ANALYZING STUDENTS’ ACADEMIC PERFORMANCE BASED ON FAMILY BACKGROUNDS USING MS-AZURE ML STUDIO

Abstract

When it comes to a student's academic performance, it's impact on career progression and development is evident. The base for a student's academic performance depends on varied family and academic backgrounds to which they get exposed to. For instance, one student may be able to acquire knowledge and utilize the opportunities provided by the educational institution he or she is exposed to; another would be proactive due to family background and support. Improvising student's academic performance amidst hindrances from varied sources also has become a challenge for today's student group. They find it difficult to overcome the varied hindrances even though they shine in their academic performance. Hence, the need to predict the students’ academic performance based on the family and academic background is realized by the researchers in this study and has attempted to analyze the underlying factors and problems.

This study aims at collating the demographic details of the students which is the basis of the study for academic performance and their underlying relationships are analyzed. In addition to the demographic factors other co-curricular and extra-curricular activities to which students are exposed to be also identified as the basis of the study.

The collated data and the formulated hypothesis, based on the factors with the dataset of 450 students are scrutinized and analyzed in this study. With the collected data, Microsoft Azure machine learning tool is used for further analysis and identify the prominent factors that affect a students' academic performance.

Based on categorization, a suitable classification model is planned to be devised by the researchers. This model will pave the way for a student’s academic group and future researchers to tap into the factors crucial to the performance and handle them in a better manner while taking decisions. This would ultimately ensure in positive outcomes in academic performance as a result.