Unsupervised Learning for Understanding Diversity: Applying Feature Engineering and Cluster Analysis to Deaf and Hard of Hearing Data
- Title
- Unsupervised Learning for Understanding Diversity: Applying Feature Engineering and Cluster Analysis to Deaf and Hard of Hearing Data
- Creator
- Poly A.; Banu P.K.N.; Azar A.T.; Kamal N.A.
- Description
- As e-Learning emerges as a promising tool for instruction delivery, personalizing the e-Learning platform for DHH learners will benefit them to improve their learning engagement and educational attainment. This study aims to collect and analyze the different features unique to DHH learners and analyze the significant features among them. This study highlights the importance of addressing the diversity among DHH learners, while creating a personalized learning environment for them. With this focus, we employ the K-Means clustering algorithm to group the learners based on similar needs and preferences and identified that distinguishing clusters can be formed within the DHH group. We also tried to understand the significant features contributing to forming well separated groups. These results provide valuable insights into the diverse preferences and requirements when they interact with the learning materials. These findings emphasize the significance of personalized approach for DHH learners in educational settings and serve as the stepping stone to develop a personalized learning environment for them. 2024 IEEE.
- Source
- 2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- DHH; feature engineering clustering; k-means; personalization
- Coverage
- Poly A., Christ (Deemed To Be University), Department of Computer Science, Bangalore, India; Banu P.K.N., Christ (Deemed To Be University), Department of Computer Science, Bangalore, India; Azar A.T., Prince Sultan University, College of Computer and Information Sciences, Riyadh, 11586, Saudi Arabia, Prince Sultan University, Automated Systems and Soft Computing Lab (ASSCL), Riyadh, Saudi Arabia, Benha University, Faculty of Computers and Artificial Intelligence, Benha, Egypt; Kamal N.A., Cairo University, Faculty of Engineering, Giza, Egypt
- Rights
- Restricted Access
- Relation
- ISBN: 979-835036102-5
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Poly A.; Banu P.K.N.; Azar A.T.; Kamal N.A., “Unsupervised Learning for Understanding Diversity: Applying Feature Engineering and Cluster Analysis to Deaf and Hard of Hearing Data,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19411.