Poster presented at EFYE 2018. Strengthening the wellbeing of students is an increasingly important approach of the development of students’ social, emotional and academic skills. Personal wellbeing motivates, among other things, students to learn and increases academic involvement and performance accordingly (Noble et al., 2008). According to the Centre for Education of Statistics and Evaluation (CESE, 2015) the educational welfare of students is also important for another reason; the recognition that teaching is not just about achieving academic performance, but also about the welfare of the student as a whole (intellectual, physical, social, emotional, moral and spiritual). Recent studies indicate that more and more students suffer from (mental) health problems (LSvB 2013, 2017; Schaufeli et al., 2002). The aim of the Student Wellbeing Project at Inholland University of Applied Sciences is to 1) investigate the state of student wellbeing in Dutch higher education and investigate the factors that influence wellbeing, 2) explore and offer best practices to improve student wellbeing (curative and preventive) 3) establish a strong (international) partnership and collaborate to improve student wellbeing.
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As part of my PhD research, I investigate the influence of the use of social media by first year students in higher education. In this research I have lessened the amount of variables, from Tinto’s theory, by including only the best-proven predictive variables, based on previous studies. Hereby, avoiding the capitalization of chance and a more easy to use model for teachers and management has been built. The latent variable ‘satisfaction’ is constructed by using just a fraction of the original manifest variables and tested using principal component analysis to proof the model can be simplified. Furthermore, I enriched the model with the use of social media, in particular Facebook, to better suit students’ contemporary society in the developed world. With principal analysis on Facebook usage, I measured the purpose of Facebook use (information, education, social and leisure) and the use of different pages amongst students. This provided different integration/engagement components, which are also included in the simplified model. For the principal component-analysis, Cronbach’s alpha and Guttman’s lambda-2 showed internal consistency and reliability. SPSS AMOS was used for testing the fit of the model and showed reasonable values for the normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and the root mean square error of approximation (RMSEA). This study will compare different background variables with the model to uncover the possible influences upon student success, engagement/satisfaction and social media use. Ultimately this paper will provide a better insight into what kind of influence social media can have upon student success.
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At the beginning of May 2020 all Inholland-students received an invitation to participate in a large international study on the corona crisis impact on student life and studies. This poster, presented by the Study Success Research Group, covers relevant results divided in four themes. These themes are student wellbeing, student engagement, satisfaction and the coronavirus. To determine student wellbeing we asked students about their feelings and contacts. Student engagement is phrased in time allocation and engagement. We also wanted to find out how satisfied students were with things like ICT facilities, quality of education and provision of information. Of course we asked students about (not) having corona and adhering to the measures.
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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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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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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 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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In this article I will discuss theories on students’ success in higher education and the need for adjustments of these theories in the contemporary, information and media saturated world. The integration theory on student retention, founded by Tinto and further developed by him and many others, lies at the base of most studies on student success. In line with Tinto’s theory the majority of studies measure both social and academic integration of a student, alongside background variables. Social integration is shaped by the personal contact with fellow students and staff and whether or not a student enjoys being at the institute. Academic integration has more to do with academic achievement and sharing the academic norms and values. Although the distinction of these types of integration has been experienced as an artificial one and has been abandoned in more recent studies, the conclusion of most studies remains the same: the higher the level of integration, the greater the level of commitment, which in turn has a positive affect on the likelihood of student persistence in college and the success of a student. More recent studies use ‘engagement’ to embed the various factors of integration to avoid the rigid distinction between social and academic and to include new forms of communication between students for social, academic and other purposes. Furthermore the world has changed since the origin of Tinto’s integration theory in the early eighties, especially if you look at the changes in society under the influence of technology in general and in particular the Internet. New ways of communicating has emerged which brought along new possibilities. The emergence of smart phones has played a big part in the various ways we communicate. The new devices and communication tools have made it possible to employ integrating social and academic activities without the necessity of physical presence. The central question of the article is: Should online communities or engaging platforms like Facebook, be taken into account when investigating the influential facors of student success?
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The research on student attrition, retention and success in the Netherlands is highly influenced by Tinto’s integration theory. In this paper, as part of my broader PhD research, I propose adjusting this theory to achieve a better fit with the present generation of students in the developed world. By measuring the best predictive variables of Tinto’s theory at an ordinal level it also fits better with the evaluation forms used in Dutch Institutes of Higher education. In contemporary society social media plays a crucial role and thus also in the lives of students. Earlier research has been inconclusive about the effectof social media on students’ success, however, as it has focused on the quantitative rather than the qualitative aspects of social media use. In line with the above-mentioned pedagogical theory and using insights from recent studies on students’ social media use, I test the influence of various factors as well as the use of social media on student success. This paper provides insight into the potential uses of social media in education – especially by students outside of the classroom.
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