Teaching VR in a MOOC

I’m really excited that we have recently completed the launch of a MOOC Specialisation about Virtual Reality that Xueni Pan and I have been developing with the University of London International Programmes (thank you to their amazing production team). The course will be on Coursera starting on the 25th September.

Developing this course has been an opportunity for me to reflect on my work for the last 15 or more years and bring Xueni and my experience to a new generation of VR developers.

I’ve been very privileged to work with some of the worlds leading VR researchers, mostly during my time at Mel Slater and Anthony Steed’s lab at University College London. It was a very exciting place to work and one that combined the technological development of VR (more my area) with experimental work that established our understanding of the psychological experience of VR leading to Mel’s theory of the three illusions: Place Illusion, Plausibility Illusion and Embodiment Illusion (a key underpinning of our MOOC).

When I left UCL I joined Goldsmiths, University of London and there I was lucky to participate in the development of a new approach to teaching computing, centred on interdisciplinarity and creativity. It was there that I really started thinking about pedagogy: how creative and individual practice can drive people to improve their coding skills.

The final piece of the puzzle was when, in 2013, we worked with Coursera and the University of London International Programmes to develop “Creative Programming” one of the first batch of MOOCs to be released from an English University. Making this MOOC really opened my eyes to the possibility of technology for education, using automatic grading and peer assessment to provide fast, constant feedback to students to improve their learning. This experience has informed both my online and on campus teaching since. The online technologies that I have been able to bring to my students at Goldsmiths have supported their learning and enabled personalised experiences even in very large classes.

The Virtual Reality Specialisation brings all of this learning together. VR feels like such a new medium, but it is one that is founded on decades of research on the technology and the psychology of the VR experience. This specialisation allows Xueni and I to share our experience and knowledge of this research with a new generation of VR creators. People are struggling to understand VR, particularly as it is so different from existing media. There is still a lot we don’t know, but actually, if you look at past research, there are a lot more answers than people think. We hope that our MOOC will help people find those answers and start their career as VR creators. VR right now is crying out for good content, which means good content creators. People making VR now are pioneers in the way film makers were in the early 20th century and web developers were in the 1990s: they are not only creating work, they are creating the basic grammar of the medium itself. We feel very privileged and excited to be able to help people get a start in developing what could be the most important medium of the 21st century.

Designing natural gesture interaction for archaeological data in immersive environments

We’ve published a new paper with colleagues in Pisa:

Archaeological data are heterogeneous and it is difficult to correlate between the different types. Data-sheets and pictures, stratigraphic data and 3D models, time and space mixed together: are only few of the categories a researcher has to deal with. New technologies may be able to help in this process, filling the gap between history and future, and trying to solve research needs with innovative solutions. In this paper, we describe the whole process for the design and development of a prototype application that uses an Immersive Virtual Reality system to acces archaeological excavation 3D data through the Gesture Variation Follower (GVF) algorithm, that allows to recognise which gesture is being performed and how it is performed. Archaeologists participated actively to the design of the interface and the set of gestures used for triggering the different tasks. Interactive machine learning techniques have been used for the real time detection of the gestures. As a case study the agora of Segesta (Sicily, Italy) has been selected. Indeed, due to the complex architectural features and the still on-going fieldwork activities, Segesta represents an ideal context where to test and develop a research approach integrating both traditional and more innovative tools and methods.



Albertini, Niccolò; Brogni, Andrea; Olivito, Riccardo; Taccola, Emanuele; Caramiaux, Baptiste and Gillies, Marco. 22 May 2017.Designing natural gesture interaction for archaeological data in immersive environments. Virtual Archaeology Review, 8(16), pp. 12-21.

Body Language Interaction with Virtual Humans

This is a video of a talk I have at Queen Mary, University of London to the Cognitive Science Research Group.

This talk describes a number of research projects aimed at creating natural non-verbal communications between real users of Virtual Reality and animated virtual characters. It will describe how relatively simple state machine models can be highly effective in creating compelling interactive character, including work with Xueni Pan on the effect of interaction with virtual characters. However, I will also describe how these methods inevitably loose the nuances of embodied human behaviour. I will then describe alternative methods using interactive machine learning to enable people to design  character’s behaviour without coding and a number of future directions.

Social Interaction, Emotion and Body Language in VR: Lessons Learnt from 15 Years of Research

Pan Xueni, Harry Brenton and I will be giving a talk at this year’s DevelopVR conference in London on the 1st December. The talk is called “Social Interaction, Emotion and Body Language in VR: Lessons Learnt from 15 Years of Research“.

Based on our 15 years of VR research, we explain how to create characters whose behaviour feels real and who respond to players. This gives the player a strong illusion of being “together” with another “person” which is not possible without VR.


Human-Centred Machine Learning

We are currently asking for submissions to a workshop on Human-Centred Machine Learning at CHI 2016. The workshop aims to bring together people working on the HCI of machine learning, an emerging filed.

