E-learning offers many advantages like being economical, flexible and customizable, but also has challenging aspects such as lack of – social-interaction, which results in contemplation and sense of remoteness. To overcome these and sustain learners’ motivation, various stimuli can be incorporated. Nevertheless, such adjustments initially require an assessment of engagement level. In this respect, we propose estimating engagement level from facial landmarks exploiting the facts that (i) perceptual decoupling is promoted by blinking during mentally demanding tasks; (ii) eye strain increases blinking rate, which also scales with task disengagement; (iii) eye aspect ratio is in close connection with attentional state and (iv) users’ head position is correlated with their level of involvement. Building empirical models of these actions, we devise a probabilistic estimation framework. Our results indicate that high and low levels of engagement are identified with considerable accuracy, whereas medium levels are inherently more challenging, which is also confirmed by inter-rater agreement of expert coders.

电子学习具有许多优点，例如经济，灵活和可定制，但也具有挑战性的方面，例如缺乏社交互动，这导致沉思和遥远感。为了克服这些问题并维持学习者的动力，可以采用各种刺激措施。但是，此类调整最初需要评估参与度。在这方面，我们建议利用以下事实来估计面部标志物的参与度：（i）在精神上要求很高的任务期间眨眼可促进知觉去耦；（ii）眼睛疲劳会增加眨眼频率，这也会随着任务脱离而扩大；（iii）眼睛纵横比与注意力状态密切相关；（iv）用户的头部位置与其参与程度相关。建立这些行动的经验模型，我们设计了一个概率估计框架。我们的结果表明，参与度的高和低被确定为相当准确，而中等水平固有地更具挑战性，这也得到专家编码者之间的评分者同意。