We explore whether interactive navigational behaviours can be used as a reliable and effective source to measure the progress, achievement, and engagement of a learning process. To do this, we propose a data-driven methodology involving sequential pattern mining and thematic analysis of the low-level navigational interactions. We applied the method on an online learning platform which involved 193 students resulting in six interactive behaviours that are significantly associated with learner achievement including exploration of the first week’s materials and exploration of the forum. The value of including these behaviours in predictive models increased their explainability by 10% and accounted for an overall explainability of 82%. Performance evaluations of the models indicate 91–95% accuracy in identifying low-achieving students. Other relevant findings indicate a strong association between the reduction of the behaviours over time and student achievement. This suggests a relationship between student interface learnability and achievement: achievers become more efficient at using the functionalities of an online learning platform. These findings can provide context to learning progress and theoretical foundations of interventions against unhelpful learning behaviours.

我们探讨了交互式导航行为是否可以用作衡量学习过程的进度，成就和参与度的可靠且有效的来源。为此，我们提出了一种数据驱动的方法，包括顺序模式挖掘和低级导航交互的主题分析。我们在网上学习平台上应用了该方法，该平台吸引了193名学生，导致六种互动行为与学习者的成就显着相关，包括探索第一周的资料和探索论坛。将这些行为包含在预测模型中的价值使它们的可解释性提高了10％，占总体可解释性的82％。对模型的性能评估表明，在识别成绩欠佳的学生方面，准确性为91–95％。其他相关发现表明，随着时间的流逝行为的减少与学生的成就之间有着密切的联系。这表明了学生界面学习能力与成就之间的关系：成就者在使用在线学习平台的功能方面变得更加高效。这些发现可以为学习进展和针对无益学习行为的干预措施提供理论基础。