چکیده مقاله
Though motivation as an educational technique has long been introduced to pedagogy in general and language teaching in particular, it seems that scant heed has been given to its importance as well as the challenges it has In an attempt to shed more light on the status of ELT enhancement with motivational factors, the researchers as university instructors attempted to predict the “passing or Failing” of the students at the beginning of the semester by using Machine Learning ML which is newly entered education In so doing, the researchers arranged with a total of 99 students from General English course to participate in the survey from both genders The required data were collected via standard questionnaire on motivational factors that was in a 5 point Likert type scale For analyzing data, Linear Regression or Logistic Regression are often used according to the dependent variables But, in the present study analyzing data has been done through Rapid Minder software using Random Forest RF which is the innovative technique for predicting students' performance Thus, RF is utilized to compare with logistic regression LR The accuracy of 73 33% for LR and 96 66% for RF indicated a much higher accuracy in RF
کلیدواژهها
نویسندگان
Mohammad Reza Moradi
Assistant Professor, Payame Noor University, Tehran, Iran
Reza Ghasemi Najafabadi
Assistant Professor, Payame Noor University, Tehran, Iran
شیوه ارجاع
Moradi, Mohammad Reza and Ghasemi Najafabadi, Reza,1400,Predicting Students’ “Passing or Failing” Status with the Utilization of Motivational Factors by Machine Learning Methods,Fourth International Conference on Soft Computing
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