Student performance prediction based on personal factors by using dashboard - Tan, Chung Hao (2024)
Within the field of education, this research initiative operates at the interface of methods of statistical and/or logical analysis used to describe, illustrate, summarize, and assess data. In order to improve instructional techniques, it handles the problem of comprehending and forecasting student performance based on individual factors. Conventional teaching methods frequently ignore the variety of individual circumstances affecting a student's performance. In order to provide educators with a data-driven tool to support targeted interventions and enhance overall student outcomes, this project aims to discover, model, and forecast these aspects. A six-step data science lifecycle, beginning with business understanding and ending with model deployment, is used in the study process. To build an extensive dataset, information is gathered from educational institutions and through surveys. An interactive dashboard is designed to provide instructors with insights, and machine learning methods are utilized in the development of the prediction model. The project starts with a thorough literature review to establish the theoretical basis. Following data comprehension, preprocessing, and modeling, the predictive model is created. The created prediction model shows excellent generalizability and accuracy in a variety of educational contexts. The model's efficacy in recognizing and forecasting student performance based on individual factors is demonstrated by evaluation measures like the SME (Mean Square Error). The successful implementation of an interactive dashboard, which provides educators with a useful tool for data-driven decision-making, marks the research's conclusion. By addressing the highlighted problem and providing a systematic way to analysing and forecasting student performance, the initiative contributes to the progress of educational practices.