There has been rise in online learning because of its flexibility and need for lifelong learning. Understanding and improving students’ engagement during online learning is pivotal as it can provide educators the feedback to improve delivery of content. However, recognising students’ emotions using visual data raises ethical issues of individual privacy. In this paper, we build on the existing research in the field of emotion detection in virtual learning environments by making use of facial keypoint images, also known as face meshes, which helps to overcome the challenge of directly working on the visual data. We make use of publicly available emotional dataset, namely, RAF-DB, and demonstrated improved classification accuracy using sophisticated facial keypoints. We finally predicted student engagement using “engagement to index” algorithm. This work not only advances the field of educational technology by improving emotion classification accuracy, but also addresses crucial ethical issues, including student permission and data privacy.