Measuring students’ transition into university and its association with learning outcomes

Abstract

Previously we showed how we measured pedagogy and revealed its association with learning outcomes of sixth-form college mathematics students. In this project we followed a similar approach to the study of university transition. We particularly sought to identify the students’ perceptions of the transitional experience, and measure the association with learning outcomes. We drew on longitudinal surveys of students entering different programmes in five universities. Following them into their first year or so, allowed us to track their ‘disposition to complete the course’ and their ‘disposition to study more mathematics’, inter alia. We developed and validated two ‘fit-for-purpose’ measures of students’ perception of their transition, one we call ‘perception of the transitional gap/jump’ and one we call ‘degree of positive feeling about the transition’. We report some statistically and educationally significant associations between these and the students’ developing dispositions, and discuss the prospects for this approach to studying transition.

Notes

1. This was an ESRC funded project entitled “Mathematics learning, identity and educational practice: the transition into Higher Education”. More information can be found at www.transmaths.org.

2. In UK, this is associated with the second year of post-secondary education (in colleges) when students get their A levels (A2) in various subjects.

3. The difference between the ‘summer’ and ‘first weeks of university’ data was explored in all the modelling.

4. The project was entitled “Keeping Open the Door to Mathematically Demanding Programmes in Further and Higher Education”. More information can be found in www.transmaths.org.

5. It should be noted that a basic difference between the two instruments was that our previously validated was a teacher self-report one, whereas this one was completed by students.

6. Analysis was performed with the FACETS software.

7. We acknowledge the importance of checking for Differential Item Functioning within this framework, however this presentation goes beyond the scope of this paper.

8. The Index of Multiple Deprivation brings together 37 different indicators that cover specific aspects or dimensions of deprivation: Income, Employment, Health and Disability, Education, Skills and Training, Barriers to Housing and Services, Living Environment and Crime. These are weighted and combined to create the overall IMD 2007 (see http://www.communities.gov.uk/communities/neighbourhoodrenewal/deprivation/deprivation07).

9. This was constructed based on the combination of qualifications and grades achieved inMathematics; further exploration of this variable goes beyond the scope of this paper.

10. We also tried and developed a measure using the combination of the two sets of data, but as the concluding measure was very highly correlated with the feelings measure we will not pursue this here, except to say that it did not produce significantly different models.

11. The figure ‘T’ coming next to each categorical variable stands for ‘treatment’, denoting the dummy coding scheme employed. The reference categories are also reported in the Tables and these are “Asian” for ethnicity, “Engineering” for subject area and “bilingual” for language.

12. It should be noted that the y-axis of the effect plots are constrained (and not adjusted to a common scale) in order to show the individual effects of each explanatory variable. Therefore they should be read with this in mind to avoid misinterpretation of the relative relationships.

13. A final note about our modelling follows from Footnote 2 (in regards to the two distinct points of collecting data at DP1): we have controlled for this indicator in both our models and even though some small effects were found these did not reach statistical significance because of the small summer (‘before exams announcement’) sample. However, the trends indicate larger from the reported effects for the summer subsample.