A Structural Model For Student Success And The Predictive Value Of A Study Choice Test

Research output: Contribution to conferenceAbstractAcademic


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.
Original languageEnglish
Publication statusPublished - Mar 2020
Event14th International Technology, Education and Development Conference - Valencia, Spain
Duration: 2 Mar 20204 Mar 2020


Conference14th International Technology, Education and Development Conference
Abbreviated titleINTED2020


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