Search:
Computing and Library Services - delivering an inspiring information environment

Mapping behavioural – related retention factors using a learning community lens: A mixed methods approach.

Chrysikos, Alexandros (2016) Mapping behavioural – related retention factors using a learning community lens: A mixed methods approach. Doctoral thesis, University of Huddersfield.

[img]
Preview
PDF - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (5MB) | Preview

Abstract

This study investigated the experiences of undergraduate learning communities in a UK Higher Education Institution and the causes that may lead to low retention rates amongst first year undergraduate computing students. Using learning communities as a lens, the authors examined students’ perception of teamwork experiences, academic and social integration issues, and knowledge and characteristics that might help students to be successful.
Four research questions guided the current study: (1) How do first year undergraduate computing students perceive their university experience? (2) To what depth and breadth does learning community participation affect social and/or academic integration? (3) What are the identified barriers/limitations to improve retention? (4) What learning characteristics or knowledge do students maintain and how are they accomplished?
The study applied a mixture of quantitative and qualitative research methods using a concurrent triangulation. Firstly, a quantitative data analysis was performed including first year undergraduate students from various departments of the examined UK Higher Education Institution. Tinto’s model of student retention connects to behavioural patterns. Behavioural patterns were therefore identified using data collected from students in order to map factors as predictors for low student retention. The data collection was driven by the information collected when students enrol at the university, as well as Pascarella and Terenzini’s questionnaire (integration scales). The data was analysed using the Structural Equation Modelling (SEM) technique which offers the opportunity to test various theoretical models, such as Tinto’s, through understanding of how sets of variables characterise constructs, and in what ways these constructs are associated to one another. The quantitative data analysis results suggested that the theory of Tinto proved to be beneficial in analysing retention in first year undergraduate students. Not at its maximum potential, though, because the model variables accounted for only a modest amount of variance in retention. Nevertheless, the data analysis discovered important relationships amongst student’s initial and later academic goals and commitments. In particular, the results revealed that academic and social integration constructs can have a significant influence on student retention processes. It is recommended that when all or some of these relationships are operating towards students’ benefit, it may be necessary to promote them with appropriate services or programmes, such as student support systems.
Secondly, after the quantitative approach was applied to the aforementioned large-scale comparative study within the institution, a qualitative approach was used to further explore student needs. Specifically, during the quantitative phase data from all first year students of the institution studied was collected in order to offer the opportunity for a comparison amongst students from different course divisions, and investigate any major similarities and/or differences regarding factors affecting retention. As this phase identified similar factors amongst all students, the qualitative phase was employed in order to narrow down the research focus. Therefore, the qualitative approach offered the opportunity for a thorough exploration of the first year computing students’ reasons for dropping out of university through the use of the ‘unfolding matrix’. The matrix was completed during group interviews, in which students were invited, and had the opportunity to read and comment on previous students’ experiences. The findings of the qualitative data analysis offered further insights, which were then mixed with the quantitative results and interpreted as one.
The final results, which were an interpretation of both quantitative and qualitative findings, revealed that learning communities critically affect students’ academic and social integration. Specifically, the importance of student support and guidance from academic staff were considered important factors which could enhance students’ motivation to continue their education. Their relationships with fellow students and academic staff were reported as vital elements in order to become academically and socially integrated. In addition, developing a sense of personal awareness and the need to develop an effective academic skill-set in order to succeed was identified as critical.

Item Type: Thesis (Doctoral)
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Schools: School of Computing and Engineering
Depositing User: Elizabeth Boulton
Date Deposited: 23 Jun 2016 10:05
Last Modified: 03 Dec 2016 12:42
URI: http://eprints.hud.ac.uk/id/eprint/28704

Downloads

Downloads per month over past year

Repository Staff Only: item control page

View Item View Item

University of Huddersfield, Queensgate, Huddersfield, HD1 3DH Copyright and Disclaimer All rights reserved ©