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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A decline in both student well-being and engagement were reported during the COVID-pandemic. Stressors and internal energy sources can co-exist or be both absent, which might cohere with different student needs. This study aimed to develop student profiles on emotional exhaustion and engagement, as well as examine how profiles relate to student participation, academic performance, and overall well-being. Survey-data from 1,460 Dutch higher education students were analyzed and resulted in a quadrant model containing four student profiles on engagement and emotional exhaustion scores. Semi-structured interviews with 13 students and 10 teaching staff members were conducted to validate and further describe the student profiles. The majority of the survey participants were disengaged-exhausted (48%) followed by engaged-exhausted students (29%). Overall, the engagedenergized students performed best academically and had the highest levels of well-being and participation, although engaged-exhausted students were more active in extracurricular activities. The engaged exhausted students also experienced the most pressure to succeed. The qualitative validation of the student profiles demonstrates that students and teachers recognize and associate the profiles with themselves or other students. Changes in the profiles are attributed to internal and external factors, suggesting that they are not fixed but can be influenced by various factors. The practical relevance of the quadrant model is acknowledged by students and teachers and they shared experiences and tips, with potential applications in recognizing students’ well-being and providing appropriate support. This study enriches our grasp of student engagement and well-being in higher education, providing valuable insights for educational practices.
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Hbo-opleidingen tot leraar lichamelijke opvoeding zijn de afgelopen jaren bezig geweest om onderzoek te integreren in het onderwijs. Het instellen van lectoraten aan hbo-opleidingen heeft daar ook een positieve bijdrage aan geleverd. Op school zal je te maken krijgen met stagiaires of nieuwe collega’s met een onderzoekende houding. Ondanks dezelfde kaders vanuit de NVAO1 wordt de invulling van onderzoek in de opleidingen aan instituten zelf overgelaten. Op het HBO-symposium Sportonderzoek van 6 november 2013 zijn verschillende ALO’s de discussie aangegaan over deze invulling. In dit artikel een kort overzicht van de bevindingen.
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In the current discourses on sustainable development, one can discern two main intellectual cultures: an analytic one focusing on measuring problems and prioritizing measures, (Life Cycle Analysis (LCA), Mass Flow Analysis (MFA), etc.) and; a policy/management one, focusing on long term change, change incentives, and stakeholder management (Transitions/niches, Environmental economy, Cleaner production). These cultures do not often interact and interactions are often negative. However, both cultures are required to work towards sustainability solutions: problems should be thoroughly identified and quantified, options for large change should be guideposts for action, and incentives should be created, stakeholders should be enabled to participate and their values and interests should be included in the change process. The paper deals especially with engineering education. Successful technological change processes should be supported by engineers who have acquired strategic competences. An important barrier towards training academics with these competences is the strong disciplinarism of higher education. Raising engineering students in strong disciplinary paradigms is probably responsible for their diminishing public engagement over the course of their studies. Strategic competences are crucial to keep students engaged and train them to implement long term sustainable solutions.
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Lectorale rede waarin wordt ingegaan op de manier waarop de mens nu binnen zijn natuurlijke omgeving functioneert. Dit wordt getypeerd als een ‘mismatch’. Tegelijkertijd is de lector er ook van overtuigd dat de technologie uiteindelijk zorgt voor een beter leven.
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Artikel in het kader van het thema: Onderzoek in de praktijk.Het is onbekend hoeveel voedingsapps er zijn en wat de kwaliteit van deze apps is. In opdracht van het Quantified Self Institute verrichtten twee studenten Voeding en Diëtetiek van de Hanzehogeschool Groningen een verkennend onderzoek.
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Manure application can spread antimicrobial resistance (AMR) from manure to soil and surface water. This study evaluated the role of the soil texture on the dynamics of antimicrobial resistance genes (ARGs) in soils and surrounding surface waters. Six dairy farms with distinct soil textures (clay, sand, and peat) were sampled at different time points after the application of manure, and three representative ARGs sul1, erm(B), and tet(W) were quantified with qPCR. Manuring initially increased levels of erm(B) by 1.5 ± 0.5 log copies/kg of soil and tet(W) by 0.8 ± 0.4 log copies/kg across soil textures, after which levels gradually declined. In surface waters from clay environments, regardless of the ARG, the gene levels initially increased by 2.6 ± 1.6 log copies/L, after which levels gradually declined. The gene decay in soils was strongly dependent on the type of ARG (erm(B) < tet(W) < sul1; half-lives of 7, 11, and 75 days, respectively), while in water, the decay was primarily dependent on the soil texture adjacent to the sampled surface water (clay < peat < sand; half-lives of 2, 6, and 10 days, respectively). Finally, recovery of ARG levels was predicted after 29–42 days. The results thus showed that there was not a complete restoration of ARGs in soils between rounds of manure application. In conclusion, this study demonstrates that rather than showing similar dynamics of decay, factors such as the type of ARG and soil texture drive the ARG persistence in the environment.
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Integrated curricula seem promising for the increase of attention on science and technology in primary education. A clear picture of the advantages and disadvantages of integration efforts could help curriculum innovation. This review has focussed on integrated curricula in primary education from 1994 to 2011. The integrated curricula were categorized according to a taxonomy of integration types synthesized from the literature. The characteristics that we deemed important were related to learning outcomes and success/fail factors. A focus group was formed to facilitate the process of analysis and to test tentative conclusions. We concluded that the levels in our taxonomy were linked to (a) student knowledge and skills, the enthusiasm generated among students and teachers, and the teacher commitment that was generated; and (b) the teacher commitment needed, the duration of the innovation effort, the volume and comprehensiveness of required teacher professional development, the necessary teacher support, and the effort needed to overcome tensions with standard curricula. Almost all projects were effective in increasing the time spent on science at school. Our model resolves Czerniac’s definition problem of integrating curricula in a productive manner, and it forms a practical basis for decision-making by making clear what is needed and what output can be expected when plans are being formulated to implement integrated education.
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Graphs are ubiquitous. Many graphs, including histograms, bar charts, and stacked dotplots, have proven tricky to interpret. Students’ gaze data can indicate students’ interpretation strategies on these graphs. We therefore explore the question: In what way can machine learning quantify differences in students’ gaze data when interpreting two near-identical histograms with graph tasks in between? Our work provides evidence that using machine learning in conjunction with gaze data can provide insight into how students analyze and interpret graphs. This approach also sheds light on the ways in which students may better understand a graph after first being presented with other graph types, including dotplots. We conclude with a model that can accurately differentiate between the first and second time a student solved near-identical histogram tasks.
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Unhealthy eating behaviors and low levels of physical activity are major problems in adolescents and young adults in vocational education. To develop effective intervention programs, more research is needed to understand how different types of motivation contribute to health behaviors. In the present study, Self-Determination Theory is used to examine how motivation contributes to dietary and physical activity behaviors in vocational students. This cross-sectional study included 809 students (mean age 17.8 ± 1.9 years) attending vocational education in the Netherlands. Linear multilevel regression analyses were used to investigate the association between types of motivation and dietary and physical activity behaviors. Amotivation was negatively associated with breakfast frequency and positively associated with diet soda consumption and high-calorie between-meal snacks. A positive association was found between autonomous motivation and water intake, breakfast frequency, fruit intake, and moderate-to-vigorous physical activity. Autonomous motivation was negatively associated with the consumption of unhealthy products. Controlled motivation was not associated with physical activity or dietary behaviors. Different types of motivation seem to explain either healthy or unhealthy dietary behaviors in vocational students. Autonomous motivation, in particular, was shown to be associated with healthy behaviors and could therefore be a valuable intervention target.
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