Student Performance Prediction Based on a Framework of Teacher’s Features




Teaching skills, students’ performance, prediction system


Teachers teaching skills are essential to motivate students’ engagement in online educational environments, where students and teachers interact with each other, generating a large amount of educational data. However, to the best of our knowledge, there is no previous study that takes advantage of the huge quantity of teachers’ behavioral data to predict students’ performance. To fill this research gap, we elaborated a theoretically based framework of teacher’s characteristics, that guided an automatic data collection of teachers’ behaviors to predict students’ performance. The implementation of a computational prediction system applied the Random Forest classifying algorithm, which achieved better performance, according to AUC metric, when compared to other algorithms. Two exploratory case studies were conducted to investigate the efficiency and efficacy of the framework of teacher’s features in Goiás Judicial School EJUG teachers in Brazil. The results from the case studies shown that the framework is effective to predict students’ performance. This work contributes to distant education, enabling monitoring teachers’ actions aiming students’ academic best achievements.


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Author Biographies

Fernando Ribeiro Trindade, Universidade Federal de Goiás

Instituto de Informática

Deller James Ferreira, Universidade Federal de Goiás

Instituto de Informática


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DOI: 10.31686/ijier.vol9.iss2.2935

How to Cite

Ribeiro Trindade, F., & James Ferreira, D. (2021). Student Performance Prediction Based on a Framework of Teacher’s Features. International Journal for Innovation Education and Research, 9(2), 178–196.
Received 2021-01-06
Accepted 2021-01-23
Published 2021-02-01