A study of the improvement of the quality of student teachers’ lessons in interactive (story)book reading through the use of data-feedback on observed lessons. Variables regarding the optimal time use, the quality of instruction and the student teachers’ pedagogical relation with pupils were included in a one group pre-test post-test design.
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In dit white paper wordt ingegaan op het begrip student engagement. Waarom nu inzetten op engagement? Wat levert dat op? Is student engagement te meten? En is er een blauwdruk voor HO instellingen?
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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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In dit rapport worden de activiteiten van Big Data Value Center in het project ‘Databoeren met boerendata in de aardappelsector’, een POP3 project, beschreven. Het BDVC heeft samen met Geronimo een proof of concept ontwikkeld op automatische voorzet ‘MijnPrecelen’ in RVO. Trefwoorden: digitalisering boerenbedrijf, pop3, databoeren, Proof of Concept, MijnPercelenRVO zaaknummer: 17717000042
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Learning analytics is the analysis of student data with the purpose of improving learning. However, the process of data cleaning remains underexposed within learning analytics literature. In this paper, we elaborate on choices made in the cleaning process of student data and their consequences. We illustrate this with a case where data was gathered during six courses taught via Moodle. In this data set, only 21% of the logged activities were linked to a specific course. We illustrate possible choices in dealing with missing data by applying the cleaning process twelve times with different choices on copies of the raw data. Consequently, the analysis of the data shows varying outcomes. As the purpose of learning analytics is to intervene based on analysis and visualizations, it is of utmost importance to be aware of choices made during data cleaning. This paper's main goal is to make stakeholders of (learning) analytics activities aware of the fact that choices are made during data cleaning have consequences on the outcomes. We believe that there should be transparency to the users of these outcomes and give them a detailed report of the decisions made.
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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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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.
MULTIFILE
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De arbeidsmarkt is continu in ontwikkeling, leidend tot een steeds veranderende vraag naar competenties en banen. Dit vraagt naast beroepsgerichte vaardigheden en kennis over veerkracht en wendbaarheid van professionals. Van de student wordt daarom verwacht dat die zich ontwikkeld in zelfgereguleerd (ZGL) leren. ZGL gaat over regie van het eigen leerproces: studenten bepalen zelf hoe tot leerresultaten te komen, deze te evalueren en sturen het leerproces zelf bij. Voor opleidingen is het de vraag hoe ze ZGL kunnen begeleiden en bevorderen. Dit behoeft inzicht in leergedrag, patronen hierin en bewustzijn over hoe deze inzichten gebruikt kunnen worden om ZGL te ondersteunen en het leerproces te begeleiden. In dit onderzoek is geïnventariseerd of de data die studenten in de elektronische leeromgeving (ELO) achterlaten een indicatie kan geven over het leerproces en ZGL van de student. Om de ingewikkelde patronen uit de data te halen, zijn de data uit de ELO met behulp van AItechnieken geanalyseerd. Hiermee kon het leerproces van studenten in verschillende categorieën worden onderverdeeld. De categorieën geven een eerste indicatie over het ZGL van de student. Verder onderzoek is benodigd, ook om te onderzoeken wat dit betekent voor de ondersteuning van studenten in hun leerproces.
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