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

Abstract

IntroductionThere is a global effort to address the school dropout phenomenon. The urgency to act on it comes from the harmful evidence that school dropout has on societal and individual levels. Early Warning Systems (EWS) for school dropout at-risk student identification have been developed to anticipate and help schools have a better chance of acting on it. However, several studies point to a doubt that Correct EWS may come too late because they use only publicly available and general student and school information. We hypothesize that having a tool to assess more subjective and inter-relational factors would help anticipate where and when to act to prevent school dropout. This study aimed to develop a multidimensional measure for assessing relational factors for predicting school dropout (SD) risk in the Brazilian context.MethodsWe performed several procedures, including (a) the specialized literature review, (b) the item development of the Relational Factors for the Risk of School Dropout Scale (IAFREE in Portuguese), (c) the content validity analysis, (d) a pilot study, and (e) the administration of the IAFREE to a large Brazilian sample of high school and middle school students (N = 15,924).ResultsAfter the theoretical steps, we found content validity for five relational dimensions for SD (Student-School, Student-School Professionals, Student-Family, Student-Community, and Student–Student) that include 12 facets of risk factors. At the empirical stage, confirmatory analysis corroborated the proposed theoretical model with 12 first-order risk factors and 5 s-order dimensions (36 items). Further, through the Item Response Theory analysis, we assessed the individual item parameters of the items, providing a brief measure without losing psychometric quality (IAFREE-12).DiscussionWe discuss how this model may fill gaps in Correct EWS models and how to advance it. The IAFREE is a good measure for scholars investigating the risk of SD. These results are important for implementing an early warning system for SD that looks into the complexity of the school dropout phenomenon.

Development of an Early Warning System to Support Educational Planning Process by Identifying At-Risk Students

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Machine Learning for Early Detection of Dropout Risks and Academic Excellence: A Stacked Classifier Approach

“…For high-achieving students, early identification can lead to opportunities for advanced coursework and enrichment programs. For students at risk of dropping out, early intervention can address underlying factors, such as academic struggles or personal challenges, and provide necessary support to keep them on track [16]. Educators can create a supportive learning environment that promotes student success by focusing on early identification and intervention.…”