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; Sun, Quanbin; Shardlow, Matthew; Kontonatsios, Georgios; Aljohani, Naif Radi; Visvizi, Anna; Hassan, Saeed‐Ul

doi:10.3390/app12010514

Cited by 26 publications

(9 citation statements)

References 52 publications

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“…Although the above discussions point to the fact that degree apprentices are excelling and making significant contributions, it seems there is a need for more efforts from the government, IfATE, UCAS, training providers, employers, colleges and other stakeholders to create awareness and promote DAs. UCAS has taken a giant step toward apprenticeships in its 2020-2025 TG 17,3 corporate strategy (UCAS, 2022b); such initiatives must be supported by policymakers and rolled out across DA stakeholders in a sustainable (Visvizi et al, 2020;Visvizi and Daniela, 2019) and innovative (Aljohani et al, 2022;Nawaz et al, 2022) way.…”

Section: Discussionmentioning

confidence: 99%

The impact of degree apprenticeships: analysis, insights and policy recommendations

Nawaz

Edifor²,

Holland³

et al. 2022

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Purpose Degree apprenticeships in the UK have grown exponentially since they were introduced. While claims and speculations about their impact have been made by high-profile politicians, lobbyists and training providers, the evidence base for these claims has never been systematically scrutinised. The purpose of this paper is to analyse and present up-to-date peer-reviewed evidence on the impact of degree apprenticeships on social mobility and productivity and their influence on future policy. Design/methodology/approach The authors critically and systematically review over 4,000 data points from relevant sources and synthesise the results qualitatively and quantitatively with a combined method approach. Quantitative evidence has been aggregated where possible and tested for statistical significance. Qualitative evidence has been critically reviewed and comparatively analysed. Findings The analysis reveals that there is a shortage of depth and breadth of concrete and peer-reviewed evidence on the impact of degree apprenticeships. Nevertheless, existing data demonstrate that degree apprenticeships are meeting their intended purpose of contributing positively to the UK Government’s high-level goals for productivity and social mobility. They are an alternative route into higher education and serve as a vital talent pipeline providing opportunities for young people, existing employees and learners from diverse backgrounds. Originality/value This paper provides the first systematic and peer-reviewed synthesis of the impact of degree apprenticeships. It brings together existing evidence and statistically validates outcomes to inform researchers, educators, training providers, policymakers and other stakeholders in their recommendations moving forward.

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Section: Discussionmentioning

confidence: 99%

The impact of degree apprenticeships: analysis, insights and policy recommendations

Nawaz

Edifor²,

Holland³

et al. 2022

Self Cite

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“…Unsupervised learning is a class of ML techniques that analyze data without labeled responses in order to discover hidden patterns and groupings (Kotsiantis et al, 2006). Instead of mapping inputs to known outputs as in supervised learning, the key goal in unsupervised learning is to model the underlying structure and relationships in the data (Nawaz et al, 2022). Clustering is one of the most common unsupervised learning methods whereby the algorithm groups data points that are similar to each other into distinct clusters (Alpaydin, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches

Demir

2023

Journal of Research in Didactical Sciences

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Machine learning (ML) techniques hold promise for innovating teacher preparation and development programs. However, the current state of research leveraging AI in teacher-focused contexts remains unclear. This study undertook a systematic bibliometric analysis to characterize the emerging domain investigating ML applications for enhancing teacher effectiveness. Using the bibliographic R tool Bibliometrix, metadata of 740 English-language articles published during 2019–2023 extracted from Web of Science educational databases were examined to determine performance metrics, science mapping, citation networks, and research trends situating at the intersection of machine learning and teacher education. Document growth averaged 39.57% annually, with collaborations involving 87% of publications and 21.62% engaging international co-authorships. The USA led productivity metrics, though opportunities exist to expand geographical diversity. Analyses revealed research activity presently concentrates around employing ML for student analytics, assessment frameworks, and online learning environments. Highly cited works dealt with ML systems for evaluation and competency modeling of teachers rather than directly supporting pedagogical practice. Significant gaps persist exploring intelligent recommendation engines and affective computing chatbots tailored to teachers’ dynamic training needs and emotional responses. This bibliometric review synthesizes the contours and trends in investigating ML applications for augmenting teachers’ capabilities. Findings inform stakeholders to mobilize efforts strategically advancing this domain for enriching classrooms.

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“…With the continuous advancement and development of information management and construction of network multimedia English teaching, the cohesion level of college English teaching under the interactive network environment of database server is constantly improved. It is necessary to construct the cohesion of college English teaching under the interactive network environment of database server to improve the management and service level of network multimedia English teaching [1][2][3]. This paper constructs the big data fusion and resource scheduling model of college English teaching convergence platform in the database server interactive network environment, establishes the optimized retrieval model of college English teaching convergence multimedia in the database server interactive network environment, and improves the ability of college English teaching convergence and information management in the database server interactive network environment.…”

Section: Introductionmentioning

confidence: 99%

Construction of university English teaching connection model based on multimedia fusion

luo

2023

Preprint

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In order to improve the cohesion and quality of college English teaching, the cohesion model of college English teaching under the interactive network environment of database server is constructed. This paper puts forward a connecting platform for college English teaching under the interactive network environment of database server based on multimedia fusion. Build a big data analysis model of college English teaching convergence multimedia information in the database server interactive network environment, get the feature quantity of college English teaching convergence information in the queue resource quota manager, and combine the big data mining method to carry out multimedia information fusion and feature clustering processing of college English teaching convergence in the database server interactive network environment. The heterogeneous directed graph fusion clustering is adopted to design the optimal storage structure of college English teaching convergence multimedia information in the database server interactive network environment, and the semantic ontology features of college English teaching convergence multimedia information in the database server interactive network environment are extracted. Through feature optimization retrieval, the design of college English teaching convergence platform and multimedia information fusion in the database server interactive network environment are realized. Through the method of multimedia information fusion of college English teaching convergence under the interactive network environment of database server, differential scheduling is carried out to improve the ability of college English teaching convergence and multimedia information fusion under the interactive network environment of database server. The simulation results show that the designed college English teaching interface platform has high information fusion degree and strong resource allocation ability under the interactive network environment of database server.

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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

Cited by 26 publications

References 52 publications

The impact of degree apprenticeships: analysis, insights and policy recommendations

The impact of degree apprenticeships: analysis, insights and policy recommendations

Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches

Construction of university English teaching connection model based on multimedia fusion

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