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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We use a randomised experiment to study the effect of offering half of 556 freshman students a learning analytics dashboard and a weekly email with a link to their dashboard, on student behaviour in the online environment and final exam performance. The dashboard shows their online progress in the learning management systems, their predicted chance of passing, their predicted grade and their online intermediate performance compared with the total cohort. The email with dashboard access, as well as dashboard use, has positive effects on student behaviour in the online environment, but no effects are found on student performance in the final exam of the programming course. However, we do find differential effects by specialisation and student characteristics.
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A primary teacher needs mathematical problem solving ability. That is why Dutch student teachers have to show this ability in a nationwide mathematics test that contains many non-routine problems. Most student teachers prepare for this test by working on their own solving test-like problems. To what extent does these individual problem solving activities really contribute to their mathematical problem solving ability? Developing mathematical problem solving ability requires reflective mathematical behaviour. Student teachers need to mathematize and generalize problems and problem approaches, and evaluate heuristics and problem solving processes. This demands self-confidence, motivation, cognition and metacognition. To what extent do student teachers show reflective behaviour during mathematical self-study and how can we explain their study behaviour? In this study 97 student teachers from seven different teacher education institutes worked on ten non-routine problems. They were motivated because the test-like problems gave them an impression of the test and enabled them to investigate whether they were already prepared well enough. This study also shows that student teachers preparing for the test were not focused on developing their mathematical problem solving ability. They did not know that this was the goal to strive for and how to aim for it. They lacked self-confidence and knowledge to mathematize problems and problem approaches, and to evaluate the problem solving process. These results indicate that student teachers do hardly develop their mathematical problem solving ability in self-study situations. This leaves a question for future research: What do student teachers need to improve their mathematical self-study behaviour? EAPRIL Proceedings, November 29 – December 1, 2017, Hämeenlinna, Finland
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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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(‘Co’-)Designing for healthy behaviour greatly benefits from integrating insights about individual behaviour and systemic influences. This study reports our experiences in using insights about individual and systemic determinants of behaviour to inform a large co-design project. To do so, we used two design tools that encourage focusing on individual determinants (Behavioural Lenses Approach) and social / systemic aspects of behaviour (Socionas). We performed a qualitative analysis to identify 1) when and how the team applied the design tools, and 2) how the tools supported or obstructed the design process. The results show that both tools had their distinctive uses during the process. Both tools improved the co-design process by deepening the conversations and underpinnings of the prototypes. Using the Behavioural Lenses under the guidance of a behavioural expert proved most beneficial. Furthermore, the Socionas showed the most potential when interacting with stakeholders, i.c. parents and PPTs.
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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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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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Specific approaches are needed to reach and support people with a lower socioeconomic position (SEP) to achieve healthier eating behaviours. There is a growing body of evidence suggesting that digital health tools exhibit potential to address these needs because of its specific features that enable application of various behaviour change techniques (BCTs). The aim of this scoping review is to identify the BCTs that are used in diet-related digital interventions targeted at people with a low SEP, and which of these BCTs coincide with improved eating behaviour. The systematic search was performed in 3 databases, using terms related to e/m-health, diet quality and socioeconomic position. A total of 17 full text papers were included. The average number of BCTs per intervention was 6.9 (ranged 3–15). BCTs from the cluster ‘Goals and planning’ were applied most often (25x), followed by the clusters ‘Shaping knowledge’ (18x) and ‘Natural consequences’ (18x). Other frequently applied BCT clusters were ‘Feedback and monitoring’ (15x) and ‘Comparison of behaviour’ (13x). Whereas some BCTs were frequently applied, such as goal setting, others were rarely used, such as social support. Most studies (n = 13) observed a positive effect of the intervention on eating behaviour (e.g. having breakfast) in the low SEP group, but this was not clearly associated with the number or type of applied BCTs. In conclusion, more intervention studies focused on people with a low SEP are needed to draw firm conclusions as to which BCTs are effective in improving their diet quality. Also, further research should investigate combinations of BCTs, the intervention design and context, and the use of multicomponent approaches. We encourage intervention developers and researchers to describe interventions more thoroughly, following the systematics of a behaviour change taxonomy, and to select BCTs knowingly.
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Increasingly aware of the importance of active lifestyles, many people intend to exercise more. One of the main challenges is to translate exercise intentions into actual exercise behaviour, the so-called intention-behaviour gap. To investigate barriers and enablers that affect this gap, we conducted a 7-day diary study with 16 women. Participants indicated what their exercise intentions and behaviour were per day, and whether and why they changed retrospectively during the day. Through the diary study, we gain insights into (i) the intention-behaviour interplay, and (ii) the experienced barriers and enablers that influence this interplay throughout the day. Based on the findings, we contribute new implications for design in supporting people translating their intentions into exercise behaviour. We propose three design concepts to illustrate underlying design opportunities. The focus is on positively influencing the interplay of enablers and barriers of exercising and how these can be addressed through design
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This open access book is a valuable resource for students in health and other professions and practicing professionals interested in supporting effective change in self-management behaviors in chronic disease, such as medication taking, physical activity and healthy eating. Developed under the auspices of the Train4Health project, funded by the Erasmus+ program of the European Union, the book contains six chapters written by international contributors from different disciplines. This chapter presents open-access educational products that supplement this book: case studies and a web application to simulate behaviour change support in persons with chronic disease. The former is of particular interest for academic educators, while the latter may interest students independently pursuing training outside the classroom. These products can also be useful for professionals aiming to enhance behaviour change competencies in practice. First, it addresses key aspects of product development, including hallmarks such as the incorporation of behaviour change science and transnational co-production with users. Then, the main features of case studies and the web application with 2D virtual humans are described.
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