22 SES 01 A, Diversity and Inequality in Higher Education
Vocational literature has shown that students who pick a higher education study program matching their interests, also called a good person-environment fit (PE fit), have better study results and have a better chance of finishing their chosen study program in timely fashion (Tracey & Robbins, 2006). For studying vocational interests and PE interest fit, the Holland RIASEC model has a long standing tradition as being one of the most influential models not only in a working environment but also in (higher) education (Holland, 1959; Toomey, Levinson, & Palmer, 2009; Zener & Schnuelle, 1976). The RIASEC model’s basic principle is very straightforward. Persons (students) and environments (study programs) are represented on the same clockwise hexagon, containing six dimensions, while using the same RIASEC code: Realistic, Investigative, Artistic, Social, Enterprising, and Conventional (Lippa, 1998). By comparing the profiles of a student and a study program, PE fit indicates how well a (future) student’s interests match a specific study program. This research line however, focuses heavily on the student side of the interest PE fit relationship, while leaving the study program environment underdeveloped at best (Nauta, 2010). To our knowledge, studies normally do not focus on how study programs in higher education are set up in terms of access (open or locked behind entry exams) and tuition fees. Also, the influence this set up has on important variables like interest diversity and study results is usually not taken into account when drawing out the conclusions of a vocational interest study. This is an important oversight in literature, since the (national) setup of higher education study programs is not necessarily identical. For example, the Eurydice program installed by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission lists fourteen different elements of importance to the education set up of 43 countries and autonomous regions (Eurydice, 2017). Specifically, the element higher education mentions two broad categories of interest: institution types and program types. The institutions can be academic or professional, with (or without) a specific research mission, while being privately or publically funded. The programs can be of three cycles, approximately matching the bachelor – master – Ph.D. structure. Apart from these categories, Eurydice (2017) lists a number of additional aspects that can be of possible importance towards student interest research. For instance, the admission to a specific study program can be open or locked behind entrance exams or other performance requirements. As a consequence of these elements, the internal diversity of interest and eventual student study results of that specific study program could vary hugely depending on the country’s higher education set up. For sure, study programs without entry exams and hefty tuition fees will have a totally different student makeup (i.e. in terms of students’ vocational interest and study success) than study programs who are subject to such a set up. In theory, open access, low tuition study programs should be (completely) subjected to the homogeneity assumption. This assumption in vocational literature predicts that individuals (students) with similar vocational interests will drift towards the same (study program) environment (Holland, 1985). When determining a study program interest profile however, almost no attention is paid to this assumption regarding vocational interest. In a unique open access, low tuition education set up, this study will try to provide an answer to two research questions. How diverse are student interests within and between study programs in an open access, low tuition fee higher education set up (H1 and H2)? And how does this student interest diversity vary between study programs, to the extent it influences study results over study programs (H3 and H4)?
Students starting an academic bachelor at a Western European university across eleven faculties and 41 study programs, with an open access policy (only a high school degree is required) and very modest yearly tuition fees, were asked to fill out the SIMON-I online interest questionnaire in the starting week of their curriculum (Fonteyne et al., 2017). Response rate was 71% (N = 4827, 57% female). This questionnaire resulted in a RIASEC profile for each student. At the end of the academic year, the results from the SIMON-I test were cross referenced to the study results. Students from the study programs Medicine and Dentistry (n = 192) were excluded from analyses since they already had to pass an exam to be admitted to the study program. The specific study program RIASEC profiles were constructed by averaging out the RIASEC dimensions of the students in the specific study programs (Allen & Robbins, 2010). Average study results were also calculated for each study program by averaging out the measures of the students. Next, the PE fit of vocational interest between a student and his study program was established using two measures, correlation fit and Euclidean distance (Tracey et al., 2012). The profile for each study program will be expanded with two measures of interest diversity: pattern shape deviance and scatter diversity based on the average Euclidean distance and correlational fit. Hypotheses and Analyses H1: A specific study program displays homogeneity. H2: The general study program displays homogeneity. H3: PE interest fit influences study results at the personal level. H4: Study program interest diversity influences study results at the program level. To test H1 from our first research question, we compared the study programs of our main body of students to our CG on study results and interest diversity using one sample, two sided t-tests. To test H2 and H3 from our second research question, we adapted the procedures suggested by Burke and Dunlap (2002) to test for homogeneity using deviance statistics. To test H4 from our third research question, we performed regressions of study results on PE interest fit at the student level. To test H5, we also performed regressions of study program average study results on study program interest diversity at the study program level.
