The IESA Program, an Emotional Intelligence-Based Intervention, in First-Year Higher Education Students: A Study of Student Outcomes
The IESA Program, an Emotional Intelligence-Based Intervention, in First-Year Higher Education Students: A Study of Student Outcomes
The IESA Program is an Emotional Intelligence (EI) intervention designed for first-year higher education students. The program is based on an ability-based EI framework and focuses on the development of four core emotional abilities: identifying, understanding, using, and managing emotions.
The intervention aims to support students as they transition into higher education and face personal, social, and academic challenges. The IESA Program is delivered in a group format and consists of ten weekly sessions.
The IESA Program is a structured group intervention designed for first-year higher education students. The program operationalizes the ability-based model of emotional intelligence (Mayer & Salovey, 1997) and is delivered through ten weekly, two-hour sessions. Each session includes structured group activities and guided reflection to develop key emotional abilities.
Primary Outcomes:
The primary goal of the program is to foster students' emotional skills, specifically: Perception of emotions, comprehension of emotions, facilitation of emotions, and management of emotions.
Secondary Outcomes:
The program also targets additional outcomes, including: Adaptation to higher education, encompassing social, emotional, study, career, and institutional aspects; Mental health improvement, assessed by reductions in stress, depression, and anxiety; Academic performance, measured by Grade Point Average (GPA) The IESA Program is intended to support students in navigating the personal, social, and academic challenges associated with the transition to higher education.
Inclusion Criteria:
Exclusion Criteria:
Additional Notes:
Participants who meet exclusion criteria will still be allowed to attend the IESA Program sessions for ethical reasons. However, their data will be systematically excluded from statistical analyses to ensure data integrity.