Mining Educational Data to Analyze the Student’s Performance in TOEFL iBT Reading, Listening and Writing Scores

Abstract

Student scores in TOEFL IBT reading, listening, and writing may reveal weaknesses and deficiencies in educational institutions. Traditional approaches and evaluations are unable to disclose the significant information hidden inside the student's TOEFL score. As a result, data mining approaches are widely used in a wide range of fields, particularly education, where it is recognized as Educational Data Mining (EDM). Educational data mining is a prototype for handling research issues in student data which can be used to investigate previously undetected relationships in a huge database of students. This study used the EDM to define the numerous factors that influence students' achievement and to create observations using advanced algorithms. The present study explored the relationship among university students’ previous academic experience, gender, student place and their current course attendance within a sample of 473 (225 male and 248 female). Educational specialists must find out the causes of student dropout in TOEFL scores. The results of the study showed that the model could be suitable for investigation of important aspects of student outcomes, the present research was supposed to use the statistical package for social sciences (SPSS V26) for both descriptive and inferential statistics and multiple linear regressions to improve their scores.