International student voice and academic advising: a demand-side analysis using machine learning and qualitative interviews

Rabeya Khatoon (University of Bristol, UK)
Claire Spencer (University of Bristol)

Tuesday, April 4, 2023 3:30 PM - 4:15 PM

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Session Outline

Academic advising is now crucial in the UK following the Higher Education and Research Act, 2017 (Parliament, 2017). Lochtie et al. (2018) mentioned the system being separated from the mainstream education provision for many institutions which is often driven by the resource constraints. Institutions have been exploring different alternative ways of addressing the constraints, including different models of academic advising, e.g., personal development tutoring of Brown and Thomas, 2022, professional model of Lochtie et al., 2022, pastoral model of Grey and Lochtie, 2016, curriculum based model of Earwaker, 1992, or relying on intelligent web based applications (e.g., Henderson and Goodridge, 2015). In this paper we propose one such solution by offering a small survey with a selected set of questions using machine learning techniques. Following Jayachandran et al. (2023), we use feature selection methods of machine learning to link the true state revealed from over a hundred semi-structured interviews and the extensive survey with about a hundred questions to come up with a small survey with five key questions. Our goal is to predict a couple of indices measuring the barriers to reaching out to academic advising services and the perception of academic advising of postgraduate (mainly international) students. Our analysis based on Lasso and random forest feature selection methods identify that English as a second language can act as a significant barrier for international postgraduate students to thrive in their short learning journey abroad. From the student's voices, our study confirms that academic advising has a broad coverage ranging from subject expertise to mentoring and coaching to allow ways to overcome language and cultural barriers that a diverse student cohort might face. We believe our proposed small survey questions will be useful for practitioners to collect crucial information and will help make the appropriate adjustments to direct the scarce resources toward the best possible use.

This session addresses the following competencies of the UKAT Professional Framework for Advising and Tutoring
I7 - Data and information technology applicable to tutoring
I5 - The characteristics, needs, and experiences of major and emerging student populations
P1 - Create and support environments that consider the needs and perspectives of students, and respect individual learners