If you are interested in finding out more about Human Centred Machine Learning, here is an extract from our proposal:

Statistical machine learning is one of the most successful areas of computer science research in recent decades. It has driven advances in domains from medical and scientific research to the arts.  It provides people the ability to create new systems based on example data, for instance creating a face recognition system from a large dataset of face images, rather than by reasoning about what features make something a face and translating that reasoning into program code. This makes it possible to provide excellent performance on tasks for which it would be very difficult, if not impossible, to describe computational procedures explicitly in code.

In practice, however, machine learning is still a difficult technology to use, requiring an understanding of complex algorithms and working processes, as well as software tools which may have steep learning curves. Patel et al.  studied expert programmers working with machine learning and identified a number of difficulties, including treating methods as a “black box” and difficulty interpreting results. Usability challenges inherent in both existing software tools and the learning algorithms themselves (e.g., algorithms may lack  a human-understandable means for communicating how decisions are made) restrict who can use machine learning and how. A human-centered approach to machine learning that rethinks algorithms and interfaces to algorithms in terms of human goals, contexts, and ways of working can make machine learning more useful and usable.

Past work also demonstrates ways in which a human-centered perspective leads to new approaches to evaluating, analysing, and understanding machine learning methods (Amershi 2014). For instance, Fiebrink showed that users building gestural control and analysis systems use a range of evaluation criteria when testing trained models, such as decision boundary shape and subjective judgements of misclassification cost. Conventional model evaluation metrics focusing on generalisation accuracy may not capture such criteria, which means that computationally comparing alternative models (e.g., using cross-validation) may be insufficient to identify a suitable model. Users may therefore instead rely on tight action-feedback loops in which they modify model behavior by changing the training data, followed by real-time experimentation with models to evaluate them and inform further modifications. Users may also develop strategies for creating training sets that efficiently guide model behaviour using very few examples (e.g., placing training examples near desired decision boundaries), which results in training sets that may break common theoretical assumptions about data (e.g., that examples are independent and identically distributed). Summarizing related work in a variety of application domains, Amershi et al. enumerate several properties of machine learning systems that can be beneficial to users, such as enabling users to critique learner output, providing information beyond mere example labels, and receiving information about the learner that helped them understand it as more than a “black box.” These criteria are not typically considered when formulating or evaluating learning algorithms in machine learning research.

I’ve also included a full reference list at the bottom of the post. If you are interested here is the Call for Papers and you can find the full proposal here.

Saleema Amershi, Maya Cakmak, W. Bradley Knox, and Todd Kulesza. 2014. Power to the people: The role of human sininteractive machine learning. AI Magazine 35, 4 (2014), 105–120.

Saleema Amershi, James Fogarty, and Daniel S. Weld. 2012. Regroup: Interactive machine learning for on- demand group creation in social networks. In Pro- ceedings of the SIGCHI Conference on Human Fac- tors in Computing Systems (CHI ’12). 21–30. DOI: http://dx.doi.org/10.1145/2207676.2207680

Bill Buxton. 2007. Sketching user experiences: Getting the design right and the right design. Morgan Kauf- mann Publishers Inc., San Francisco, CA, USA.

Steven P. Dow, Alana Glassco, Jonathan Kass, Melissa Schwarz, Daniel L. Schwartz, and Scott R. Klemmer. 2010. Parallel prototyping leads to bet- ter design results, more divergence, and increased self-efficacy. ACM Transactions on Computer- Human Interaction 17, 4 (Dec. 2010), 1–24. DOI: http://dx.doi.org/10.1145/1879831.1879836

Jerry Alan Fails and Dan R. Olsen Jr. 2003. Interactive machine learning. In Proceedings of the International Conference on Intelligent User Interfaces (IUI ’03). 39– 45. DOI:http://dx.doi.org/10.1145/604045.604056

Rebecca Fiebrink. 2011. Real-time human interaction with supervised learning algorithms for music compo- sition and performance. Ph.D. Dissertation. Princeton University, Princeton, NJ, USA.

Andrea Kleinsmith and Marco Gillies. 2013. Customizing by doing for responsive video game characters. International Journal of Human-Computer Studies 71, 7–8 (2013), 775–784. DOI:http://dx.doi.org/10.1016/j. ijhcs.2013.03.005

Todd Kulesza, Saleema Amershi, Rich Caruana, Danyel Fisher, and Denis Charles. 2014. Structured labeling for facilitating concept evolution in machine learning. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14). 3075–3084. DOI:http://dx.doi.org/10.1145/2556288. 2557238

Kayur Patel, James Fogarty, James A. Landay, and Beverly Harrison. 2008. Investigating statistical ma- chine learning as a tool for software development. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’08). 667–676. DOI:http://dx.doi.org/10.1145/1357054.1357160

Novel Dramatic and Ludic Tensions

Nicky Donald is in Copenhagen this week at ICIDS (8th International Conference on Interactive Digital Storytelling) presenting his latest paper Novel Dramatic and Ludic Tensions Arising from Mixed Reality Performance as Exemplified in Better Than Life. Which was based on Better Than Life, a performance we staged last year with Coney.

