Evaluating the Impact of a Guided Personalised Learning Framework for Undergraduate Engineering Education: A Data-Driven Empirical Study | Chapter 5 | Language, Literature and Education: Research Updates Vol. 10

 

The concept of personalised learning (PL) has evolved significantly in higher education, driven by the need to accommodate increasingly diverse student profiles and to promote more inclusive and effective learning environments. This study investigates the implementation and impact of the Guided Personalised Learning (GPL) model, a structured pedagogical framework designed to operationalise personalised and student-centred learning in STEM higher education. The GPL model integrates three interconnected components: a three-dimensional knowledge and skill grid, Interactive Learning Progress Assessments (ILPA), and an adaptive learning resource pool. These components were embedded into two undergraduate engineering modules, Network Engineering and Software Engineering, at a UK university. A mixed-method evaluation involving 741 students across two academic years, incorporating quantitative attainment analyses, qualitative student feedback, and both within-cohort and inter-cohort comparisons, was conducted. Statistical tests included F-tests, and Welch’s t-tests were conducted. Results show that students who engaged with GPL, particularly those who completed ILPA activities, achieved significant improvements, including higher mean grades, increased proportions of high achievers, and reduced failure rates. These findings demonstrate the GPL model’s effectiveness in supporting learner autonomy, formative assessment, and targeted feedback, while offering a scalable and evidence-based approach to integrating personalised learning into mainstream STEM curricula. This study is important for educators, curriculum designers, and institutions as it provides a practical framework for embedding personalisation into core teaching and learning processes. It recommends that staff development prioritise training in diagnostic assessment design, resource curation, and data-informed pedagogy, while strategic institutional investment should focus on interdisciplinary collaboration, technical integration, and continuous feedback mechanisms. Future studies should investigate the application of GPL in other disciplinary domains and at different academic levels, with longitudinal studies exploring the sustained impact of GPL on progression, retention, and academic identity formation.

 

 

Author(s) Details

Yue Chen
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.

 

Ling Ma
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.

 

Pireh Pirzad
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.

 

Kok Keong Chai
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK.

 

Please see the book here :- https://doi.org/10.9734/bpi/lleru/v10/6794