EMT: Ensemble Meta-Based Tree Model for Predicting Student Performance

Abstract

In recent decades, predicting the performance of students in the academic field has revealed the attention by researchers for enhancing the weaknesses and provides support for future students. In order to facilitate the task, educational data mining (EDM) techniques are utilized for constructing prediction models built from student academic historical records. These models present the embedded knowledge that is more readable and interpretable by humans. Hence, in this paper, the contributions are presented in three folds that include the following: (i) providing a thorough analysis about the selected features and their effects on the performance value using statistical analysis techniques, (ii) building and studying the performance of several classifiers from different families of machine learning (ML) techniques, (iii) proposing an ensemble meta-based tree model (EMT) classifier technique for predicting the student performance. The experimental results show that the EMT as the ensemble technique gained a high accuracy performance reaching 98.5% (or 0.985). In addition, the proposed EMT technique obtains a high performance, which is a superior result compared to the other techniques.

Integrating Learning Analytics and Collaborative Learning for Improving Student’s Academic Performance

“…The study [25] observes the factors that affect the student's performance using statistical analysis techniques. The study uses a dataset of 400 students collected over a period of two semesters with the proposed ensemble meta-based tree model (EMT).…”

“…The primary data are collected using a questionnaire that includes questions related to several personal, socio-economic, psychological, and academicrelated variables. The questionnaire is improvised from several articles related to problem in hand [1], [25], [27], [40], [41]. After consultation and supervision from several faculty members, a questionnaire with 34 questions is finalized.…”

Teaching Teacher Recommendation Method Based on Fuzzy Clustering and Latent Factor Model

“…The predicted evaluation value  , Tzf += α  (error Cfc -ω Tzf); Cfj += α (errorTzf -ωCfj); 27. α=α*0.9 ;// When the optimization reaches a certain level, the learning rate must be slowed down, and the optimal value must be gradually approached 28…”

The Sensitivity Conundrum – Random Forest or Boosting

“…Reference [10] shows how the use of ensemble methods provided better results in an elearning setup. An ensemble meta-based tree model classifier technique for predicting the student performance was used by [11]. The proposed model essentially combined two consistent machine learning techniques into a voting bagging technique to achieve higherperformance.…”