Drop out rates in Massive Open Online Courses(MOOCs) are very high. Although there are many external factors, such as users not having enough time or not intending to complete the course, there are some aspects that instructors can control to optimally engage their students. To do this, they need to know how students engage throughout their video lecture. This paper explores the use of webcams to record students' facial expressions whilst they watched educational video material to analyse their Learner Engagement levels. Convolutional neural networks (CNNs) were trained to detect facial action units, which were mapped onto two psychological measurements, valence(emotional state) and arousal (attentiveness), using support vector regressions. These valence and arousal values were combined in a novel manner resulting in Learner Engagement levels. Moreover,a new approach was used to combine CNNs with geometric feature-based techniques to improve the performance of the models. Two experiments were conducted and found that 9 out of 10 CNN models achieved 95% accuracy on average across the majority of the subjects, whilst the Learner Engagement detector was able to identify facial expressions that translated to Learner Engagement levels successfully. These results suggest that there is promise in this approach, in that feedback on students' Learner Engagement can be provided back to the instructor. Additional research should be undertaken to further prove these results and overcome some limitations that were faced.