Applying adaptive learning by integrating semantic and machine learning in proposing student assessment model

Abstract

<p>Adaptive learning is one of the most widely used data driven approach to teaching and it received an increasing attention over the last decade. It aims to meet the student’s characteristics by tailoring learning courses materials and assessment methods. In order to determine the student’s characteristics, we need to detect their learning styles according to visual, auditory or kinaesthetic (VAK) learning style. In this research, an integrated model that utilizes both semantic and machine learning clustering methods is developed in order to cluster students to detect their learning styles and recommend suitable assessment method(s) accordingly. In order to measure the effectiveness of the proposed model, a set of experiments were conducted on real dataset (Open University Learning Analytics Dataset). Experiments showed that the proposed model is able to cluster students according to their different learning activities with an accuracy that exceeds 95% and predict their relative assessment method(s) with an average accuracy equals to 93%.</p>

Framework for the Adaptive Learning of Higher Education Students in Virtual Classes in Peru Using CRISP-DM and Machine Learning

“…Literature shows that five ML algorithms have been applied for the following purposes: Prediction of the student's performance (Evangelista, 2021;Lincke et al, 2021;Qiu et al, 2022;Sense et al, 2021), recommendation of appropriate actions to improve the quality of courses (Hosny and Elkorany, 2022;Yanes et al, 2020), recommendation of individualized learning resources (Arsovic and Stefanovic, 2020;Cheng and Wang, 2021;Ling and Chiang, 2022), prediction of the best academic engineering program for the student (Ezz and Elshenawy, 2020). Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) have been used to predict the performance of students' evaluation, being DT and RF the most accurate algorithms, exceeding 90 % (Evangelista, 2021).…”

“…On the other hand, K-means clustering, DT, LR, RF, and SVM have been the algorithms used to recommend appropriate actions for teachers to improve the academic performance of their students. K-means obtained the best accuracy, with 93% (Hosny and Elkorany, 2022), while DT achieved the best accuracy, with 69.23% (Ling and Chiang, 2022), for the recommendation of study content material. LR, RF, and SVM algorithms were used to recommend the most suitable engineering department for each student, LR obtained the best measure of accuracy based on both the precision and the recall, with 91% (Ezz and Elshenawy, 2020).…”

“…However, the articles Clark et al (2022); White (2020) that exclusively considered this factor failed to greatly satisfy the learning experience of students (Clark and Kaw, 2020;Tavakoli et al, 2022). For their part, the articles that used the speed of learning of students and interactions within a learning platform limited the ability of the system to provide an adequate recommendation, since these factors do not provide a complete picture of the learning process of students (Hamim et al, 2021;Hosny and Elkorany, 2022;Ling and Chiang, 2022).…”

Communication barriers in learning during pandemic and its pedagogical approaches

“…It basically adapts learning courses to meet the student's characteristics. It also provides flexibility, as students are not constrained to a specific class schedule or a predefined content [23]. Students can use adaptive learning to tailor their learning experiences to their needs and preferences.…”