PREDICTION OF GRADES ANALYSIS AT PRIMARY SCHOOL LEVEL PAKISTAN

Abstract

In the era of digital advancements, education has undergone a profound transformation through the integration of data science and predictive analytics. The application of data science methodologies in education revolves around the collection and analysis of extensive data from diverse sources, encompassing student demographics, attendance records, assessment scores, and more. This research offers an overview of the fundamental elements and implications of utilizing data science to predict student performance within educational institutions. The findings show that attitudes towards the current grading system are varied: 53% of respondents are satisfied with it, while 42% think there are many factors which need to be considered to determine academic success. Opinions differed as to whether the grading system reflects a child's true capabilities; 48% agreed, whereas 14% disagreed. There is also no consensus regarding how parents would like to be kept informed about their children’s progress at school; some would rather have regular meetings with teachers, others for example, prefer sending emails, etc., so that each parent can choose his/her preferred communication channel. Thus, according to 48% of those surveyed believe there should be Parent-Teacher Conferences (PTCs) while another 42% feel more resources will make prediction and improvement better respectively.