Using Artificial Intelligence to Improve Classroom Learning Experience
It addresses student engagement and retention in education, but is incremental as it applies standard machine learning methods to educational data.
This paper tackled improving classroom learning by using logistic regression to identify student learning styles and predict academic dropout risk, achieving 87.39% accuracy in dropout prediction on a dataset of 76,519 candidates.
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.