Leveraging AI and Machine Learning for National Student Survey: Actionable Insights from Textual Feedback to Enhance Quality of Teaching and Learning in UK’s Higher Education

Nawaz, Raheel and Sun, Quanbin and Shardlow, Matthew and Kontonatsios, Georgios and Aljohani, Naif R. and Visvizi, Anna and Hassan, Saeed-Ul (2022) Leveraging AI and Machine Learning for National Student Survey: Actionable Insights from Textual Feedback to Enhance Quality of Teaching and Learning in UK’s Higher Education. Applied Sciences, 12 (1). e514. ISSN 2076-3417

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Students’ evaluation of teaching, for instance, through feedback surveys, constitutes an integral mechanism for quality assurance and enhancement of teaching and learning in higher education. These surveys usually comprise both the Likert scale and free-text responses. Since the discrete Likert scale responses are easy to analyze, they feature more prominently in survey analyses. However, the free-text responses often contain richer, detailed, and nuanced information with actionable insights. Mining these insights is more challenging, as it requires a higher degree of processing by human experts, making the process time-consuming and resource intensive. Consequently, the free-text analyses are often restricted in scale, scope, and impact. To address these issues, we propose a novel automated analysis framework for extracting actionable information from free-text responses to open-ended questions in student feedback questionnaires. By leveraging state-of-the-art supervised machine learning techniques and unsupervised clustering methods, we implemented our framework as a case study to analyze a large-scale dataset of 4400 open-ended responses to the National Student Survey (NSS) at a UK university. These analyses then led to the identification, design, implementation, and evaluation of a series of teaching and learning interventions over a two-year period. The highly encouraging results demonstrate our approach’s validity and broad (national and international) application potential—covering tertiary education, commercial training, and apprenticeship programs, etc., where textual feedback is collected to enhance the quality of teaching and learning.

Item Type: Article
Additional Information: ** From MDPI via Jisc Publications Router ** History: accepted 29-12-2021; pub-electronic 05-01-2022. ** Licence for this article: https://creativecommons.org/licenses/by/4.0/
Identification Number: https://doi.org/10.3390/app12010514
29 December 2021Accepted
5 January 2022Published Online
Uncontrolled Keywords: National Student Survey (NSS), Education for Sustainable Development (EDS), AI for education, higher education policy making, intervention strategies
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
CAH22 - education and teaching > CAH22-01 - education and teaching > CAH22-01-01 - education
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
SWORD Depositor: JISC PubRouter
Depositing User: JISC PubRouter
Date Deposited: 17 Jan 2022 14:07
Last Modified: 17 Jan 2022 14:07
URI: https://www.open-access.bcu.ac.uk/id/eprint/12623

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