Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students

Area of Science:

• Educational Psychology
• Sociology of Education
• Psychometrics

Background:

• School dropout poses significant societal and individual risks globally.
• Existing Early Warning Systems (EWS) often rely on late-arriving, general data.
• There's a need for tools assessing subjective, inter-relational factors for proactive dropout prevention.

Purpose of the Study:

• To develop a multidimensional measure for assessing relational factors predicting school dropout (SD) risk.
• To create a tool for the Brazilian context to enhance early identification of at-risk students.
• To bridge the gap in current EWS by incorporating nuanced relational dynamics.

Main Methods:

• Literature review and item development for the Relational Factors for the Risk of School Dropout Scale (IAFREE).
• Content validity analysis, pilot study, and administration to a large Brazilian sample (N=15,924).
• Confirmatory and Item Response Theory analyses to validate the scale's psychometric properties.

Main Results:

• Validated five relational dimensions (Student-School, Student-School Professionals, Student-Family, Student-Community, Student-Student) with 12 risk factor facets.
• Confirmatory analysis supported a 12-factor, 5-dimension model (36 items).
• Item Response Theory analysis yielded a brief, psychometrically sound measure (IAFREE-12).

Conclusions:

• The IAFREE scale effectively measures relational factors crucial for predicting school dropout risk.
• This multidimensional approach can significantly improve the accuracy and timeliness of EWS.
• The findings support the implementation of more complex, nuanced early warning systems for school dropout prevention.