Purpose– The present paper aims to explore to what extent the quality of facility services can be related to the differences in educational achievements in higher education.Design/methodology/approach - This paper is based on the first preliminary analyses of a national online survey among 1,752 lecturers of 18 Dutch Universities of Applied Sciences. Via explorative desk research, additional data were gathered regarding the educational achievements, size and religious identity of the institutions. Exploratory factor analysis and multiple regressionwere used to test the propositions.Findings – The results seem to indicate that the perceived quality of facility services that are education-related and provide personal comfort to teachers have a positive relationship with study success. Layout, fitting out, and general facility services show no statistically significant relationship with study success, whereas (traditional) workplaces have a negative relationship. Also, we found that the size of the education institution strongly negatively relates to studysuccess, and institutions with a Christian identity outperform non-Christian institutions.Practical implications– These preliminary research findings suggest that a prime consideration in learning space design is the facilitation of social interaction, creating a (virtual) small-scale learning environment in large institutions.Originality/value - This paper suggests that facility services can assist the quality of higher education.
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Poster presentation, International Conference on evoking Excellence in Higher Education and Beyond. Groningen, Hanzehogeschool, 4 oktober 2012.
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In this PhD thesis, we aimed to improve understanding of the study progression and success of autistic students in higher education by comparing them to students with other disabilities and students without disabilities. We studied their background and enrollment characteristics, whether barriers in progression existed, how and when possible barriers manifested themselves in their student journey, and how institutions should address these issues. We found autistic students to be different from their peers but not worse as expected based on existing findings. We expect we counterbalanced differences because we studied a large data set spanning seven cohorts and performed propensity score weighting. Most characteristics of autistic students at enrollment were similar to those of other students, but they were older and more often male. They more often followed an irregular path to higher education than students without disabilities. They expected to study full time and spend no time on extracurricular activities or paid work. They expected to need more support and were at a higher risk of comorbidity than students with other disabilities. We found no difficulties with participation in preparatory activities. Over the first bachelor year, the grade point averages (GPAs) of autistic students were most similar to the GPAs of students without disabilities. Credit accumulation was generally similar except for one of seven periods, and dropout rates revealed no differences. The number of failed examinations and no-shows among autistic students was higher at the end of the first semester. Regarding progression and degree completion, we showed that most outcomes (GPAs, dropout rates, resits, credits, and degree completion) were similar in all three groups. Autistic students had more no-shows in the second year than their peers, which affected degree completion after three years. Our analysis of student success prediction clarified what factors predicted their success or lack thereof for each year in their bachelor program. For first-year success, study choice issues were the most important predictors (parallel programs and application timing). Issues with participation in pre-education (absence of grades in pre-educational records) and delays at the beginning of autistic students’ studies (reflected in age) were the most influential predictors of second-year success and delays in the second and final year of their bachelor program. Additionally, academic performance (average grades) was the strongest predictor of degree completion within three years. Our research contributes to increasing equality of opportunities and the development of support in higher education in three ways. First, it provides insights into the extent to which higher education serves the equality of autistic students. Second, it clarifies which differences higher education must accommodate to support the success of autistic students during their student journey. Finally, we used the insights into autistic students’ success to develop a stepped, personalized approach to support their diverse needs and talents, which can be applied using existing offerings.
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TitleDefining Student Success as a Multidimensional Concept: a Scoping Review (additional files)DescriptionStudent success is a critically important concept in educational assessment and research. Yet, a universal definition of the concept has not been established. A comprehensive scoping review was conducted to define student success and take inventory of associated factors. This page contains files pertaining to the publication that presents the findings.
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The Study Success Research Group helps students on their way by providing ten useful tips on how to study successfully. These tips are based on an article the research group wrote for the study guide of the Vereniging van Schooldecanen en loopbaanbegeleiders (‘Association of Vocational and Academic Advisers’). The article contains various tips on how to study successfully, aimed at prospective students. These tips are used and supplemented with tips that are relevant for every student.
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Are you looking for some tips to stay focused on your studies, now that education has gone online? Have a read through the tips below from the Study Success research group. These tips have been compiled on the basis of scientific insight from cognitive psychology, neuropsychology and educational science, as well as our own studies into motivation, stress, enthusiasm and drop-out.
