DMCNet: Diversified Model Combination Network for Understanding Engagement from Video Screengrabs

TL;DR

This paper explores multiple models, both traditional and deep learning, to assess student engagement from video screengrabs, aiming to improve online learning experiences by accurately recognizing engagement levels.

Contribution

It introduces a comprehensive comparison of non-deep and deep learning models for engagement detection using an open-source dataset, including novel insights into feature space distribution.

Findings

Deep learning models outperform traditional methods in engagement recognition.

Dimensionality reduction techniques reveal meaningful data clusters.

Models achieve high accuracy metrics, aiding online education tools.

Abstract

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.