Connecting Educators with Pupils' Emotional States to Enhance Learning Success
New Emotional Analysis Framework Aims to Boost Student Engagement and Performance in Higher Education
A groundbreaking framework designed to enhance student engagement and performance in Higher Education Institutions (HEIs) has been unveiled. This innovative approach, based on real-time computer vision emotion detection, offers a new way to assess and improve learning experiences.
The core of the framework involves continuous, objective, and empirically grounded emotional assessment during actual learning activities. It uses facial expression data captured from students to assess their emotional states and engagement levels in real time.
Key components of the framework include data collection, emotion classification, analysis, and application. Data collection employs computer vision technology, such as the DeepFace library, to process live facial expression data from students attending video-based or in-person classes. Emotions are classified primarily following Ekman’s theory of basic emotions, with a neutral state to account for the absence of detectable emotion.
The framework links specific pedagogical events or instructional moments to students’ affective responses, identifying engagement or emotional support needs. It provides educators with immediate insight into teaching effectiveness and student engagement, enabling adaptive instructional design that fosters emotionally supportive and pedagogically sound learning environments.
The framework compiles the gathered data in an ontology and uses machine learning to extract patterns and outliers. Data collection methods include human observations, electronic sensors, and automatic analysis software, such as eye tracking devices and emotion detection through automatic facial expression recognition software.
The problem of student drop-out and academic disengagement is prevalent in HEIs, and factors leading to this issue include physical or psychological disabilities, social-economic or academic marginalization, and emotional and affective problems. A shortage of educational resources is worsening this problem.
This new framework aims to enable a faster, personalized response to help students, leading to improvements in academic performance. It can profile students in critical situations such as disengagement, attention deficit, drop-out, and other sociological issues, offering valuable insights into real-time cognitive data.
Although the framework does not specify the particular HEIs it is designed for, it is designed for flexible application in varied educational settings, including conventional classrooms and non-computerized environments with suitable technical adjustments. The goal of the framework is to lead to improvements in academic performance by tailoring educational experiences to students’ emotional states as they learn.
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- Modern science in the field of health-and-wellness and mental-health, merged with technology, is being utilized to develop an emotional analysis framework aimed at enhancing student engagement and performance in Higher Education Institutions (HEIs).
- This framework, furthering education-and-self-development, uses cutting-edge technology like eye tracking devices and artificial intelligence algorithms to assess students' emotions during actual learning activities.
- By linking pedagogical events with students' affective responses, the framework aims to foster emotionally supportive and pedagogically sound learning environments, thereby reducing instances of student drop-out and academic disengagement.
