Identifying learning style through eye tracking technology in adaptive learning systems

Learner learning style represents a key principle and core value of the adaptive learning systems (ALS). Moreover, understanding individual learner learning styles is a very good condition for having the best services of resource adaptation. However, the majority of the ALS, which consider learning styles, use questionnaires in order to detect it, whereas this method has a various disadvantages, For example, it is unsuitable for some kinds of respondents, time-consuming to complete, it may be misunderstood by respondent, etc. In the present paper, we propose an approach for automatically detecting learning styles in ALS based on eye tracking technology, because it represents one of the most informative characteristics of gaze behavior. The experimental results showed a high relationship among the Felder-Silverman Learning Style and the eye movements recorded whilst learning

Cooperative Learning Groups: A New Approach Based on Students’ Performance Prediction

Cooperative learning is a pedagogical approach in which students collaborate in small groups to attain a shared academic objective. In the classroom, cooperative learning aims to enhance learning outcomes by promoting the exchange of information, social, and personal resources among students. Group formation is a critical and complex step that significantly impacts the effectiveness of cooperative learning. In this article, we propose a novel approach for constructing cooperative learning groups that employs machine learning to predict student performance and incorporates the most common grouping strategies to recommend optimal group formation

Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance

A Recommender System for Predicting Students' Admission to a Graduate Program using Machine Learning Algorithms

In the 21st century, University educations are becoming a key pillar of social and economic life. It plays a major role not only in the educational process but also in the ensuring of two important things which are a prosperous career and financial security. However, predicting university admission can be especially difficult because the students are not aware of admission requirements. For that reason, the main purpose of this research work is to provide a recommender system for early predicting university admission based on four Machine Learning algorithms namely Linear Regression, Decision Tree, Support Vector Regression, and Random Forest Regression. The experimental results showed that the Random Forest Regression is the most suitable Machine Learning algorithm for predicting university admission. Also, the Cumulative Grade Point Average (CGPA) is the most important parameter that influences the chance of admission

A Recommender System for Predicting Students' Admission to a Graduate Program using Machine Learning Algorithms

In the 21st century, University educations are becoming a key pillar of social and economic life. It plays a major role not only in the educational process but also in the ensuring of two important things which are a prosperous career and financial security. However, predicting university admission can be especially difficult because the students are not aware of admission requirements. For that reason, the main purpose of this research work is to provide a recommender system for early predicting university admission based on four Machine Learning algorithms namely Linear Regression, Decision Tree, Support Vector Regression, and Random Forest Regression. The experimental results showed that the Random Forest Regression is the most suitable Machine Learning algorithm for predicting university admission. Also, the Cumulative Grade Point Average (CGPA) is the most important parameter that influences the chance of admission.</p

Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance

Personalized Ubiquitous Learning via an Adaptive Engine

Nowadays, the world's population is increasingly waiting for permanent and constant access to information. Accessing the right information at any time and any place is becoming a necessity. A learning system is called ubiquitous if it is able to adapt itself to its context (user, platform, environment, device, etc.). In this sense, theories and methods of adaptations keep rolling in order to make learning processes more efficient and relevant. In this paper, we propose an approach for providing personalized course content in ubiquitous learning, considering learning styles and context-awareness. The proposed approach aims to support learners by presenting course materials generated by an adaptive engine based on adaptation rules

Personalized Ubiquitous Learning via an Adaptive Engine

Nowadays, the world's population is increasingly waiting for permanent and constant access to information. Accessing the right information at any time and any place is becoming a necessity. A learning system is called ubiquitous if it is able to adapt itself to its context (user, platform, environment, device, etc.). In this sense, theories and methods of adaptations keep rolling in order to make learning processes more efficient and relevant. In this paper, we propose an approach for providing personalized course content in ubiquitous learning, considering learning styles and context-awareness. The proposed approach aims to support learners by presenting course materials generated by an adaptive engine based on adaptation rules