A Machine Learning Approach to Explore and Predict Student Motivation Types
Motivation plays a significant role in shaping students’ educational outcomes. Understanding the factors that influence student motivation is crucial for enhancing academic performance and designing effective learning environments. This study utilizes Self-Determination Theory to examine...
Saved in:
| Main Authors: | Hafsa Al Ansari, Rasha Shakir Abdulwahhab Al Jassim, Rupert Ward |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11126017/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Learning Motivation of Science Education Students on Biochemical Learning Outcomes: Profile and Correlation
by: Dea Santika Rahayu, et al.
Published: (2023-06-01) -
The Relationship between Academic Motivation Types and Learning Styles of Pre-service EFL Teachers
by: Derya Uysal
Published: (2022-12-01) -
Exploring student perceptions of the Osmosis digital learning platform in undergraduate medical education and its influences on motivation and inclusivity
by: Sanat Kulkarni, et al.
Published: (2025-07-01) -
My teacher, my peers, or myself? A collective case study in regards to classroom comfort and academic success concerning the taxonomy of motivations within self-determination theory.
by: Saliga, H.
Published: (2017-07-01) -
An Integrated Framework to Motivate Student Engagement in Science Education for Sustainable Development
by: Neil MacIntosh, et al.
Published: (2025-07-01)