This paper examines which factors determine academic achievement for a non-traditional group of students in Dutch university education, namely, professional bachelors. We expected that different factors affected achievement for these non-traditional students. The educational productivity model, a model that specifies nine factors that influence achievement, was examined for two groups of students: professional and university bachelors. A total of 1964 university bachelors and 208 professional bachelors filled in a questionnaire on motivation to choose for the study, on quality of instruction and on the social-psychological environment. Analyses indicated that professional bachelors have higher intrinsic and extrinsic motivation, but lower generic skills and perceptions of appropriate workload. Covariance analyses indicated that for professional and university bachelors intrinsic motivation was positively related to perceptions of good teaching and good teaching was related to generic skills. The analyses for university bachelors indicated that a more appropriate workload and less generic skills were related to less time spend on task. This relationship was not found for the professional bachelors. Finally, we found a positive relationship between workload, generic skills, time on task and academic achievement for university bachelors; for professional bachelors only the relationship between workload, time on task and academic achievement was found. These results indicate that different factors affect achievement for non-traditional groups of students. These findings need to be taken into account when examining how teachers and universities can affect achievement in university education.

Introduction

Which factors determine academic achievement? This question is examined with a non-traditional group of students in Dutch university education, namely, professional bachelors. The bachelor-master structure in the Netherlands allows students with a professional bachelor diploma to enter university master programs. These professional bachelors differ from university bachelors in their domain-specific knowledge and skills. Therefore professional bachelors first enroll in a pre-master program to improve their domain-specific knowledge and skills. We expect that these non-traditional students, characterized by being older, having a part-time job and less time for study, have different experiences in university education. Thus different factors may be important to determine their academic achievement.

We examine a model which specifies factors that influence achievement for professional bachelors and university bachelors: the educational productivity model. (Reynolds, & Walberg, 1992; Bruinsma & Jansen, 2007). This model specifies nine factors divided over three clusters: student-aptitude attributes, instruction and social-psychological environment. The first cluster concerns ability, motivation and age. The second concerns qualitative and quantitative aspects of instruction. The third cluster concerns the social-psychological environment, i.e. home environment, class climate, school climate and peer climate, as well as exposure to mass-media. To examine which of these factors determine achievement of professional and university bachelors, we specified the following research questions:

 

Method

Data stems from the University of Groningen student-satisfaction survey. In 2008, 1964 university bachelors and 208 professional bachelors filled in a questionnaire on motivation to choose for the study, on quality and quantity of instruction and on perceptions of the social-psychological environment.

Academic achievement was measured in terms of credits obtained until the time of measurement, corrected for number of years in the program. Motivation was measured in terms of extrinsic motivation (4 items, α = .83) and intrinsic motivation (4 items, α = .68). Quantity of instruction was measured by students’ time on task, i.e. students indicated how many hours per week they spend on their study. Quality of instruction was measured in terms of appropriate workload (α = .88) and generic skills (α = .74) from the Course Experience Questionnaire (CEQ). The social-psychological environment was measured by classroom climate and support from the environment. The classroom climate was a scale from the CEQ and consisted of six items such as “my lecturers were very good at explaining things”, α = .83. Support was measured with one item on support from teachers outside class hours.

 

Results and conclusions

Analyses indicated differences in student-aptitude attributes and in perceptions of quality of instruction. Professional bachelor students are the older students and show higher scores for both intrinsic and extrinsic motivation. They also perceive a higher workload and indicate less that the program has contributed to their generic skills. There were no significant differences in factors related to the social-psychological environment, nor in time on task during regular college periods. Interestingly, a significant difference was found in time on task when preparing for examinations; professional bachelors indicate to spend less time on task.

We specified a model similar to the Reynolds and Walberg 1992 model and assumed that quality of instruction affects outcomes directly and indirectly through time on task (Bruinsma & Jansen, 2007). Covariance analyses indicated that for both professional and university bachelors intrinsic motivation was positively related to perceptions of good teaching. Further, good teaching was related to appropriate workload, generic skills and time on task for university bachelors. In contrast, only the relationship between good teaching and generic skills was found for professional bachelors. The analyses for university bachelors indicated that a more appropriate workload and less generic skills were related to less time spend on task. These relationships were not found for the professional bachelors. Finally, we found a positive relationship between workload, generic skills, time on task and academic achievement for university bachelors; for professional bachelors only the relationship between workload, time on task and academic achievement was found.

The analyses suggest several similarities between the two groups; there are interesting differences though. For example, when there is less perceived support from teachers outside the classroom, professional bachelors spend more time on task, whereas university bachelors spend less time on task. Further, appropriate workload and generic skills are related to time on task for university bachelors but not for professional bachelors. Also, a positive effect was found from generic skills on achievement for university bachelors; for professional bachelors a negative, though not significant, effect from generic skills on achievement was found.

Our analyses indicate differences between professional and university bachelors in motivation to choose for the study, appropriate workload and generic skills. Professional bachelors have higher intrinsic and extrinsic motivation, but lower generic skills and perceptions of appropriate workload. The importance of workload and time on task for professional bachelors might be explained by the fact that most of these students have jobs beside their study. Therefore, good time-management skills are needed to prevent difficulties with workload and time on task. For these students, it seems important to focus on generic skills in programs and optimising workload.

As expected, different models of educational productivity were found to explain academic achievement. It was interesting to note that the indirect relationship between quality of instruction and achievement through time on task was not found for the professional bachelors. Evidently non-traditional and traditional students have different experiences in university education. These different experiences need to be taken into account when examining how teachers, schools and policy influence learning and academic.

   

References

Bruinsma, M. & Jansen, E.P.W.A. (2007). Educational productivity in higher education: An examination of part of the Walberg Educational Productivity Model. School Effectiveness and School Improvement, 18(1), 45-65.

Reynolds, A. J. & Walberg, H. J. (1992). A structural model of science achievement and attitude: An extension to high school. Journal of Educational Psychology, 84, 371-382.