Engagement　is　an　essential　indicator　of　the　Quality-of-Learning　Experience　(QoLE)　and　plays　a　major　role　in　developing　intelligent　educational　interfaces.　The　number　of　people　learning　through　Massively　Open　Online　Courses　(MOOCs)　and　other　online　resources　has　been　increasing　rapidly　because　they　provide　us　with　the　flexibility　to　learn　from　anywhere　at　any　time.　This　provides　a　good　learning　experience　for　the　students.　However,　such　learning　interface　requires　the　ability　to　recognize　the　level　of　engagement　of　the　students　for　a　holistic　learning　experience.　This　is　useful　for　both　students　and　educators　alike.　However,　understanding　engagement　is　a　challenging　task,　because　of　its　subjectivity　and　ability　to　collect　data.　In　this　paper,　we　propose　a　variety　of　models　that　have　been　trained　on　an　open-source　dataset　of　video　screengrabs.　Our　non-deep　learning　models　are　based　on　the　combination　of　popular　algorithms　such　as　Histogram　of　Oriented　Gradient　(HOG),　Support　Vector　Machine　(SVM),　Scale　Invariant　Feature　Transform　(SIFT)　and　Speeded　Up　Robust　Features　(SURF).　The　deep　learning　methods　include　Densely　Connected　Convolutional　Networks　(DenseNet-121),　Residual　Network　(ResNet-18)　and　MobileNetV1.　We　show　the　performance　of　each　models　using　a　variety　of　metrics　such　as　the　Gini　Index,　Adjusted　F-Measure　(AGF),　and　Area　Under　receiver　operating　characteristic　Curve　(AUC).　We　use　various　dimensionality　reduction　techniques　such　as　Principal　Component　Analysis　(PCA)　and　t-Distributed　Stochastic　Neighbor　Embedding　(t-SNE)　to　understand　the　distribution　of　data　in　the　feature　sub-space.　Our　work　will　thereby　assist　the　educators　and　students　in　obtaining　a　fruitful　and　efficient　online　learning　experience.　©　2022　The　Author(s)