The paper presents an analysis, from a theatre theory point of view, of the way the performance was able to support new forms of dramatic tension, centred around asymmetries of knowledge and metalepsis (if, like me, you don’t know what that is, well, you’d better read the paper).

Here is the abstract and the full citation and link are below:

We observe that a Mixed Reality Performance called Better Than Life gave rise to novel dramaturgical and ludic possibilities that have not been observed elsewhere. Mixed Reality Performance is an emergent genre that takes many forms, in this case a live experience for a small group of physical participants (PP) and a larger group of online participants (OP). Both groups were offered individual and collective interactions that altered the narrative in real time. A mixed methodology approach to data generated during the performance has identified two key moments where both physical and online participant groups are split into many subgroups by ongoing live events. These events cause tensions that affect the trajectories of participants that make up their experience. Drawing on literary, theatre, cinema and digital game criticism we suggest that the possibilities for engagement in Mixed Reality Performance are exponentially greater than those available to previous media.

Donald, Nicky and Gillies, Marco. 2015. ‘Novel Dramatic and Ludic Tensions Arising from Mixed Reality Performance as Exemplified in Better Than Life’. In: International Conference on Interactive Digital Storytelling. Copenhagen, Denmark.

Better Than Life Final Report

The final report for our Nesta Digital R&D Fund for the Arts project “Better Than Life” has now been published. You can find it here:


This was a collaborative project with Annette Mees and colleagues at Coney and also the (sadly departed) live streaming platform ShowCaster.

This is the executive summary of the report:

We are in a period of significant change. The interconnectivity that the web offers and the quick rise of pervasive media has changed how we communicate with each other, how we access information, how we experience news, stories and the world.

These changes have had a deep impact on storytellers of all kinds. The tools we use to tell tales are evolving, becoming more modular and tailored, more participatory and more engaging than just the printed word or the moving image. These new forms of digitally-enabled storytelling move beyond reinterpreting a text for radio or screen. We need to find new structures, and new relationships with audiences.

Better Than Life, led by Coney, an immersive theatre company that specialises in creating new forms of responsive playing theatre, brought together an extraordinary multidisciplinary team involving award-winning interactive theatre makers, digital broadcasters, developers, multi-platform creatives, academics, VR experts, a magician and many more.

We wanted to create a project that focused, in particular, on how live performance fits into the landscape of this terra nova. The aim was to see how to create an event for a large online audience that combined digital connectivity and interactivity with the liveness and shared experience of theatre.

In particular, we wished to understand what kinds of agency and control audiences might want and enjoy when engaging with this new form of live performance, and we set up a system that allowed both audiences – in the live space and online – to participate in and comment upon the show in several new ways. A total of eight public rehearsals and performances took places in June 2014, with over 300 people taking part either in the live space or online. At the end of the R&D process there emerged a narrative of a new medium. The material in the R&D wasn’t normal theatre and it wasn’t quite broadcast and it wasn’t a game. It was a cultural experience that built on the live-storytelling and visceral nature of theatre, but combined it with the social interaction of MMO (Massively multiplayer online role-playing games) and the delivery infrastructure of online broadcast.

The show was held at a ‘secret’ location in London, with 12 people attending and entering the fictional world of the “Positive Vision Movement” (PVM). In the live space, the audience promenaded through the storyworld of the PVM, following three actors, playing, solving puzzles, chatting, debating and witnessing magic as they went.

Online, people spoke and instructed characters, found commentary, spoke to each other, made choices and switched camera views at will. At points, the online audience could even take control of lighting in the space in order to create specific atmospheres, or shine light on a particular place or person.

In every show the audiences were monitored carefully, questioned at various stages within the show and, in some cases, interviewed in depth about the experience.

Interestingly, interactivity – the ability to ‘take control’ of a situation, make a decision about plot or performance or change the mood through lighting or sound – was not rated as highly, by either audience, as the opportunities to socialise and engage with each other.

Data suggests that the online audience, in particular, enjoyed the ability to form strong social bonds each other, and that they favoured elements of the show in which they were able to connect and communicate directly with performers in the show.

This would suggest that this new kind of hybridised digitally-driven storytelling and play environment is seen first and foremost, as an opportunity to connect with others in a theatrical context – interacting with each other more as one might at a music festival or a house party. This is not then simply theatre with an online component bolted on.

For the three R&D partners, the project was also a great ‘social’ success in terms of what we learned from each other. The project genuinely worked within the gaps of the knowledge overlaps between Coney, Goldsmiths and Showcaster, and we pushed each other to deliver a project with as many interesting new features as we could cram into one production space.

Better Than Life explored what is possible – and proved that hybridised models of entertainment and performance can open up experiences to audiences that genuinely span beyond the geographic boundaries of a single location or building.