Introducing a Cutting-Edge Dataset: Revealing Key Factors in Student Academic Outcomes and Learning Processes - Son, Nguyen Thi Kim; Hoa, Nguyen Hong; Trang, Hoang Thi Thu; Ngan, Tran Quynh (VNU Journal of Science: Education Research, 2024)
This paper presents an educational dataset that consolidates various aspects of educational science. The purpose of this study is to illuminate the factors that impact students’ learning experiences before and during their time at university. This dataset was designed to support research in educational science, including the application of machine learning and deep learning models to predict student outcomes. The primary objective is to improve educational methodologies, empower students with informed decision-making, and enhance overall learning effectiveness. The dataset comprises 992 samples across 89 fields and was collected through direct methods like questionnaires and indirect methods involving training management units. These samples are categorized into Personalized factors, Factors affecting learning outcomes, and Learning outcomes, encompassing both general education performance and university module achievements. Following collection, the dataset was subjected to thorough processing, cleaning, and statistical analysis, using techniques such as Pearson correlation analysis, analysis of variance, Std. Error, Std. Deviation, and tests of homogeneity of variance.