Within this open access, low tuition environment, descriptive statistics revealed that study programs had low interest diversity, due to the high average PE fit of students with their chosen majors. This means that students, when given the chance, seem to choose a major that nicely fit their vocational interests. In sum, we advocate to incorporate the (national) higher education set up of study programs in future study design when setting up environment profiles and when drawing conclusions from studies of vocational interest. This study’s major advantage of a unique open access, low tuition set up is also his biggest limitation; more research is needed in different set ups. Moreover, this interest homogeneity seemed to be widespread over study programs, with only three to four study programs out of a possible 39 (depending on the measures) showing formal interest diversity. However, study results were quite low, with majors averaging a success rate of 41%, with a grade point average of about 46%. Student PE interest fit only had a very small influence on student’s study results (best case scenario, only 1% explained variance). However, at the study program level of analyses, interest diversity had a profound influence on the average study program study results, explaining up to 42% of the variance. Remarkably, the measures used showed two types of relations between interest diversity and study results: a straightforward linear one and a curvilinear one. Post hoc analyses revealed the social RIASEC dimension could provide an explanation for this curvilinear phenomenon. Study programs combining a low interest diversity with a high social dimension or a high interest diversity with a lower social dimension seemed to have better average study results than study programs not showing this combination.
Allen, J. & Robbins, S. (2010). Effects of Interest–Study program Congruence, Motivation, and Academic Performance on Timely Degree Attainment. Journal of Counseling Psychology, 57, 1, 23-35. doi:10.1037/a0017267 Eurydice (2017). Retrieved November, 27, 2017, from https://webgate.ec.europa.eu/fpfis/mwikis/eurydice/index.php/Countries Fonteyne, L., Wille, B., Duyck, W., & De Fruyt, F. (2017). Exploring vocational and academic fields of study: development and validation of the Flemish SIMON Interest Inventory (SIMON-I). International Journal for Educational and Vocational Guidance, 17, 2, 233-262. doi:10.1007/s10775-016-9327-9 Holland, J. L. (1985). Making vocational choices: A theory of careers. Englewood Cliffs, NJ: Prentice Hall. Holland, J.L. (1959). A theory of vocational choice. Journal of Counseling Psychology, 6, 35–45. doi:10.1037/h0040767 Lippa, R. (1998). Gender-related individual differences and the structure of vocational interests: the importance of the people-things dimension. Journal of Personality and Social Psychology, 74, 4, 996-1009. doi:10.1037/0022-3522.214.171.1246 Nauta, M.M. (2010). The development, evolution, and status of Holland's theory of vocational personalities: reflections and future directions for counseling psychology. Journal of Counseling Psychology, 57, 1, 11–22. doi:10.1037/a0018213 Toomey, K.D., Levinson, E.M., & Palmer, E.J. (2009). A test of Holland’s theory of vocational personalities and work environments. Journal of Employment Counseling, 46, 2, 82-93. doi:10.1002/j.2161-1920.2009.tb00070.x Tracey, T. J. G., & Robbins, S. B. (2006). The interest-study program congruence and college success relation: A longitudinal study. Journal of Vocational Behavior, 69, 1, 64-89. Tracey, T.J.G., Allen, J., & Robbins, S.B. (2012). Moderation of the relation between person-environment congruence and academic success: environmental constraint, personal flexibility and method. Journal of Vocational Behavior, 80, 1, 38-49. doi:10.1016/j.jvb.2011.03.005 Zener, T. B., & Schnuelle, L. (1976). Effects of the Self-Directed Search on high school students. Journal of Counseling Psychology, 23, 4, 353-359. doi:10.1037/0022-0126.96.36.1993
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