Drivers of academic pathways in higher education: traditional vs. non-traditional students

ABSTRACT

Dropout rates in higher education (HE) are particularly high for non-traditional students which may be due to unadjusted educational policies. Considering as non-traditional the students who are employed at the enrolment moment and using a longitudinal database containing information on 5351 students from a Portuguese HE institution, an event history analysis approach is employed to distinguish the main drivers of graduation and dropout risks for traditional and non-traditional students and to test a set of research hypotheses. The results show significant differences between the two types of students, confirming the need of discretionary policies. For non-traditional students, policies that assist them in an early stage are shown to be of critical importance, for example by offering pre-enrolment preparatory courses or by joining them, in the classroom, with students with similar characteristics. For traditional students, providing solutions for financial limitations and promoting academic integration seem to be more effective.

KEYWORDS:

• Traditional students
• non-traditional students
• event history analysis
• competing risks
• dropout
• education policy

Acknowledgements

The authors gratefully acknowledge Polytechnic of Leiria, particularly the presidency and planning office, for the authorization to use their internal databases essential for the investigation.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Available at https://stats.oecd.org/ (Productivity > Productivity and ULC (Unit Labor Cost) – Annual, Total Economy > Growth in GDP per capita, productivity and ULC > indicator “Growth in GDP per capita, productivity and ULC”).

2 In a recent survey conducted by the authors and sent to all working students in HE in Portugal in 2018/2019, which had 234 validated answers, the main motives to enroll in HE were found to be ‘to increase skills’ and ‘to increase professional opportunities’ (median of 7 in a 1–7 scale), ‘career progression’ (median 6), ‘to increase social status’ (median 5), and ‘to find a new profession’ and ‘to meet people’ (median 4).

3 Social integration is typically defined as the students’ satisfaction with the formal and informal social systems of the school and includes essentially the social relations with other students and staff in the school, while academic integration is usually defined as a measure of student’s connection and involvement with the school and includes essentially the relation with the school, departments, services and professors from an academic point of view.

4 A student enrolled in an e-learning degree is expected to be less socially and academically integrated, as compared to a student attending classes in school.

5 It is likely that a working student feels more socially integrated when surrounded by other working students who face similar difficulties.

6 It is important to note that students may be loaded some credits at the initial enrolment moment due to past formation, which is naturally frequent in returns, but also common to observe in course-institution change and TOC. By including these admission regimes in the present study, graduations in the first-two years of enrolment are made possible, even with a three-year normal duration of the courses.

7 ‘Entry cohort is used, instead of the leaving cohort, in order to standardize for time-varying influences’ (Naylor and Smith 2004).

8 The exception is the ‘Other admission regimes’, as it includes international students who usually do not have a professional activity.

9 The exception in multicollinearity is between the variables age and age-squared, which are naturally correlated. However, as documented in the literature, this does not raise the typical multicollinearity problems. For robustness check purposes, the model was estimated considering only students that start their degree from zero, i.e. excluding those admitted through returns, course-institution change, TOC and other regimes. The results (available upon request to the authors) are very similar to the ones in Table 2 for most of the variables, especially for traditional students. For non-traditional students, the effects of time (first year), age, management field and unemployment loose some magnitude. Moreover, given that it was not possible to include in the model variables as the number of credits enrolled and academic marks obtained each year, typically relevant in explaining graduation and dropout rates, the model was also estimated including two proxies for such variables (the dummy variable part-time, identifying those students who adopted the part-time student status, which allows to reduce the number of credits enrolled below normal/full load and the correspondent tuition costs per year, for the number of credits enrolled; and the average number of years of enrolment per curriculum year for the academic marks) to investigate possible biases in the estimated coefficients in the main model. Again, the results (available upon request to the authors) are very similar to those in Table 2 with no evidence of additional multicollinearity problems and of biases in the coefficients in the main model.

10 The outputs of the additional estimations of the model discussed throughout the last two paragraphs, omitted in the text, are available upon request to the authors.

11 A student with previous experience in HE is probably more academically integrated than a student enrolling in HE for the first time.