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Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics

Laura J. Johnston, Takoua Jendoubi

TL;DR

This paper introduces an interpretable and actionable model for student engagement, which integrates objective, data-driven analysis with students' perspectives and underscores the model's potential to improve online pedagogical practices across diverse educational settings.

Abstract

In the context of higher education's evolving dynamics post-COVID-19, this paper assesses the impact of new pedagogical incentives implemented in a first-year undergraduate computing module at University College London. We employ a mixed methods approach, combining learning analytics with qualitative data, to evaluate the effectiveness of these incentives on increasing student engagement. A longitudinal overview of resource interactions is mapped through Bayesian network analysis of Moodle activity logs from 204 students. This analysis identifies early resource engagement as a predictive indicator of continued engagement while also suggesting that the new incentives disproportionately benefit highly engaged students. Focus group discussions complement this analysis, providing insights into student perceptions of the pedagogical changes and the module design. These qualitative findings underscore the challenge of sustaining engagement through the new incentives and highlight the importance of communication in blended learning environments. Our paper introduces an interpretable and actionable model for student engagement, which integrates objective, data-driven analysis with students' perspectives. This model provides educators with a tool to evaluate and improve instructional strategies. By demonstrating the effectiveness of our mixed methods approach in capturing the intricacies of student behaviour in digital learning environments, we underscore the model's potential to improve online pedagogical practices across diverse educational settings.

Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics

TL;DR

This paper introduces an interpretable and actionable model for student engagement, which integrates objective, data-driven analysis with students' perspectives and underscores the model's potential to improve online pedagogical practices across diverse educational settings.

Abstract

In the context of higher education's evolving dynamics post-COVID-19, this paper assesses the impact of new pedagogical incentives implemented in a first-year undergraduate computing module at University College London. We employ a mixed methods approach, combining learning analytics with qualitative data, to evaluate the effectiveness of these incentives on increasing student engagement. A longitudinal overview of resource interactions is mapped through Bayesian network analysis of Moodle activity logs from 204 students. This analysis identifies early resource engagement as a predictive indicator of continued engagement while also suggesting that the new incentives disproportionately benefit highly engaged students. Focus group discussions complement this analysis, providing insights into student perceptions of the pedagogical changes and the module design. These qualitative findings underscore the challenge of sustaining engagement through the new incentives and highlight the importance of communication in blended learning environments. Our paper introduces an interpretable and actionable model for student engagement, which integrates objective, data-driven analysis with students' perspectives. This model provides educators with a tool to evaluate and improve instructional strategies. By demonstrating the effectiveness of our mixed methods approach in capturing the intricacies of student behaviour in digital learning environments, we underscore the model's potential to improve online pedagogical practices across diverse educational settings.
Paper Structure (14 sections, 1 figure)

This paper contains 14 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Related Works
  3. Mixed methods in educational research
  4. Bayesian network
  5. Methodology
  6. Context
  7. Learning analytics
  8. Focus group
  9. Results and Discussion
  10. Weekly code submissions
  11. Weekly feedback
  12. Module design
  13. Limitations
  14. Conclusion

Figures (1)

  • Figure 1: Bayesian Network Depicting Resource Interactions Over Chapters for the STAT0004 Module. This graph illustrates the conditional dependencies between each module chapter's resources (nodes). Nodes are colour--coded by type: blue for quizzes (quiz_1 to quiz_9), green for videos (vid_1 to vid_9), orange for lecture notes (ln_4 to ln_9), and pink for submissions (sub_1 to sub_8), where the suffix indicates the chapter number. Directed arrows between nodes represent probabilistic dependencies, where the engagement with one resource may influence the likelihood of engagement with another. Notably, lecture notes from chapters 1 to 3 are absent due to uniform high access rates, while other missing resources were not available.