Identifying Slow Learners in an e-Learning Environment Using K-Means Clustering Approach
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| Title: | Identifying Slow Learners in an e-Learning Environment Using K-Means Clustering Approach |
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| Language: | English |
| Authors: | Beena Joseph (ORCID |
| Source: | Knowledge Management & E-Learning. 2023 15(4):539-553. |
| Availability: | Laboratory of Knowledge Management & E-Learning. Web site: http://www.kmel-journal.org/ojs/index.php/online-publication |
| Peer Reviewed: | Y |
| Page Count: | 16 |
| Publication Date: | 2023 |
| Document Type: | Journal Articles Reports - Evaluative |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Electronic Learning, Learning Management Systems, Slow Learners, Educational Environment, Educational Practices, Online Courses, Teaching Methods, Barriers, Student Experience, Student Motivation, Undergraduate Students, Time on Task, Learner Engagement, Cluster Grouping, Algorithms, Cognitive Ability, Difficulty Level |
| ISSN: | 2073-7904 |
| Abstract: | Currently, the majority of e-learning lessons created and disseminated advocate a "one-size-fits-all" teaching philosophy. The e-learning environment, however, includes slow learners in a noticeable way, just like in traditional classroom settings. Learning analytics of educational data from a learning management system (LMS) have been considered by the researchers as a potential means of identifying slow e-learners and supporting, contesting, and altering present educational practices in e-learning. We used the students' rates of learning and grade points along with the total learning time, which is calculated from the time series log data, to cluster the learners. The rate at which a student learns determines whether he or she is a slow learner, an average learner, or a gifted learner. For classifying learners, we followed a step-by-step procedure that included instructional design to create a dataset, learning analytics of the dataset, and a machine learning strategy to cluster e-learners. The system has been adequately integrated with the methods for measuring student learning. A strategy based on the revised Bloom's Taxonomy is offered for the assessment of learners. The K-Means clustering approach is used to group learners who have similar performance without collecting a learner's previous academic records or demographic information. In the experimental evaluation, 7.7% of e-learners are grouped as slow learners, while advanced learners make up 61.3 percent of the student body and average learners make up 31 percent. According to the study, there is a correlation between learning rate and academic success, with fast learners having a lower learning rate. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1412110 |
| Database: | ERIC |
| Abstract: | Currently, the majority of e-learning lessons created and disseminated advocate a "one-size-fits-all" teaching philosophy. The e-learning environment, however, includes slow learners in a noticeable way, just like in traditional classroom settings. Learning analytics of educational data from a learning management system (LMS) have been considered by the researchers as a potential means of identifying slow e-learners and supporting, contesting, and altering present educational practices in e-learning. We used the students' rates of learning and grade points along with the total learning time, which is calculated from the time series log data, to cluster the learners. The rate at which a student learns determines whether he or she is a slow learner, an average learner, or a gifted learner. For classifying learners, we followed a step-by-step procedure that included instructional design to create a dataset, learning analytics of the dataset, and a machine learning strategy to cluster e-learners. The system has been adequately integrated with the methods for measuring student learning. A strategy based on the revised Bloom's Taxonomy is offered for the assessment of learners. The K-Means clustering approach is used to group learners who have similar performance without collecting a learner's previous academic records or demographic information. In the experimental evaluation, 7.7% of e-learners are grouped as slow learners, while advanced learners make up 61.3 percent of the student body and average learners make up 31 percent. According to the study, there is a correlation between learning rate and academic success, with fast learners having a lower learning rate. |
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| ISSN: | 2073-7904 |
