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.
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De African Digital Rights Network (ADRN) heeft een nieuw rapport gepubliceerd waarin de toevoer en verspreiding van digitale surveillance technologie in Afrika in kaart is gebracht. Onderzoeker Anand Sheombar van het lectoraat Procesinnovatie & Informatiesystemen is betrokken bij het ADRN-collectief en heeft samen met de Engelse journalist Sebastian Klovig Skelton, door middel van desk research de aanvoerlijnen vanuit Westerse en Noordelijke landen geanalyseerd. De bevindingen zijn te lezen in dit Supply-side report hoofdstuk van het rapport. APA-bronvermelding: Klovig Skelton, S., & Sheombar, A. (2023). Mapping the supply of surveillance technologies to Africa Supply-side report. In T. Roberts (Ed.), Mapping the Supply of Surveillance Technologies to Africa: Case Studies from Nigeria, Ghana, Morocco, Malawi, and Zambia (pp. 136-167). Brighton, UK: Institute of Development Studies.
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African citizens are increasingly being surveilled, profiled, and targeted online in ways that violate their rights. African governments frequently use pandemic or terrorism-related security risks to grant themselves additional surveillance rights and significantly increase their collection of monitoring apparatus and technologies while spending billions of dollars to conduct surveillance (Roberts et al. 2023). Surveillance is a prominent strategy African governments use to limit civic space (Roberts and Mohamed Ali 2021). Digital technologies are not the root of surveillance in Africa because surveillance practices predate the digital age (Munoriyarwa and Mare 2023). Surveillance practices were first used by colonial governments, continued by post-colonial governments, and are currently being digitalized and accelerated by African countries. Throughout history, surveillance has been passed down from colonizers to liberators, and some African leaders have now automated it (Roberts et al. 2023). Many studies have been conducted on illegal state surveillance in the United States, China, and Europe (Feldstein 2019; Feldstein 2021). Less is known about the supply of surveillance technologies to Africa. With a population of almost 1.5 billion people, Africa is a continent where many citizens face surveillance with malicious intent. As mentioned in previous chapters, documenting the dimensions and drivers of digital surveillance in Africa is
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