University of Malta. Centre for Resillience & Socio-Emotional Health
Abstract
In times of growing importance and emphasis on improving academic outcomes for
young people, their academic selves/lives are increasingly becoming more central to
their understanding of their own wellbeing. How they experience and perceive their
academic successes or failures, can influence their perceived self-efficacy and eventual
academic achievement. To this end, ‘cognitive emotions’, elicited to acquire or develop
new skills/knowledges, can play a crucial role as they indicate the state or the “flow” of
a student’s emotions, when facing challenging tasks. Within innovative teaching models,
measuring the affective components of learning have been mainly based on self-reports
and scales which have neglected the real-time detection of emotions, through for
example, recording or measuring facial expressions. The aim of the present study is to
test the reliability of an ad hoc software trained to detect and classify cognitive emotions
from facial expressions across two different environments, namely a video-lecture and a
chat with teacher, and to explore cognitive emotions in relation to academic e-selfefficacy
and academic adjustment. To pursue these goals, we used video-recordings of
ten psychology students from an online university engaging in online learning tasks, and
employed software to automatically detect eleven cognitive emotions. Preliminary
results support and extend prior studies, illustrating how exploring cognitive emotions in
real time can inform the development and success of academic e-learning interventions
aimed at monitoring and promoting students’ wellbeing.peer-reviewe