Learning is all about feedback. Runners, for example, use apps like the RunKeeper. Research shows that apps like that enhance engagement and results. And people think it is fun. The essence being that the behavior of the runner is tracked and communicated back to the runner in a dashboard. We wondered if you can reach the same positive effect if you had a dashboard for Study-behaviour. For students. And what should you measure, track and communicate? We wondered if we could translate the Quantified Self Movement into a Quantified Student. So, together with students, professors and companies we started designing & building Quantified Student Apps. Apps that were measuring all kinds of study-behaviour related data. Things like Time On Campus, Time Online, Sleep, Exercise, Galvanic Skin Response, Study Results and so on. We developed tools to create study – information and prototyped the Apps with groups of student. At the same time we created a Big Data Lake and did a lot of Privacy research. The Big Difference between the Quantified Student Program and Learning Analytics is that we only present the data to the student. It is his/her data! It is his/her decision to act on it or not. The Quantified Student Apps are designed as a Big Mother never a Big Brother. The project has just started. But we already designed, created and learned a lot. 1. We designed and build for groups of prototypes for Study behavior Apps: a. Apps that measure sleep & exercise and compare it to study results, like MyRhytm; b. Apps that measure study hours and compare it to study results, like Nomi; c. Apps that measure group behavior and signal problems, like Groupmotion; d. Apps that measure on campus time and compare it with peers, like workhorse; 2. We researched student fysics to see if we could find his personal Cup-A-Soup-Moment (meaning, can we find by looking at his/her biometrics when the concentration levels dip?); 3. We created a Big Data lake with student data and Open Data and are looking for correlation and causality there. We already found some interesting patterns. In doing so we learned a lot. We learned it is often hard to acquire the right data. It is hard to create and App or a solution that is presenting the data in the right way and presents it in a form of actionable information. We learned that health trackers are still very inprecise. We learned about (and solved some) challenges surrounding privacy. Next year (2017) we will scale the most promising prototype, measure the effects, start a new researchproject and continu working on our data lake. Things will be interesting, and we will blog about it on www.quantifiedstudent.nl.
In this paper we compare the effects of using three game user research methodologies to assist in shaping levels for a 2-D platformer game, and illustrate how the use of such methodologies can help level designers to make more informed decisions in an otherwise qualitative oriented design process. Game user interviews, game metrics and psychophysiology (biometrics) were combined in pairs to gauge usefulness in small-scale commercial game development scenarios such as the casual game industry. Based on the recommendations made by the methods, three sample levels of a Super Mario clone were improved and the opinions of a second sample of users indicated the success of these changes. We conclude that user interviews provide the clearest indications for improvement among the considered methodologies while metrics and biometrics add different types of information that cannot be obtained otherwise.
The AR in Staged Entertainment project focuses on utilizing immersive technologies to strengthen performances and create resiliency in live events. In this project The Experiencelab at BUas explores this by comparing live as well as pre-recorded events that utilize Augmented Reality technology to provide an added layer to the experience of the user. Experiences will be measured among others through observational measurements using biometrics. This projects runs in the Experience lab of BUas with partners The Effenaar and 4DR Studio and is connected to the networks and goals related to Chronosphere, Digireal and Makerspace. Project is powered by Fieldlab Events (PPS / ClickNL)..
Evaluating player game experiences through biometric measurementsThe BD4CG (Biometric Design for Casual Games project) worked in a highly interdisciplinary context with several international partners. The aim of our project was to popularize the biometric method, which is a neuro-scientific approach to evaluating the player experience. We specifically aimed at the casual games sector, where casual games can be defined as video or web-based games with simple and accessible game mechanics, non threatening themes and generally short play sessions. Popular examples of casual games are Angry Birds and FarmVille. We focussed on this sector because it is growing fast, but its methodologies have not grown with it yet. Especially the biometrics method has so far been almost exclusively used domain by the very large game developers (such as Valve and EA). The insights and scientific output of this project have been enthusiastically embraced by the international academic arena. The aim of the grant was to focus on game producers in the casual sector, and we have done so but we also established further contacts with the game sector in general. Thirty-one outputs were generated, in the form of presentations, workshops, and accepted papers in prominent academic and industry journals in the field of game studies and game user research. Partners: University of Antwerpen, RANJ, Forward Games, Double Jungle, Realgames, Dreams of Danu, Codemasters, Dezzel, Truimph Studios, Golabi Studios
In dit project wordt onderzocht welke opkomende data-gedreven ontwerptechnieken interessant zijn voor de beroepspraktijk. Doel In dit project proberen we samen met de beroepspraktijk grip te krijgen op nieuwe data-gedreven technieken in de UX-beroepspraktijk, zoals data-gedreven personas en usability mining en we proberen in te schatten hoe kansrijk deze technieken zijn. Resultaten We werken toe naar een routekaart waarin we voor elke techniek in kaart brengen op welke termijn we een rol weggelegd zien in de beroepspraktijk en welke barrières er zijn voor adoptie. Looptijd 01 januari 2021 - 01 augustus 2021 Aanpak We vergelijken eerst twee technieken die nu veel gebruikt worden: A/B Testen en biometrics. Daarna gebruiken we de lessen die we hier uit trekken om beter in te kunnen schatten wat nieuwe technieken kunnen brengen. Relevantie van het onderzoek We stellen vaak dat data-gedreven ontwerp meer is dan A/B testen. In dit project maken we concreet wat het nog meer is.
