Palomar project: predicting school renouncing dropouts, using the artificial neural networks as a support for educational policy decisions.
The "Palomar" project confronts two problem situations that are partly independent and partly connected to the Italian schooling system: unstable participation in school such as drop out and educational guidance. Our concern is that of a set of phenomena which consists of ceasing compulsory education, repetition of a year at school, school "drop outs", irregular compulsory attendance and delays in the course of studies. The "Palomar" project is designed to offer educators and administrators who want to effectively intervene with these complex problems to furnish school guidance services as an instrument able to: 1. Predict: creating a system able to predict in advance (not in a "cause-effect" way but as an approximation): a) which students are at "risk" for school destabilization or failure; b) what are the prototypical characteristics of these students; c) which students among those studied are more likely to "destabilize" or fail in school; in which course of study does each student have the greatest chance of success; d) which, among the variables studied and appropriately weighted for each student, will predict the successful grade, analyzed for each possible course of studies. 2. Optimize: selecting and focusing on a student on the basis of the information given. It is possible: a) to point out which personal factors (relational, familial, student, disciplinary, economical) need to be reinforced in order to improve the school performances of each selected student, both to prevent or limit "dropping out" desertion or failure and to raise the performances in the chosen school course as much as possible; b) on the basis of what was mentioned above, to simulate the possible support measures to increase the efficacy of the considered intervention; c) to choose for each student the appropriate intervention strategy capable of obtaining the maximum result and the maximum efficacy in the given conditions. 3. Verify: when the strategy of intervention has been decided and we proceed with its implementation, it is possible to periodically verify ("follow-up"), through subsequent administration of the form, the outcome variations elapsed in the prediction of school success or failure. This makes it possible to verify in itinere the efficacy of the interventions carried out and, if necessary, to create variations and adjustments. 4. Produce scenarios: the application field of the Prediction System with Artificial Neural Networks can also be one of a group of students, of one or more organized units (for example a class, a school, or a group of schools). In this case the Prediction System ANN using the program SCHEMA (Buscema, 1996b) is able to: a) determine intervention strategies in order to optimize and to produce the maximum results of a group of students as the one of a class; b) optimize the formative route of a whole institute in order to prevent or limit the need for school guidance.
Carbone
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Municipal Administration of San Giovanni Valdarno, Arezzo, Italy.
Piras
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D000293:Adolescent; D001185:Artificial Intelligence; D003657:Decision Making; D004493:Education; D005544:Forecasting; D006801:Humans; D016571:Neural Networks, Computer; D011050:Policy Making; D013332:Student Dropouts