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Impact of UK Postgraduate Student Experiences on Academic Performance in Blended Learning: A Data Analytics Approach

Muhidin Mohamed, Shubhadeep Mukherjee, Bhavana Baad

TL;DR

This study addresses how UK postgraduate students' experiences in blended learning relate to their academic performance. Using a data-analytic, survey-based approach guided by the Community of Inquiry framework, the authors analyze satisfaction with teaching methods, workload, stress, motivation, and staff support alongside self-reported performance. They demonstrate that teaching and social presences, particularly effective feedback and timely staff responses, strongly predict better outcomes, while higher workload and stress degrade performance. The study extends CoI by revealing four latent learner profiles, showing heterogeneity in how presences combine to influence learning in blended postgraduate settings. Practically, the findings argue for adaptive BL designs that balance workload, support wellbeing, and foster social and teaching presence to improve postgraduate success in UK universities.

Abstract

Blended learning has become a dominant educational model in higher education in the UK and worldwide, particularly after the COVID-19 pandemic. This is further enriched with accompanying pedagogical changes, such as strengthened asynchronous learning, and the use of AI (from ChatGPT and all other similar tools that followed) and other technologies to aid learning. While these educational transformations have enabled flexibility in learning and resource access, they have also exposed new challenges on how students can construct successful learning in hybrid learning environments. In this paper, we investigate the interaction between different dimensions of student learning experiences (ranging from perceived acceptance of teaching methods and staff support/feedback to learning pressure and student motivation) and academic achievement within the context of postgraduate blended learning in UK universities. To achieve this, we employed a combination of several data analytics techniques including visualization, statistical tests, regression analysis, and latent profile analysis. Our empirical results (based on a survey of 255 postgraduate students and holistically interpreted via the Community of Inquiry (CoI) framework) demonstrated that learning activities combining teaching and social presences, and tailored academic support through effective feedback are critical elements for successful postgraduate experience in blended learning contexts. Regarding contributions, this research advances the understanding of student success by identifying the various ways demographic, experiential, and psychological factors impact academic outcomes. And in theoretical terms, it contributes to the extension of the CoI framework by integrating the concept of learner heterogeneity and identifying four distinct student profiles based on how they engage in the different CoI presences.

Impact of UK Postgraduate Student Experiences on Academic Performance in Blended Learning: A Data Analytics Approach

TL;DR

This study addresses how UK postgraduate students' experiences in blended learning relate to their academic performance. Using a data-analytic, survey-based approach guided by the Community of Inquiry framework, the authors analyze satisfaction with teaching methods, workload, stress, motivation, and staff support alongside self-reported performance. They demonstrate that teaching and social presences, particularly effective feedback and timely staff responses, strongly predict better outcomes, while higher workload and stress degrade performance. The study extends CoI by revealing four latent learner profiles, showing heterogeneity in how presences combine to influence learning in blended postgraduate settings. Practically, the findings argue for adaptive BL designs that balance workload, support wellbeing, and foster social and teaching presence to improve postgraduate success in UK universities.

Abstract

Blended learning has become a dominant educational model in higher education in the UK and worldwide, particularly after the COVID-19 pandemic. This is further enriched with accompanying pedagogical changes, such as strengthened asynchronous learning, and the use of AI (from ChatGPT and all other similar tools that followed) and other technologies to aid learning. While these educational transformations have enabled flexibility in learning and resource access, they have also exposed new challenges on how students can construct successful learning in hybrid learning environments. In this paper, we investigate the interaction between different dimensions of student learning experiences (ranging from perceived acceptance of teaching methods and staff support/feedback to learning pressure and student motivation) and academic achievement within the context of postgraduate blended learning in UK universities. To achieve this, we employed a combination of several data analytics techniques including visualization, statistical tests, regression analysis, and latent profile analysis. Our empirical results (based on a survey of 255 postgraduate students and holistically interpreted via the Community of Inquiry (CoI) framework) demonstrated that learning activities combining teaching and social presences, and tailored academic support through effective feedback are critical elements for successful postgraduate experience in blended learning contexts. Regarding contributions, this research advances the understanding of student success by identifying the various ways demographic, experiential, and psychological factors impact academic outcomes. And in theoretical terms, it contributes to the extension of the CoI framework by integrating the concept of learner heterogeneity and identifying four distinct student profiles based on how they engage in the different CoI presences.

Paper Structure

This paper contains 21 sections, 2 equations, 11 figures, 17 tables.

Table of Contents

  1. Introduction
  2. Literature Review and the CoI Framework
  3. Related works
  4. The Community of Inquiry (CoI) framework
  5. Research objectives
  6. Research Design and Methodology
  7. Data Collection and preprocessing
  8. Data Analysis and Inference
  9. Profile of the study participants
  10. To what extent is academic achievement in BL dependent on satisfaction?
  11. How does teaching staff support affects students’ academic performance?
  12. What influence do study workload and stress have on academic achievement?
  13. Testing the dependence of student experience factors on academic performance
  14. Analysis based on Spearman test
  15. Analysis based on Mann-Whitney U test
  16. ...and 6 more sections

Figures (11)

  • Figure 1: A high-level illustration of the Community of Inquiry framework in the study context
  • Figure 2: Research methodology workflow
  • Figure 3: Age (A) and gender (B) distributions of the survey participants.
  • Figure 4: Distribution of study participants in terms of academic performance by gender and age group
  • Figure 5: Comparison of the study times for different student groups based on the sex and age group variables: rare classes ($n<10$) are excluded for lack statistical interpretability
  • ...and 6 more figures