A novel predicting students performance approach to competency & hidden risk factor identifier using a various machine learning classifiers

Author:

Sathya V.1,Mahendra Babu G.R.2,Ashok J.3,Lakkshmanan Ajanthaa4

Affiliation:

1. Department of Artificial Intelligence and Data Science Panimalar Engineering College, Chennai, Tamil Nadu, India

2. Department of ECE, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India

3. V.S.B. Engineering College, Karur, Tamil Nadu, India

4. Sathyabama University, Chennai, Tamil Nadu, India

Abstract

In a recent survey conducted in the year of 2022, it came to know that India contains around 50 percentages of its population is occupied by young people belonging to the age group of 25 and falls under a category of student. Guiding this young mass people in a right guidelines and strengthening the future of this students are a huge responsibility. The power of students are elder citizens of India such as their parent’s teachers, professors, entrepreneur etc. The only way to strengthen the future of the young students is through educating them. In order to analyze how effectively the student can compete with the modern world and what kind of teaching methodology are needed to be adopted to each student, which is very important to every country peoples. Therefore, we are provided to be interpret the effective education to the children’s and students. To monitor student’s academic record there is a need of building a new model which can predict the performance and risk factor associated with the students for the next academic year. This is done by gathering the student’s previous academic record to analysis with different classifier techniques. The proposed work is aim to build a model, that can predict the student’s competency level and the risk factors or students are improve himself with all fields effort to both academic and non-academic activity. This proposed model also helps the parents, teachers, and educational institutes. This entire analysis is done by using different Machine Learning (ML) bifurcations algorithm. Also we aim to find the best classifier which can emerge with a highest predicting accuracy among all other classifiers to the above said problems.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference20 articles.

1. Learning designand learning analytics in mobile and ubiquitous learning: Asystematic review;Pishtari;Br J Educ Technol,2020

2. A comon between mobile and ubiquitous learning from the perspective of human- computer interaction;Aljohani;International Journal of Mobile Learning and Organization,2012

3. A reference model for learning analytics;Chatti;International Journal of Technology Enhanced Learning,2012

4. Power Quality Improvement in Modular Multilevel Inverter Using for Different Multicarrier PWM;Kaliannan;European Journal of Electrical Engineering and Computer Science,2021

5. A Hybrid Deep Learning Model for Effective Segmentation and Classification of Lung Nodules from CT Images;Murugesan;Journal of Intelligent and Fuzzy System,2021

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3