Evaluating Engagement in Digital Narratives from Facial Data

Engagement researchers indicate that the engagement level of people in a narrative has an influence on people's subsequent story-related attitudes and beliefs, which helps psychologists understand people's social behaviours and personal experience. With the arrival of multimedia, the digital narrative combines multimedia features (e.g. varying images, music and voiceover) with traditional storytelling. Research on digital narratives has been widely used in helping students gain problem-solving and presentation skills as well as supporting child psychologists investigating children's social understanding such as family/peer relationships through completing their digital narratives. However, there is little study on the effect of multimedia features in digital narratives on the engagement level of people. This research focuses on measuring the levels of engagement of people in digital narratives and specifically on understanding the media effect of digital narratives on people's engagement levels. Measurement tools are developed and validated through analyses of facial data from different age groups (children and young adults) in watching stories with different media features of digital narratives. Data sources used in this research include a questionnaire with Smileyometer scale and the observation of each participant's facial behaviours

Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

Affective learning: improving engagement and enhancing learning with affect-aware feedback

This paper describes the design and ecologically valid evaluation of a learner model that lies at the heart of an intelligent learning environment called iTalk2Learn. A core objective of the learner model is to adapt formative feedback based on students’ affective states. Types of adaptation include what type of formative feedback should be provided and how it should be presented. Two Bayesian networks trained with data gathered in a series of Wizard-of-Oz studies are used for the adaptation process. This paper reports results from a quasi-experimental evaluation, in authentic classroom settings, which compared a version of iTalk2Learn that adapted feedback based on students’ affective states as they were talking aloud with the system (the affect condition) with one that provided feedback based only on the students’ performance (the non-affect condition). Our results suggest that affect-aware support contributes to reducing boredom and off-task behavior, and may have an effect on learning. We discuss the internal and ecological validity of the study, in light of pedagogical considerations that informed the design of the two conditions. Overall, the results of the study have implications both for the design of educational technology and for classroom approaches to teaching, because they highlight the important role that affect-aware modelling plays in the adaptive delivery of formative feedback to support learning

What does not happen: quantifying embodied engagement using NIMI and self-adaptors

Previous research into the quantification of embodied intellectual and emotional engagement using non-verbal movement parameters has not yielded consistent results across different studies. Our research introduces NIMI (Non-Instrumental Movement Inhibition) as an alternative parameter. We propose that the absence of certain types of possible movements can be a more holistic proxy for cognitive engagement with media (in seated persons) than searching for the presence of other movements. Rather than analyzing total movement as an indicator of engagement, our research team distinguishes between instrumental movements (i.e. physical movement serving a direct purpose in the given situation) and non-instrumental movements, and investigates them in the context of the narrative rhythm of the stimulus. We demonstrate that NIMI occurs by showing viewers’ movement levels entrained (i.e. synchronised) to the repeating narrative rhythm of a timed computer-presented quiz. Finally, we discuss the role of objective metrics of engagement in future context-aware analysis of human behaviour in audience research, interactive media and responsive system and interface design

Proximity and gaze influences facial temperature:a thermal infrared imaging study

Direct gaze and interpersonal proximity are known to lead to changes in psycho-physiology, behaviour and brain function. We know little, however, about subtler facial reactions such as rise and fall in temperature, which may be sensitive to contextual effects and functional in social interactions. Using thermal infrared imaging cameras 18 female adult participants were filmed at two interpersonal distances (intimate and social) and two gaze conditions (averted and direct). The order of variation in distance was counterbalanced: half the participants experienced a female experimenter’s gaze at the social distance first before the intimate distance (a socially ‘normal’ order) and half experienced the intimate distance first and then the social distance (an odd social order). At both distances averted gaze always preceded direct gaze. We found strong correlations in thermal changes between six areas of the face (forehead, chin, cheeks, nose, maxilliary and periorbital regions) for all experimental conditions and developed a composite measure of thermal shifts for all analyses. Interpersonal proximity led to a thermal rise, but only in the ‘normal’ social order. Direct gaze, compared to averted gaze, led to a thermal increase at both distances with a stronger effect at intimate distance, in both orders of distance variation. Participants reported direct gaze as more intrusive than averted gaze, especially at the intimate distance. These results demonstrate the powerful effects of another person’s gaze on psycho-physiological responses, even at a distance and independent of context

Real Time Detection and Analysis of Facial Features to Measure Student Engagement with Learning Objects

This paper describes a software application that records student engagement in an on-screen task. The application records in real time the on-screen activity and simultaneously estimates the emotional state and head pose of the learner. The head pose is used to detect when the screen is being viewed and the emotional state provides feedback on the form of engagement. The application works without recording images of the learner. On completing the task, the percentage of time spent viewing the screen and statistics on emotional state (neutral, happy, sad) are produced. A graph depicting the learner’s engagement and emotional state synchronised with the screen captured video is also produced. It is envisaged that the tool will find application in learning activity and learning object design

Biometric features modeling to measure students engagement.

The ability to measure students’ engagement in an educational setting may improve student retention and academic success, revealing which students are disinterested, or which segments of a lesson are causing difficulties. This ability will facilitate timely intervention in both the learning and the teaching process in a variety of classroom settings. In this dissertation, an automatic students engagement measure is proposed through investigating three main engagement components of the engagement: the behavioural engagement, the emotional engagement and the cognitive engagement. The main goal of the proposed technology is to provide the instructors with a tool that could help them estimating both the average class engagement level and the individuals engagement levels while they give the lecture in real-time. Such system could help the instructors to take actions to improve students\u27 engagement. Also, it can be used by the instructor to tailor the presentation of material in class, identify course material that engages and disengages with students, and identify students who are engaged or disengaged and at risk of failure. A biometric sensor network (BSN) is designed to capture data consist of individuals facial capture cameras, wall-mounted cameras and high performance computing machine to capture students head pose, eye gaze, body pose, body movements, and facial expressions. These low level features will be used to train a machine-learning model to estimate the behavioural and emotional engagements in either e-learning or in-class environment. A set of experiments is conducted to compare the proposed technology with the state-of-the-art frameworks in terms of performance. The proposed framework shows better accuracy in estimating both behavioral and emotional engagement. Also, it offers superior flexibility to work in any educational environment. Further, this approach allows quantitative comparison of teaching methods, such as lecture, flipped classrooms, classroom response systems, etc. such that an objective metric can be used for teaching evaluation with immediate closed-loop feedback to the instructor