MODIFICATION OF K-MEANS AND K-MODE ALGORITHMS TO ENHANCE THE PERFORMANCE OF CLUSTERING STUDENT LEARNING STYLES IN THE LEARNING MANAGEMENT SYSTEM

During the Corona Virus Disease (COVID-19) pandemic, many access to learning used the e-learning system through the Learning Management System (LMS) platform. One of the weaknesses of the learning process through e-learning is that it cannot detect student learning styles based on actual behavior patterns during online learning. Most of the methods used to study automatic detection techniques use classification methods. One of the weaknesses of the classification method is the determination of class labels, so a learning style detection model was developed using the concept of clustering before classification to produce class labels with a high level of validation. This study focuses on increasing the validity of the clustering method by comparing the performance of the modified K-Means and K-Mode algorithms. The proposed modification of the two algorithms is carried out at the initial centroid determination stage. The performance of the two modified algorithms was carried out by measuring the validation values of the Davies-Bouldin Index (DBI) and Silhouette Index (SI) using log file data from 88 students taking computer programming courses. The validation results of the DBI and SI values indicate that the proposed model has better performance when implemented in the K-Mode algorithm than the K-Means algorithm. © 2023 ICIC International.

EFL Students' Interpretations of e-Learning during COVID-19 using GETAMEL: Indonesian Higher Education Context

While the use of e-learning has been around for decades, the global pandemic increased the number of investigations on e-learning exponentially. Earlier studies have given useful insights into the benefits/ impacts of e-learning. However, students' acceptance of technology within the context of emergency EFL remote teaching is still under-researched. A qualitative study framed within the General Extended Technology Acceptance Model for E-Learning (GETAMEL) aims to shed light on students' acceptance of technology during pandemics based on their perceived experience. It reports the challenges, opportunities of e-learning, and projections on future use based on the current experience. To collect the data, a questionnaire consisting of open and closed questions was distributed to 89 participants. In-depth interviews were conducted with focal respondents after gaining their consent. The data were then analyzed using the interactive model of data analysis. This study reveals that regardless of the negative experiences and challenges in the use of technology in e-learning, the students held positive perspectives and saw opportunities to use technology during the COVID-19 pandemic. They projected their future practice using the technology. These indicate that the students well accept the use of technology in the e-learning context. The study concluded that using e-learning during a pandemic is the ideal way to continue learning. However, given the challenges that students face, some changes in the implementation of distance learning are still needed. Additional studies should address GETAMEL on EFL teachers in an Indonesian school, so we know about the acceptance of e-learning by in-service teachers.