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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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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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.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