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For my PhD research I build a structural model to predict student success. Initially to show the influence of social media use by first year students in higher education. However, for this research I use the model to investigate the predictive value of a student choice test. This test is mandatory for all students prior to their enrolment at the Amsterdam University of Applied Sciences. In this study two of the Institutes (Communication and Creative Business/Media, Information and Communication) of the Faculty of Digital Media and the Creative Industries participated with a first year enrollment in the year 2017 of 1010 students (respectively of 327 and 683), and in 2018, 1193 students (respectively 225 and 968). This study choice test involved an assignment that the student-to-be had to do at home and bring to the Institute when they took part in the second half of the study choice test. This second half involved an exam in topics central to the curriculum, a Dutch language test and all students had a final meeting with a teacher where they were given a positive or negative advice. Because of the large number of students, a substantial number of teachers and resources were used for this test. In order to see the pros and cons of the test, the predictive value was tested along with other variables which are proven to have a predictive value on student success. The best proven variables from Tinto’s theory were included, based on previous studies. The central variable in Tinto’s study is ‘satisfaction’ (which in other research is revert to as ‘engagement’ of ‘belonging’), consisting originally of a vast number of manifest variables. By using a fraction of those variables, I simplified the model, so it was an easier tool to use for teachers and management and in the meantime, avoiding the capitalization of chance. The smaller latent variable ‘satisfaction’ was tested using principal component analysis to prove the manifest variables where in fact representing one latent variable. Cronbach’s alpha and Guttman’s lambda-2 then provided the internal consistency and reliability of the variable. Along with ‘satisfaction’, the model included different background variables (gender, prior education, ethnicity), commitment and effort, expected progress and of course study success. This was measured by the time it takes a student to finish all first year exams and the average grade point (GPA). 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). The advice from the study choice test and the scores were tested in the model to uncover if there was a significant difference. Furthermore, the influence of all variables in the model were compared for their influence on study success.
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Individuals with autism increasingly enroll in universities, but little is known about predictors for their success. This study developed predictive models for the academic success of autistic bachelor students (N=101) in comparison to students with other health conditions (N=2465) and students with no health conditions (N=25,077). We applied propensity score weighting to balance outcomes. The research showed that autistic students’ academic success was predictable, and these predictions were more accurate than predictions of their peers’ success. For first-year success, study choice issues were the most important predictors (parallel program and application timing). Issues with participation in pre-education (missingness of grades in pre-educational records) and delays at the beginning of autistic students’ studies (reflected in age) were the most influential predictors for the second-year success and delays in the second and final year of their bachelor’s program. In addition, academic performance (average grades) was the strongest predictor for degree completion in 3 years. These insights can enable universities to develop tailored support for autistic students. Using early warning signals from administrative data, institutions can lower dropout risk and increase degree completion for autistic students.
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For delayed and long-term students, the education process is often a lonely journey. The main conclusion of this research is that learning should not be an individual process of the student connected to one lecturer, but rather a community where learning is a collective journey. The social interaction between lecturers, groups of delayed students and other actors is an important engine for arriving at the new knowledge, insights and expertise that are important to reach their final level. This calls for the design of social structures and the collaboration mechanism that enable the bonding of all members in the community. By making use of this added value, new opportunities for the individual are created that can lead to study success. Another important conclusion is that in the design and development of learning communities, sufficient attention must be paid to cultural characteristics. Students who delay are faced with a loss of self-efficacy and feelings of shame and guilt. A learning community for delayed students requires a culture in which students can turn this experience into an experience of self-confidence, hope and optimism. This requires that the education system pays attention to language use, symbols and rituals to realise this turn. The model ‘Building blocks of a learning environment for long-term students’ contains elements that contribute to the study success of delayed and long-term students. It is the challenge for every education programme to use it in an appropriate way within its own educational context. Each department will have to explore for themselves how these elements can be translated into the actions, language, symbols and rituals that are suitable for their own target group.
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In the following paper I investigate the relation between the purpose of Facebook use and its possible relation with students’ previous education and their subjective study success in higher education. Three surveys (six in total) were conducted in two successive years (cohort 2011-2012 and 2012-2013) amongst the first year students in the Department of Media, Information and Communication at the Amsterdam University of Applied Sciences.Facebook use will be categorized, according to a previous paper, by the motives of Facebook use:1) for information sharing2) for educational purposes3) for social purposes4) for leisure.Furthermore, the use of special group pages on Facebook is also compared with the students’ previous education. The subjective study success is measured by questioning how much time a student thinks he needs to complete all first year exams and is measured in all three surveys in both years, to uncover possible changes in their opinion. All variables are measured amongst the 904 students in both cohorts, using digital surveys and all data is analysed with the help of statistical tests. This study is part of a broader (PhD) research in which I investigate the possible relation between media literacy and students’ success in higher education.
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