Özet:

 The purpose of this study is to investigate how educational ecologies, which have become more diverse as a result of recent global social mobility, might lead to pedagogical marginalization and cultural conflicts like suicide. The goal of the study is to put out a preventative approach that will lessen the suicide rate and other unintended consequences of pedagogical marginalization. To do this, machine learning techniques were used to test a model to predict students' suicidal tendencies, which is one of the potential indications of multicultural educational ecologies. The scanning technique was used to conduct the study, which was created within the parameters of the quantitative paradigm. 50 high school and 268 university students, of whom 25% were domestic, made up the research sample. Data collected from the sample via the questionnaire was used to construct a dataset for automated analysis. Each record in the dataset has a total of 26 numerical variables and 25 category variables. The prediction model was created by the analysis of the data using the machine learning techniques Decision Trees (DT), Naive Bayes (NB), Support Vector Machines (SVM), k-Nearest Neighbor (k-NN), and Artificial Neural Networks (ANN). The 10-fold cross-validation approach was utilized to conduct experiments, and several performance assessment measures were applied. Testing the mentioned model produced the following outcomes: In multicultural educational environments, the proposed approach was able to predict students' suicidal tendencies with great accuracy. According to research, social isolation, depression, anxiety, and stress are the main risk factors for student suicide. As a consequence, it has been determined that the research model may significantly advance education's core goal of "preparing the individual for a quality life." On the other hand, this model, which is an actual consequence of the interaction between technology and education, can offer significant benefits for psychological counseling and preventive guidance services to students at risk by foreseeing the triggers of suicidal tendencies, which can be a negative outcome of multicultural educational ecologies. As a result, the school, teachers, and parents have the chance to adopt the required safety measures, which can make the school a more livable ecosystem. The digital pedagogy of the twenty-first century also demands that machine learning and educational issues be combined. Since digital pedagogy implies not just the use of technology in education and training but also the entire technologicalization of pedagogy.

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