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Article

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

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Department of Operations, Technology, Events and Hospitality Management, Manchester Metropolitan University, Manchester M15 6BH, UK
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Department of Computing & Data Science, Birmingham City University, Birmingham B4 7XG, UK
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Department of Computing and Mathematics, Manchester Metropolitan University, Manchester M15 6BH, UK
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Department of Computer Science, Edge Hill University, Ormskirk L39 4QP, UK
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Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Effat College of Business, Effat University, Jeddah 21551, Saudi Arabia
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Institute of International Studies (ISM), SGH Warsaw School of Economics, Al. Niepodległości 162, 02-554 Warsaw, Poland
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Authors to whom correspondence should be addressed.
Academic Editors: Jenny Pange and Zoi Nikiforidou
Appl. Sci. 2022, 12(1), 514; https://doi.org/10.3390/app12010514
Received: 25 November 2021 / Revised: 22 December 2021 / Accepted: 29 December 2021 / Published: 5 January 2022
(This article belongs to the Special Issue ICT and Statistics in Education)
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. View Full-Text
Keywords: National Student Survey (NSS); Education for Sustainable Development (EDS); AI for education; higher education policy making; intervention strategies National Student Survey (NSS); Education for Sustainable Development (EDS); AI for education; higher education policy making; intervention strategies
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MDPI and ACS Style

Nawaz, R.; Sun, Q.; Shardlow, M.; Kontonatsios, G.; Aljohani, N.R.; Visvizi, A.; Hassan, S.-U. 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. Appl. Sci. 2022, 12, 514. https://doi.org/10.3390/app12010514

AMA Style

Nawaz R, Sun Q, Shardlow M, Kontonatsios G, Aljohani NR, Visvizi A, Hassan S-U. 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. 2022; 12(1):514. https://doi.org/10.3390/app12010514

Chicago/Turabian Style

Nawaz, Raheel, Quanbin Sun, Matthew Shardlow, Georgios Kontonatsios, Naif R. Aljohani, Anna Visvizi, and Saeed-Ul Hassan. 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, no. 1: 514. https://doi.org/10.3390/app12010514

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