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...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11126017/ |
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| author | Hafsa Al Ansari Rasha Shakir Abdulwahhab Al Jassim Rupert Ward |
| author_facet | Hafsa Al Ansari Rasha Shakir Abdulwahhab Al Jassim Rupert Ward |
| author_sort | Hafsa Al Ansari |
| collection | DOAJ |
| description | 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 various types of motivation, aiming to develop an integrated framework for analyzing and predicting student motivation. The proposed framework employs multi-source data and evaluates three machine learning techniques: Multinomial Regression, Support Vector Machine, and XGBoost. These models are applied to data collected from a UK-based institution, specifically from the Computer Science department. The findings highlight the superior performance of the XGBoost model in identifying learning analytics characteristics that influence each motivation type, achieving precision ranging from 95% to 100%. Additionally, this study explores the underlying philosophy of using LMS data and its features to classify student motivation, supporting the effectiveness of XGBoost in this context. While the results are promising within the context of a single-institution Computer Science setting, further studies are needed to validate the applicability of this methodology across broader educational frameworks. |
| format | Article |
| id | doaj-art-35b6c3ffddbd4f3fbc79f3f118178f31 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-35b6c3ffddbd4f3fbc79f3f118178f312025-08-25T23:18:12ZengIEEEIEEE Access2169-35362025-01-011314490814492610.1109/ACCESS.2025.359916611126017A Machine Learning Approach to Explore and Predict Student Motivation TypesHafsa Al Ansari0https://orcid.org/0000-0001-9500-4087Rasha Shakir Abdulwahhab Al Jassim1https://orcid.org/0000-0002-7526-070XRupert Ward2https://orcid.org/0000-0003-1514-5870Computing and Engineering Department, University of Huddersfield, Huddersfield, U.K.Department of Information Technology, University of Technology and Applied Sciences, Sohar, OmanComputing and Engineering Department, University of Huddersfield, Huddersfield, U.K.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 various types of motivation, aiming to develop an integrated framework for analyzing and predicting student motivation. The proposed framework employs multi-source data and evaluates three machine learning techniques: Multinomial Regression, Support Vector Machine, and XGBoost. These models are applied to data collected from a UK-based institution, specifically from the Computer Science department. The findings highlight the superior performance of the XGBoost model in identifying learning analytics characteristics that influence each motivation type, achieving precision ranging from 95% to 100%. Additionally, this study explores the underlying philosophy of using LMS data and its features to classify student motivation, supporting the effectiveness of XGBoost in this context. While the results are promising within the context of a single-institution Computer Science setting, further studies are needed to validate the applicability of this methodology across broader educational frameworks.https://ieeexplore.ieee.org/document/11126017/Self-determination theorymotivationmachine learninglearning analyticspredictionXGBoost |
| spellingShingle | Hafsa Al Ansari Rasha Shakir Abdulwahhab Al Jassim Rupert Ward A Machine Learning Approach to Explore and Predict Student Motivation Types IEEE Access Self-determination theory motivation machine learning learning analytics prediction XGBoost |
| title | A Machine Learning Approach to Explore and Predict Student Motivation Types |
| title_full | A Machine Learning Approach to Explore and Predict Student Motivation Types |
| title_fullStr | A Machine Learning Approach to Explore and Predict Student Motivation Types |
| title_full_unstemmed | A Machine Learning Approach to Explore and Predict Student Motivation Types |
| title_short | A Machine Learning Approach to Explore and Predict Student Motivation Types |
| title_sort | machine learning approach to explore and predict student motivation types |
| topic | Self-determination theory motivation machine learning learning analytics prediction XGBoost |
| url | https://ieeexplore.ieee.org/document/11126017/ |
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