Fuzzy Logic and Machine Learning-Enabled Recommendation System to Predict Suitable Academic Program for Students

Abstract

In recent years, educational data mining has gained a considerable lot of interest as a consequence of the large number of pedagogical content that can be gathered from a range of sources. This is because there is a lot of instructional information that can be obtained. The data mining tools collaborate with academics to improve students’ learning strategies by analyzing, sifting through, and estimating components that are pertinent to students’ characteristics or patterns of behavior. This is accomplished through the following steps: EDM is utilized in the vast majority of instances to develop the classification model, which then assigns a certain class to each student based on the known properties of the training dataset. Before putting the classification model into use, it is possible to utilize a test dataset to verify that the model is accurate. This article provides a description of a recommendation system that determines the most beneficial academic program for students by utilizing fuzzy logic and machine learning. The compilation of a student dataset has begun. It includes a total of 21 features and 1000 individual cases. The initial step is to employ the CFS attribute selection method. This methodology selects 15 of the initial set of 21 characteristics. Following the completion of the data gathering, it is put through various machine learning methods such fuzzy SVM, random forest, and C4.5. This methodology that has been offered makes predictions about the academic program that is best suitable for students.

Visual Data and Pattern Analysis for Smart Education: A Robust Drl-Based Early Warning System for Student Performance Prediction

“…This section compares the proposed method with several existing ones, including PCT [43], MST-FaDe [37], Fuzzy SVM [29], and Artificial Intelligence-Based Student Assessment and Recommendation (AISAR) [38]. It evaluates its effectiveness using performance metrics such as accuracy, precision, recall, F1-score, true positive rate, false positive rate, true negative rate, and false negative rate.…”

Visual Data and Pattern Analysis for Smart Education: A Robust DRL-Based Early Warning System for Student Performance Prediction

“…Similarly, the combination of machine learning techniques with fuzzy methodologies in [29] presents an advanced approach for designing performance prediction systems. By integrating fuzzy logic, this model aims to refine the accuracy of predictions and recommendations, addressing the complexity of student data and learning paths.…”

“…Handling massive datasets with machine learning algorithms introduces complexity [29] Performance prediction using machine learning and fuzzy methodology…”

College Course Recommender System based on Sentiment Analysis

“…Fuzzy Logic is instrumental when dealing with complex and irregular student information. A recommendation system based on fuzzy Logic and machine learning is described in [19] for determining the most beneficial academic program for students. The system uses a student dataset with 21 features and 1000 individual cases and employs attribute selection methods and machine learning techniques such as fuzzy SVM, random forest, and C4.5 to predict the most suitable academic program for students.…”