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Project

Improving the guidance and support of transfer students in engineering: A validated diagnostic test and effective interventions

In order to stimulate flexible lifelong learning, the educational system of Flanders provides alternative ways to enter a Master’s programme, next to the traditional academic Bachelor’s programme. Students who obtained a professional Bachelor’s degree can enrol in an academic Master’s programme provided that they successfully complete a transfer programme. In general, these  transfer students enter university for the first time, just like traditional first-year students. Transfer students at the Faculty of Engineering Technology (FET) experience similar adaptation problems as the first-year students at FET. However, transfer students feel significantly less prepared. Nevertheless, their outcome variables (i.e. academic achievement and dropout rate) after one year of enrolment are similar. Approximately 50% of the transfer students drop out. This high dropout rate is the principal rationale for this research. It is of paramount importance to improve the guidance of students in their educational choice before enrolment as well as to provide them with the required support once they are enrolled. In order to achieve this, the following steps are performed: (1) development of a validated diagnostic test and (2) development and implementation of effective interventions (i.e. a student support programme).

The diagnostic test is voluntary, non-binding (i.e. if students do not pass the test, they can still enrol in the programme), and preferably organised before enrolment in the transfer programme. The objectives of the test are (1) to provide students with feedback about their skills and capacities thus to stimulate them to make a well-considered educational choice; and (2) to encourage students to participate in interventions before or during their transfer programme in order to overcome stumbling blocks. The development of the diagnostic test was an iterative process over multiple years, of which the first version was based on (1) an analysis of a diagnostic test for the regular first-year students and (2) students’ stumbling blocks, mathematics and study strategies, which were defined during focus group discussions. It is important that the test includes both cognitive and non-cognitive tests, since students need to realise that both are important for study success at university.

Given that the aim is to properly inform students before enrolment and support them after enrolment, two types of advisory models, based on pre-entry characteristics, are distinguished. The first model, a students’ background model, only includes fixed variables (i.e. prior schooling). This model explains a substantial part of variation in students’ grades, but since these variables are fixed, the students’ background model is primarily useful before enrolment. The second model, a diagnostic model, only includes malleable variables (i.e. skills and abilities) that are measured in the diagnostic test. Although this model does not explain the same amount of variance in students’ grades as the students’ background model, the diagnostic model is useful both before and after enrolment. When students receive actionable feedback about their test results and are given the opportunity to participate in interventions, they can enhance their skills and abilities, which in turn can improve students’ academic achievement.

Eight interventions were developed in this dissertation and combined into a student support programme. This student support programme starts in the third year of the professional Bachelor’s programme and ends after the transfer programme. The student support programme aims to (1) attract the right students, (2) decrease the feeling of unpreparedness at the beginning of the academic year, and (3) support students after enrolment. Both the effectiveness and efficiency of the eight interventions are examined. The results are combined into an effectiveness/efficiency matrix. This study shows that the most effective interventions are not always the most efficient and vice versa. For two interventions that focus on the students’ stumbling blocks, mathematics and study strategies, a more in-depth analysis of the effectiveness is performed. In this study there was significant evidence for the effectiveness of the mathematics MOOC.

Date:7 Nov 2014 →  25 Feb 2019
Keywords:Bridging students, Engineering Technology, Diagnostic test, Interventions
Disciplines:Education curriculum, Education systems, General pedagogical and educational sciences, Specialist studies in education, Other pedagogical and educational sciences
Project type:PhD project