Understanding and enhancing student engagement through digital platforms is critical in higher education. This study introduces a methodology for quantifying engagement across an entire module using virtual learning environment (VLE) activity log data. Using study session frequency, immediacy, and diversity, we create a cumulative engagement metric and model it against weekly VLE interactions with resources to identify critical periods and resources predictive of student engagement. In a case study of a computing module at University College London's Department of Statistical Science, we further examine how delivery methods (online, hybrid, in-person) impact student behaviour. Across nine regression models, we validate the consistency of the random forest model and highlight the interpretive strengths of generalised additive models for analysing engagement patterns. Results show weekly VLE clicks as reliable engagement predictors, with early weeks and the first assessment period being key. However, the impact of delivery methods on engagement is inconclusive due to inconsistencies across models. These findings support early intervention strategies to assist students at risk of disengagement. This work contributes to learning analytics research by proposing a refined VLE-based engagement metric and advancing data-driven teaching strategies in higher education.

翻译：通过数字平台理解并提升学生参与度对高等教育至关重要。本研究提出一种方法，利用虚拟学习环境（VLE）活动日志数据量化整个课程模块的参与度。通过分析学习会话频率、即时性和多样性，我们构建了累积参与度指标，并将其与每周VLE资源交互数据进行建模，以识别预测学生参与度的关键时段和资源。在伦敦大学学院统计科学系某计算模块的案例研究中，我们进一步探讨了授课模式（在线、混合、线下）对学生行为的影响。通过对九种回归模型的比较，我们验证了随机森林模型的一致性，并强调了广义可加模型在分析参与模式方面的解释优势。结果表明，每周VLE点击量是可靠的参与度预测指标，其中课程初期和首次评估阶段尤为关键。然而，由于不同模型间存在不一致性，授课模式对参与度的影响尚无定论。这些发现为针对存在脱离风险学生的早期干预策略提供了依据。本研究通过提出改进的基于VLE的参与度度量指标，推动了高等教育领域数据驱动教学策略的发展，为学习分析研究作出了贡献。