A Neural Network Based Model for Predicting Educational Vulnerability of Undergraduate Students

Document Type : Scientific - Research

Authors

1 Professor, Department of Educational Management, Semnan University

2 Professor at Iran University of Science and Technology

3 PhD student in Educational Management, Semnan University

Abstract

The aim of this study as a part of a doctoral thesis was to develop a model for predicting educational vulnerability of undergraduate students in engineering disciplines in short term period (by semester).  The method was data mining by using neural network algorithm. The statistical population, including all "Term- student" from the first semester in academic year  1390-91 till the second semester of 1393-94 in three Iranian technical-engineering universities (with a total of 53,422 records). The needed data were used in the model by direct exploitation of MISs in all three universities. The results indicate that by using the available data in educational systems of universities and engaging the neural networks algorithm, it is possible to make a prediction by more than 95 percent accuracy and with validity over 60, in terms of semester results for all students. “GPA (Grade Point Average) of the last semester”, “total GPA”, “the condition of the semesters in the case of being an odd or an even one”, “type of units taken within the semester” and “engaging in extra activities”, were identified as the most effective predictive variables.

Keywords

References

 
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