ACADEMIC PERFORMANCE PREDICTION APPLICATION (APPA)

The research study offers a thorough description of the process of deployment after training and testing of the classification model respectively. The performance of students is a crucial prerequisite to help students that don’t perform well in the examination and can impact the final semester result. To overcome the difficulties they come across while learning and assist them to achieve the best results. The researcher uses the advantages of the React-Native platform to build an "APPA" mobile application capable of delivering student performance prediction-related solutions. It also provides a proposed model of student academic success prediction. The further study highlights the further scope of the mobile App built for predicting student performance.

Predicting student performance in a blended learning environment using learning management system interaction data

PurposeStudent attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.Design/methodology/approachThis study develops a new approach harnessing applications of machine learning (ML) models on a dataset, that is publicly available, relevant to student attrition to identify potential students at risk. The dataset consists of the data generated by the interaction of students with LMS for their BL environment.FindingsIdentifying students at risk through an innovative approach will promote timely intervention in the learning process, such as for improving student academic progress. To evaluate the performance of the proposed approach, the accuracy is compared with other representational ML methods.Originality/valueThe best ML algorithm random forest with 85% is selected to support educators in implementing various pedagogical practices to improve students’ learning.

A THEORETICAL ANALYSIS OF STUDENTS’ ACADEMIC ACHIEVEMENT AND ASSOCIATED FACTORS

The academic performance of student is influenced by several factors. Studies have been conducted in the field of educational data mining to find out what all the factors are that have an effect on a student's academic performance. There are many factors that can have an effect on a student's academic success, but our study aims to find the main factors that can have an effect on a student's academic performance. Predicting student performance becomes more difficult due to the large volume of information in academic databases. The responsibility of the teacher increases, she must be attentive to the activities and behavior of the student. To facilitate the work of a teacher, this study attempted to identify some factors that affect the academic performance of students. Our study could bring benefits and impacts to students, educators/teachers, and tutoring institutions. The factors investigated in this study are: student base knowledge, socio-economic status, college/school environment, environment pollution, teachers’ support, parental/family support, friend circle and health.