Abstract
Recently, artificial intelligence (AI) domain increased to contain finance, education, health, mining, and education. Artificial intelligence controls the performance of systems that use new technologies, especially in the education environment. The multiagent system (MAS) is considered an intelligent system to facilitate the e-learning process in the educational environment. MAS is used to make interaction easily among agents, which supports the use of feature selection. The feature selection methods are used to select the important and relevant features from the database that could help machine learning algorithms produce high performance. This paper aims to propose an effective and suitable system for multiagent-based machine learning algorithms and feature selection methods to enhance the e-learning process in the educational environment which predicts pass or fail results. The univariate and Extra Trees feature selection methods are used to select the essential attributes from the database. Five machine learning algorithms named Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and K-nearest neighbors algorithm (KNN) are applied to all features and selected features. The results showed that the learning algorithm that has been measured by the Extra Trees method has achieved the highest performance depending on the evaluation of cross-validation and testing.
Highlights
During the last two years, global disasters have occurred, so all people are forced to use technologies to get their services remotely [1]
Machine learning (ML) algorithms are optimized using grid search with cross-validation. e dataset was partitioned into two parts: an 80% training set for optimizing models and registering crossvalidation results and a 20% testing dataset for evaluating models and registering testing results
We conducted various experiments to study the effect of learning and activity types in the educational process using feature selection methods based on five ML algorithms: Decision Tree (DT), K-nearest neighbors algorithm (KNN), Naive Bayes (NB), Logistic Regression (LR), and Random Forest (RF)
Summary
During the last two years, global disasters have occurred, so all people are forced to use technologies to get their services remotely [1]. In the e-learning system, students could use many features to enhance their performance [9]. Using artificial intelligence systems is the way to enhance the performance of students by using feature selection methods [10, 11]. Machine learning algorithms play an essential role in the educational process and feature selection algorithms. Is paper proposed an education system using multiagents to study interactive agents’ effects to enhance e-learning. We integrated different agents: course, student, and different activities, and we applied different feature selection methods to select the most attributes that are playing an important role in enhancing the e-learning process. We applied five machine learning algorithms on selected features and evaluated ML algorithms’ performance using different measurement methods to enhance the effect of the feature selection methods on the performance of the educational process.
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