The present study focuses on the level of stress male and female teachers perceive when dealing with the most behaviorally challenging student in his or her classroom. To measure stress in Dutch elementary classrooms, a sample was drawn of 582 teachers. First, they rated the most challenging student in their classroom on six different behavioral components: Against the grain, Full of activity/Easily distractible, Needs a lot of attention/Week student, Easily upset, Failuresyndrome/Excessively perfectionist, and Aggressive/Hostile. Teachers then scored perceived stress as a result of this challenging behavior. Two questions concerning gender relations in class rooms will be addressed. Do female and male teachers select the same type of behaviorally challenging students as the most challenging? And: do they perceive the same level of stress? Our data shows that female teachers do indeed report significantly more incidence of challenging behavior, but no evidence is found for differences between stress levels of male and female teachers.
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The hospitality industry contributes significantly to global climate change through its high resource consumption and emissions due to travel. As public pressure for hotels to develop sustainability initiatives to mitigate their footprint grows, a lack of understanding of green behavior and consumption of hotel guests hinders the adoption of effective programs. Most tourism research thus far has focused on the ecotourism segment, rather than the general population of travelers, and while research in consumer behavior shows that locus of control (LOC) and guilt can influence guests’ environmental behavior, those factors have not been tested with consideration of the subjective norm to measure their interaction and effect on recycling behavior. This study first examines the importance of internal and external LOC on factors for selecting hotel accommodation and the extent of agreement about hotel practices and, second, examines the differences in recycling behavior among guests with internal versus external LOC under levels of positive versus negative subjective norms and feelings of low versus high guilt.
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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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Een van de meest populaire modellen voor onderzoek naar welzijn, stress en bevlogenheid van medewerkers is het Job Demands-Resources model (JD-R model). Voor onderzoek naar het welzijn van studenten heeft het lectoraat Studiesucces het Student Wellbeing model ontwikkeld, een model gebaseerd op het JD-R model. Het Student Wellbeing model beschrijft net als het JD-R model een motivatieproces en een uitputtingsproces, maar dan van studenten. Het model veronderstelt dat de balans tussen positieve (energiebronnen) en negatieve (stressoren) kenmerken van ‘het student zijn/de studententijd’ invloed heeft op het welzijn van studenten en o.a. de studieprestaties kan beïnvloeden.
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At the beginning of May 2020 Inholland students received an invitation to participate in a large international study on the corona crisis impact on student life and studies. Almost 3000 students participated. This factsheet shows data on their lifestyleand their resilience. But also on their worries about corona, their knowledge of it and their opinion on the information supply.
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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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Introduction Student success is positively linked to engagement, but negatively linked to emotional exhaustion. Though both constructs have been conceptualized as opposites previously, we hypothesize that students can demonstrate high or low engagement and emotional exhaustion simultaneously. We used quantitative and qualitative data to identify the existence of four student profiles based on engagement and exhaustion scores. Furthermore, we studied how profiles associate to study behaviour, wellbeing and academic achievement, and what risks, protective factors and support requirements students and teachers identify for these profiles. Methods The Student Wellbeing Monitor 2021, developed by Inholland University of Applied Sciences, was used to identify profiles using quadrant analyses based on high and low levels of engagement and emotional exhaustion (n= 1460). Correlation analyses assessed profile specific differences on study behaviours, academic delay, and wellbeing. Semi-structured interviews with students and teachers are currently in progress to further explore the profiles, to identify early signals, and to inspect support requirements. Results The quadrant analysis revealed four profiles: low engagement and low exhaustion (energised-disengaged; 9%), high engagement and low exhaustion (energised-engaged; 15%), low engagement and high exhaustion (exhausted-disengaged; 48%), and high engagement and high exhaustion (exhausted-engaged; 29%). Overall, engaged students demonstrated more active study behaviours and more social connections and interactions with fellow students and teachers. The exhausted students scored higher on depressive symptoms and stress. The exhausted-engaged students reported the highest levels of performance pressure, while the energised-disengaged students had the lowest levels of performance pressure. So far, students and teachers recognise the profiles and have suggested several support recommendations for each profile. Discussion The results show that students can be engaged but at the same time are exhausting themselves. A person-oriented mixed-methods approach helps students and teachers gain awareness of the diversity and needs of students, and improve wellbeing and student success.
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The relationship among students' reading performance, their behavior (task-focused behavior, emotional stability, and compliant behavior) in the classroom, and the teacher's skills was investigated in 66 third-grade classrooms. Results from this study showed the students' reading performance and their behavior in the classroom are all significantly interrelated. Better reading performance at the beginning of the school year goes with better behavior at the end of the school year. In turn, better behavior at the beginning of the school year goes with better reading performance at the end of the school year. The teacher can improve the behavior of the students by providing high-quality reading instruction. Some teacher skills have differential effects, however, on the various behavioral aspects. The implications for the educational practice as well as for future research are discussed.
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Much research has been done into the relationship between students’ motivation to learn and their basic psychological needs as defined by the self-determination theory (autonomy, competence, relatedness). However, few studies have explored how these psychological needs relate to different types of maladaptive behavior in the classroom. To prevent or remedy such behavior, more insight into its relationships is required. The present study attempted to determine the relationship between maladaptive behavior of secondary school students (grades 8 and 9) and the degree to which both teachers and peers address their needs for competence, autonomy, and relatedness. Results show significant, negative correlations between maladaptive student behavior in the classroom and the extent to which students’ basic psychological needs are met by teachers and fellow students. Both teachers and fellow students play a role in students’ maladaptive behavior toward school and withdrawn behavior. When it comes to unfriendly behavior, the perceived support of teachers appears to be particularly relevant, while the role of peers is an important factor in delinquent behavior.
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The purpose of this study was to provide insight into the interplay between student perceptions of competence-based assessment and student self-efficacy, and how this influences student learning outcomes. Results reveal that student perceptions of the form authenticity aspect and the quality feedback aspect of assessment do predict student self-efficacy, confirming the role of mastery experiences and social persuasions in enhancing student self-efficacy as stated by social cognitive theory. Findings do not confirm mastery experiences as being a stronger source of self-efficacy information than social persuasions. Study results confirm the predictive role of students’ self-efficacy on their competence outcomes. Mediation analysis results indicate that student’s perceptions of assessment have an indirect effect on student’s competence evaluation outcomes through student’s self-efficacy. Study findings highlight which assessment characteristics, positively influencing students’ learning, contribute to the effectiveness of competence-based education. Limitations of the study and directions for future research are indicated.
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