Predictive modelling of academic success and retention has been a key research theme in Learning Analytics. While the initial work on predictive modelling was focused on the development of general predictive models, portable across different learning settings, later studies demonstrated the drawbacks of not considering the specificities of course design and disciplinary context. This study builds on the methods and findings of related earlier studies to further explore factors predictive of learners' academic success in blended learning. In doing so, it differentiates itself by (i) relying on a larger and homogeneous course sample (15 courses, 50 course offerings in total), and (ii) considering both internal and external conditions as factors affecting the learning process. We apply mixed effect linear regression models, to examine: i) to what extent indicators of students' online learning behaviour can explain the variability in the final grades, and ii) to what extent that variability is attributable to the course and students' internal conditions, not captured by the logged data. Having examined different types of behaviour indicators (e.g., indicators of the overall activity level, those indicative of regularity of study, etc), we found little difference, if any, in their predictive power. Our results further indicate that a low proportion of variance is explained by the behaviour-based indicators, while a significant portion of variability stems from the learners' internal conditions. Hence, when variability in external conditions is largely controlled for (the same institution, discipline, and nominal pedagogical model), students’ internal state is the key predictor of their course performance.

学业成功和保留的预测建模一直是学习分析的一个关键研究主题。虽然预测建模的最初工作侧重于通用预测模型的开发，可在不同的学习环境中移植，但后来的研究证明了不考虑课程设计和学科背景的特殊性的缺点。本研究以相关早期研究的方法和结果为基础，进一步探索预测学习者在混合学习中学业成功的因素。在这样做时，它的与众不同之处在于 (i) 依赖于更大和同质的课程样本（15 门课程，总共 50 门课程），以及 (ii) 将内部和外部条件视为影响学习过程的因素。我们应用混合效应线性回归模型来检查：i) 学生在线学习行为的指标在多大程度上可以解释最终成绩的可变性，以及 ii) 可变性在多大程度上归因于课程和学生的内部条件，而不是被记录的数据捕获。在检查了不同类型的行为指标（例如，总体活动水平指标、指示学习规律的指标等）后，我们发现它们的预测能力几乎没有差异（如果有的话）。我们的结果进一步表明，基于行为的指标可以解释低比例的变异，而很大一部分变异源于学习者的内部条件。因此，当外部条件的可变性在很大程度上受到控制时（相同的机构、学科和名义教学模式），