Prediction and Localization of Student Engagement in the Wild

Abstract

In this paper, we introduce a new dataset for student engagement detection and localization. Digital revolution has transformed the traditional teaching procedure and a result analysis of the student engagement in an e-learning environment would facilitate effective task accomplishment and learning. Well known social cues of engagement/disengagement can be inferred from facial expressions, body movements and gaze pattern. In this paper, student's response to various stimuli videos are recorded and important cues are extracted to estimate variations in engagement level. In this paper, we study the association of a subject's behavioral cues with his/her engagement level, as annotated by labelers. We then localize engaging/non-engaging parts in the stimuli videos using a deep multiple instance learning based framework, which can give useful insight into designing Massive Open Online Courses (MOOCs) video material. Recognizing the lack of any publicly available dataset in the domain of user engagement, a new 'in the wild' dataset is created to study the subject engagement problem. The dataset contains 195 videos captured from 78 subjects which is about 16.5 hours of recording. We present detailed baseline results using different classifiers ranging from traditional machine learning to deep learning based approaches. The subject independent analysis is performed so that it can be generalized to new users. The problem of engagement prediction is modeled as a weakly supervised learning problem. The dataset is manually annotated by different labelers for four levels of engagement independently and the correlation studies between annotated and predicted labels of videos by different classifiers is reported. This dataset creation is an effort to facilitate research in various e-learning environments such as intelligent tutoring systems, MOOCs, and others.

The Human Touch: Using a Webcam to Autonomously Monitor Compliance During Visual Field Assessments

“…We examined 42 eyes from 42 adults with correctedto-normal vision (median [interquartile range, IQR] age: 26 [22][23][24][25][26][27][28][29] years), and 14 eyes from seven adults with an established diagnosis of glaucoma (69 [64][65][66][67][68][69][70][71][72][73][74] years of age).…”

Attention Network for Engagement Prediction in the Wild

“…Our database collection details are discussed in the Kaur et al [9]. Student participants were asked to watch five minutes long MOOC video.…”

“…Request permissions from permissions@acm.org. ICMI '18, October 16-20, 2018 Figure 1: The images of the videos in the student engagement recognition sub-challenge [9]. Please note the varied backgrounds environment and illumination.…”

Unsupervised Learning of Eye Gaze Representation from the Web

“…It provides information about human visual attention and cognitive process [1]. It aids several applications such as human-computer interaction [2], student engagement detection [3], video games with basic human interaction [4], driver attention modelling [5], psychology research [6] etc.…”