connected the participants in new collaborations. The event again garnered great reviews and included all the major ASU centers and labs doing research in this area. We also increased the number of visitors from outside ASU.

We filled a large event space with seventy-none diverse presentations, which included two demonstrations of educational uses of virtual reality. Over 300 people attended.


The list of presentations is available here, where will also find more photos and a video of the event.


In the past decade, interest has emerged in the large amounts of data generated by the increasing use of technology in education and what we can learn from it. Communities of researchers have arisen around the fields of learning analytics and educational data mining to look for patterns in various types of digital activity. Learning Management Systems can track every click a student makes from the time they enroll to the time they leave an educational program using it. Artificial Intelligence (AI) techniques have been developed to analyze the specific online interactions and adapt learning experiences based on this.

Learning analytics research is often purely quantitative in approach and does not provide the full context surrounding student behavior. We can show “what” is happening (e.g. large drop-offs in participation in MOOCs), but not “why” it is happening. The “why” is important if we are to understand the motivations of students and how we can best design interventions to assist them. Artificial intelligence (AI) approaches can analyze how effectively and efficiently students move through a course, but not how satisfied they are with the experience. There is a link to research in social networks, which may lead to a richer set of interventions to support student success. Data science should be complemented with the qualitative methods of social science to help get to deeper issues involved in student interactions with educational experiences.

ASU’s online learning support division EdPlus has developed an Action Lab to set up the data collection infrastructure and coordinate the research expertise to make progress in this relatively new area. It is providing a unique angle to this work by integrating social scientists and data scientists with the goal of using data not only to improve the persistence and course completion of students, but to understand their motivations and how they can be best supported in long-term achievement.

Types of analytics

Realities 360

Over the summer, I participated in the Realities 360 conference. This was a hands-on conference looking at how developing augmented (AR) and virtual realities (VR) can impact learning. Most of the participants were from a commercial rather than academic backgrounds, which is always makes for an interesting change of perspective. Here is a short clip of Maxwell Planck, one of the main presenters, that will give you a flavor of what was discussed:

Along with the presentation there were lots of hands on demos and workshops using various tools.

Although virtual reality is currently at a high level of hype, the concept in various forms has been around for some time ( see this article on the history of VR ). This article dates the concept back to 1930’s science fiction. It is possible to trace the concept even further back than this article does to the use of immersive panoramic paintings.
This picture illustrates the Battle of Borodino panorama created in Moscow in 1911:
Battle of Borodino Panorama

There is also an earlier example in the US at the Gettysburg Museum, which attempts to give a sense of the last day of the civil war battle that took place there.

What was most revealing for me at the conference, was not VR, but the potential for augmented reality, which enables the projection of information and animations on a view of the real world. This is illustrated by a recent demo at the Apple developers conference, which introduced ARKit to app developers. Although initially this will probably be most used for entertainment apps, there is huge potential for creating a variety of learning related apps. I am looking forward to seeing how this develops when it becomes widely available.

End-User Considerations in Educational Technology Design

book cover image

Over the last year, I have had the pleasure of working with Rod Roscoe and Scotty Craig on an edited book project. Rod and Scotty are professors in the Human Factors Engineering program at ASU’s Polytechnic school. The resulting book “End-User Considerations in Educational Technology Design” lays much of the foundation for the definition of the new transdisciplinary field of learning  engineering covered in last month’s post.

You can find out more about the book and its chapters on the publisher’s web site. The book will be released in July.

Learning Engineers


A boat elevator connecting two waterways in Falkirk, Scotland

All around us we see the benefits of a well-developed engineering profession translating the discoveries of modern science into all sorts of useful systems. Learning science is constrained by the fact that we do not have a well-developed equivalent of engineering to compliment and exploit the science. It may be part of the reason why despite producing decades of research in learning,  many classrooms are little different from previous centuries in both the design and the activities that take place in them. (see previous post).

I have heard others make this claim and my answer was always that we have, in the profession of instructional design, the equivalent of the learning engineer (see previous post). The USA is unique in having developed a discipline of instructional design, and many colleges of education produce graduates in this field that are in high demand. However, I have begun to believe that this is not enough. We need a new kind of professional that has skills that traditional colleges of education are not able to provide.

Instructional design was initially inspired by many of the early developments in software engineering. It has not developed as extensively as software engineering; it has been bounded by not being truly cross-disciplinary (Douglas, 2006). It needs to embrace a stronger technological component and have a connection to the emerging field of design science. It needs people skilled in the design of sociotechnical systems, not just the design of individual pieces of educational technology. This involves transforming traditional instructional design by integrating ideas from outside educational colleges such as software engineering, human systems engineering and design schools.

Design thinking has moved away from process heavy models focused on the systems, to models focused on the user experience (see previous post) with systems and the context in which they are used. It was the development of a design-oriented culture focused on user experience that helped to differentiate Apple in the technology sector. There is scope for innovative universities to create new educational programs in the field of learning engineering, a field focused on the conversion of learning research into successful large scale practice. A field that does not just integrate new technologies into existing models of education, but that seeks to transform the whole system. A field that can bridge engineering, education and design into a transdisciplinary expertise that will finally bring learning into the 21st century.


I. Douglas (2006). Issues in software engineering of relevance to instructional design. TechTrends, 50(5): 28–35.

Learning Science

Many people would agree there is a need for radical change in education. There is no shortage of ideas, technologies and research, but despite this what you often see in a twenty-first century classroom is not radically different from what you would see in a nineteenth century classroom. Even with online education, which would seem a radically new approach, the available models are often constrained by that with which instructors are already familiar.

Education is still largely dominated by methods of instructional design and teaching practice that are rooted in pre-internet thinking. Technology is often used to augment, rather than redefine the existing models. There is still a sense that many content-heavy courses should be taken before students can engage in more practically oriented courses. Assessment is still dominated by letter grade based on a broad non-standardized level of attainment in a course.

Learning science has been seen as the research field to help overcome these constraints and provide evidence-based models for more effective forms of learning. Learning science has its roots in cognitive psychology and initially was primarily focused on understanding learning processes in individual humans. More recently it has expanded to cover interests in areas such as learning environments, instructional methods, and the impacts of technology. It could be argued that it has not expanded quickly or broadly enough to cope with the rapid development of new technologies and the need for better models of teaching and learning support.

I think it is important to point out that when we think of learning – we tend to think of it in the context of formal learning in schools. I would say that there are three intersecting domains of life-long learning. In each of these areas there can be formal (e.g. courses) and informal (e.g. conversations with other learners) methods of learning. The study of the intersections of these areas and between the formal and the informal is the first area that needs more attention from researchers.


Over future posts I will highlight other areas where learning science can be expanded and improved.