Using Artificial Intelligence to Improve Classroom Learning Experience
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916646804783104 |
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| author | Hossain, Shadeeb |
| author_facet | Hossain, Shadeeb |
| contents | This paper explores advancements in Artificial Intelligence technologies to enhance classroom learning, highlighting contributions from companies like IBM, Microsoft, Google, and ChatGPT, as well as the potential of brain signal analysis. The focus is on improving students learning experiences by using Machine Learning algorithms to : identify a student preferred learning style and predict academic dropout risk. A Logistic Regression algorithm is applied for binary classification using six predictor variables, such as assessment scores, lesson duration, and preferred learning style, to accurately identify learning preferences. A case study, with 76,519 candidates and 35 predictor variables, assesses academic dropout risk using Logistic Regression, achieving a test accuracy of 87.39%. In comparison, the Stochastic Gradient Descent classifier achieved an accuracy of 83.1% on the same dataset. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_05709 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Using Artificial Intelligence to Improve Classroom Learning Experience Hossain, Shadeeb Computers and Society Human-Computer Interaction Machine Learning This paper explores advancements in Artificial Intelligence technologies to enhance classroom learning, highlighting contributions from companies like IBM, Microsoft, Google, and ChatGPT, as well as the potential of brain signal analysis. The focus is on improving students learning experiences by using Machine Learning algorithms to : identify a student preferred learning style and predict academic dropout risk. A Logistic Regression algorithm is applied for binary classification using six predictor variables, such as assessment scores, lesson duration, and preferred learning style, to accurately identify learning preferences. A case study, with 76,519 candidates and 35 predictor variables, assesses academic dropout risk using Logistic Regression, achieving a test accuracy of 87.39%. In comparison, the Stochastic Gradient Descent classifier achieved an accuracy of 83.1% on the same dataset. |
| title | Using Artificial Intelligence to Improve Classroom Learning Experience |
| topic | Computers and Society Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2503.05709 |