Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning

Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate... Forecasting academic performance of student has been a substantial research inquest in the Educational Data-Mining that utilizes Machine-learning (ML) procedures to probe the data of educational setups. Quantifying student academic performance is challenging because academic performance of students hinges on several factors. The in hand research work focuses on students’ grade and marks prediction utilizing supervised ML approaches. The data-set utilized in this research work has been obtained from the Board of Intermediate & Secondary Education (B.I.S.E) Peshawar, Khyber Pakhtunkhwa. There are 7 areas in BISEP i.e., Peshawar, FR-Peshawar, Charsadda, Khyber, Mohmand and Upper and Lower Chitral. This paper aims to examine the quality of education that is closely related to the aims of sustainability. The system has created an abundance of data which needs to be properly analyzed so that most useful information should be obtained for planning and future development. Grade and marks forecasting of students with their historical educational record is a renowned and valuable application in the EDM. It becomes an incredible information source that could be utilized in various ways to enhance the standard of education nationwide. Relevant research study reveals that numerous methods for academic performance forecasting are built to carryout improvements in administrative and teaching staff of academic organizations. In the put forwarded approach, the acquired data-set is pre-processed to purify the data quality, the labeled academic historical data of student (30 optimum attributes) is utilized to train regression model and DT-classifier. The regression will forecast marks, while grade will be forecasted by classification system, eventually analyzed the results obtained by the models. The results obtained show that machine learning technology is efficient and relevant for predicting students performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Data Science Springer Journals

Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning

Hussain, Shah; Khan, Muhammad Qasim
Annals of Data Science , Volume 10 (3): 19 – Jun 1, 2023
Download PDF
19 pages

Loading next page...
 
/lp/springer-journals/student-performulator-predicting-students-academic-performance-at-KWouTpTdlT

References (36)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
ISSN
2198-5804
eISSN
2198-5812
DOI
10.1007/s40745-021-00341-0
Publisher site
See Article on Publisher Site

Abstract

Forecasting academic performance of student has been a substantial research inquest in the Educational Data-Mining that utilizes Machine-learning (ML) procedures to probe the data of educational setups. Quantifying student academic performance is challenging because academic performance of students hinges on several factors. The in hand research work focuses on students’ grade and marks prediction utilizing supervised ML approaches. The data-set utilized in this research work has been obtained from the Board of Intermediate & Secondary Education (B.I.S.E) Peshawar, Khyber Pakhtunkhwa. There are 7 areas in BISEP i.e., Peshawar, FR-Peshawar, Charsadda, Khyber, Mohmand and Upper and Lower Chitral. This paper aims to examine the quality of education that is closely related to the aims of sustainability. The system has created an abundance of data which needs to be properly analyzed so that most useful information should be obtained for planning and future development. Grade and marks forecasting of students with their historical educational record is a renowned and valuable application in the EDM. It becomes an incredible information source that could be utilized in various ways to enhance the standard of education nationwide. Relevant research study reveals that numerous methods for academic performance forecasting are built to carryout improvements in administrative and teaching staff of academic organizations. In the put forwarded approach, the acquired data-set is pre-processed to purify the data quality, the labeled academic historical data of student (30 optimum attributes) is utilized to train regression model and DT-classifier. The regression will forecast marks, while grade will be forecasted by classification system, eventually analyzed the results obtained by the models. The results obtained show that machine learning technology is efficient and relevant for predicting students performance.

Journal

Annals of Data Science – Springer Journals

Published: Jun 1, 2023

Keywords: Supervised learning; Educational data-mining (EDM); Machine-learning (ML); Students’ performance prediction; Learning analytics

There are no references for this article.