A Prognosis of Junior High School Students’ Performance Based on Active Learning Methods

Georgios Kostopoulos, Sotiris Kotsiantis, Vassilios S. Verykios

In recent years, there is a growing research interest in applying data mining techniques in education. Educational Data Mining has become an efficient tool for teachers and educational institutions trying to effectively analyze the academic behavior of students and predict their progress and performance. The main objective of this study is to classify junior high school students’ performance in the final examinations of the “Geography” module in a set of five pre-defined classes using active learning. The exploitation of a small set of labeled examples together with a large set of unlabeled ones to build efficient classifiers is the key point of the active learning framework. To the best of our knowledge, no study exist dealing with the implementation of active learning methods for predicting students’ performance. Several assessment attributes related to students’ grades in homework assignments, oral assessment, short tests and semester exams constitute the dataset, while a number of experiments are carried out demonstrating the advantage of active learning compared to familiar supervised methods, such as the Naïve Bayes classifier.

The final publication is available at Springer via https://doi.org/10.1007/978-3-319-67615-9_6.