Sexual interest in children is an important factor contributing to sexual (re)offending against children. The current state of research makes it difficult to conclude if people with pedophilia are overly interested in children, or have lower interest in adults, or both. This is relevant knowledge in treatment for preventing sexual (re)offenses against children. This study aimed to systematically analyze sexual interest in both children and adults in samples of men with pedophilia and comparison groups. A total of 55 studies (N = 8465) were included in four meta-analyses and a systematic review. Most included studies considered people who had sexually offended against children (PSOC; nPSOC = 5213). Results indicated that PSOC with pedophilia did not have a clear sexual preference for either children or adults. Compared to comparison groups, they had more absolute sexual interest in children and lower sexual interest in adults. We conclude that the lack of sexual interest in adults may be a relevant factor in PSOC with pedophilia. More studies are needed to disentangle sexual interest in children from sexual interest in adults, while using carefully matched comparison groups and appropriate research designs.
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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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This study investigated the comparative impact of AR avatars versus 2D video avatars on secondary students' engagement, learning motivation, and subject interest in history education. A quasi-experimental study was conducted with German secondary students aged 11-14 across three conditions: control group (no intervention), 2D avatar learning experience, and AR avatar learning experience.
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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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Obtaining credits, studying for exams, attending classes, engaging with fellow students and lecturers, living alone or with others, and taking part in extra-curricular activities: there is a fair amount for students in higher education to take in. There are also numerous external factors — such as the COVID-19 pandemic and the changing labour and housing market — that affect students. However, students experience these situations differently and deal with them in different ways. How can we ensure that, notwithstanding these stress factors and differences, as many students as possible become and remain engaged and energised? Happier students tend to be more engaged and generally achieve better study results.1 That is why student well-being is also a widely researched and important topic. The search is on for measures to promote student well-being and success. Having a clear idea of how things are going for a student and what they need is a starting point. This booklet helps readers to identify different student profiles and understand what is needed to improve student success. We zoom in on two key aspects of student success: engagement and emotional exhaustion.
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Why studying student agency? • Prepare students for lifelong learning. (Biesta & Tedder, 2007;OECD, 2018) • Agency fosters motivation, which could enhance performance. (Bandura, 2018; Ryan & Deci, 2020) • More flexibility in higher education, but not all students can handle this. (De Bruin & Verkoeijen, 2022; Van Casteren e.a., 2021)
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The student well-being study of the Study Success Research Group has mapped out how students are doing. Among other things, they researched how healthy and engaged the students are and how they experience their study resources and personal energy sources, such as resilience and selfefficiency. The research shows that students predominantly think that they have a healthy lifestyle and consider themselves to be healthy. However, a large part of the students experiences stress on a regular basis (to a large extent) during their time as students. This infographic shows the top 10 of suggestions that students have for the students themselves, their teachers and for changes within education/the curriculum that could contribute to reducing stress and could promote the well-being of students.
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The corona pandemic has forced higher education (HE) institutes to transition to online learning, with subsequent implications for student wellbeing. Aims: This study explored influences on student wellbeing throughout the first wave of the corona crisis in the Netherlands by testing serial mediation models of the relationships between perceived academic stress, depression, resilience, and HE support.
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Earlier research argues that educational programmes based on social cognitive theory are successful in improving students’ self-efficacy. Focusing on some formative assessment characteristics, this qualitative research intends to study in-depth how student teachers’ assessment experiences contribute to their self-efficacy. We interviewed 15 second year student teachers enrolled in a competence based teacher educational programme. Thematic content analysis results reveal that the assessment characteristics ‘authenticity’ and ‘feedback’ exert a positive influence on student teachers self-efficacy during all phases of the portfolio competence assessment. The results provide a fine-grained view of several types of self-efficacy information connected with these assessment phases.
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As most Universities around the world the Amsterdam University of Applied Sciences conduct surveys (student evaluation monitor: STEM) among their students to evaluate the different courses and their teachers. At the Department of Media, Information and Communication the response by students tend to decline in the course of the year. In 2011-2012 with a limited enrolment of 900 first year students, 70% responded to the first survey conducted after the first exams in October and dropped to 26% in the last survey at the end of the first year (July 2012). In 2012-2013 (with the same amount of students) the response was respectively 75% and 30%. This might be due to several factors, such as the length of the questionnaire, the way the survey is spread (via e-mail to the students University account), the time of spreading the surveys (after the courses and exams) or simple due to lack of interest. Another problem of the surveys is found in the quest to limit the length of the questionnaires. Hereby, some relevant aspects to apprehend the success of students (or the return of the department) and the quality of the courses and teachers aren’t measured, such as: coherence between the courses, the students opinion about the form of education and exams, the connection between the evaluation and the exam results or other influential factors of student’s success. Given these difficulties and the fact that insight in all of the above mentioned aspects are crucial for both students and teachers and not in the least for the management, a new approach for evaluating is needed. An evaluating system that can uncover crucial information, for example to pinpoint the characteristics of dropout or long-term students in order to limit these, and/or improve the education/course. This paper will describe a pilot study wherein a first step towards a new way of evaluating is taken by separating the course- and teacher evaluation from the rest of the surveys by using an app/QR or website. Furthermore, the literature about in- or outside class surveys and student success will serve as a theoretical base for the discussion this pilot and is part of a broader PhD research.
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