Clustering of Learners based on Readiness to Online Modality using K-Means Algorithm
Journal: International Journal of Advanced engineering, Management and Science (Vol.7, No. 9)Publication Date: 2021-09-13
Authors : Daryl B. Valdez Rey Anthony G. Godmalin;
Page : 01-05
Keywords : Clustering; K-means algorithm; data mining; online learning modality; learner’s segmentation.;
Abstract
Clustering is one of the important techniques in data mining. It is an unsupervised task of grouping similar data. It has been applied in various fields with high degree of success. This study aimed to determine the learner segments based on readiness to online learning modality using K-means algorithm. A dataset was collected, tabulated and pre-processed. Further, the values were scaled and transformed using t-distributed Stochastic Neighbor Embedding. Using elbow method and determining the silhouette score, the best K value was determined. Then clustering was conducted using the selected number of clusters. Results revealed three groups of learners; Moderate-signal mobile users, Low-signal mobile users, and mixed group of Low/moderate-signal mobile/broadband users. Students from the different clusters are more suited for flexible learning as opposed to online learning. Varied learning modalities can be catered for students from the different learner segments. Formulation and adoption of new policies are needed to offset the effect of the pandemic towards the students.
Other Latest Articles
- A STUDY ON THE RATE OF INNOVATION ACTIVITIES TO STRENGTHEN R&D CAPABILITIES
- IMPROVEMENT OF ORGANIZATIONAL COMMITMENT AND JOB SATISFACTION FOR NON-REGULAR WORKERS
- A STUDY ON CUSTOMER AWARENESS OF SLICEPAY AMONG COLLEGE STUDENTS IN CHENNAI
- REVIEW ON MODELLING AND ANALYSIS OF VARIABLE FLUX MACHINES FOR TRACTION APPLICATION
- SYSTEM EFFICIENCY AND SUSTAINABLE TRANSPORT IN INDIA A RETROSPECTION
Last modified: 2021-09-13 15:04:49