Predicting Academic Performance in students by an Analytical study on Big Data Machine Learning Techniques

Authors

  • J. Naren Assistant Professor, School of Computing, SASTRA Deemed University, Thanjavur, India Author
  • Dr.G. Vithya, Professor, School of Computing, KL University, Vijayawada, AP, India Author
  • Bharath Reddy B.Tech Electronics and Communication Engineering, School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur, Tamil Nadu, India Author

DOI:

https://doi.org/10.61841/6gc2qy06

Keywords:

Machine Learning, Big data, Data Mining

Abstract

Education is the key to solving the majority of world problems. It is important that the education imparted is assimilated by every student who aspires to learn. Classifying slow learners in the early stages so that necessary help can be given to improve their performance is one of the crucial responsibilities for educational institutions. Technology plays an important role in aiding in the process of classifying students. Various techniques have been developed to predict the performance of students accurately for addressing their learning needs and styles. In the present paper, a bird's-eye view of the data mining and machine learning techniques used for predicting student performance is presented. 

Downloads

Download data is not yet available.

References

[1] MuslihahWook, Yuhanim Hani Yahaya, NorshahriahWahab, Mohd Rizal Mohd Isa, Nor Fatimah Awang, Hoo

Yann Seong, (2009), Predicting NDUM Student’s Academic Performance Using Data Mining Techniques",

2009 Second International Conference on Computer and Electrical Engineering

[2] HashmiaHamsa, Simi Indiradevi, and Jubilant J. Kizhakkethottam (2016), "Student Academic Performance

Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm", Procedia Technology, Volume 25,

2016, Pages 326-332

[3] NorlidaBuniyamin, Usamah bin Mat, PauziahMohd Arshad (2015), "Educational Data Mining for Prediction

and Classification of Engineering Students Achievement", Engineering Education (ICEED), 2015 IEEE 7th

International Conference on 17-18 Nov. 2015

[4] Ishwank Singh, A Sai Sabitha, Abhay Bansal (2016), "Student performance analysis using clustering

algorithm", Cloud System and Big Data Engineering (Confluence), 2016 6th International Conference.

[5] YohannesKurniawan, Erwin Halim (2013), "Use data warehouse and data mining to predict student academic

performance in schools: A case study (perspective application and benefits)", Teaching, Assessment and

Learning for Engineering (TALE), 2013 IEEE International Conference on 26-29 Aug. 2013

[6] S Chaitanya Kumar, Deepak Chowdary, VenkatramaPhani Kumar S, Krishna Kishore (2016), "M5P model

tree in predicting student performance: A case study", 2016 IEEE International Conference on Recent Trends

in Electronics, Information & Communication Technology (RTEICT)

[7] TismyDevasia, Vinushree T, Vinayak Hegde (2016), "Prediction of Students Performance using Educational

Data Mining", 2016 International Conference onData Data Mining and Advanced Computing (SAPIENCE 2016)

[8] Zhenpeng Li, Changjing Shang and Qiang Shen (2016), "Fuzzy-clustering embedded regression for

predicting student academic performance", Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference

on 24-29 July 2016

[9] Muhammad Fahim Uddin, Jeongkyu Lee (2016), "Utilizing Relevant Academic and Personality Features from

Big Unstructured Data to Identify Good and Bad Fit Students", Procedia Computer Science, Volume

95, 2016, Pages 383-391

[10] Roshani Ade, P. R. Deshmukha (2015), "Efficient Knowledge Transformation System Using Pair of

Classifiers for Prediction of Students Career Choice", Procedia Computer Science, Volume 46, 2015, Pages

176 – 183

[11] Carlos J.Villagrá-Arnedo, Francisco J.Gallego-Durán, FaraónLlorens-Largo, Patricia Compañ-Rosique,

Rosana Satorre-Cuerda, Rafael Molina-Carmona (2017), "Improving the expressiveness of black-box models

for predicting student performance", Computers in Human Behavior, Volume 72, July 2017, Pages 621-631

[12] S. Rajeswari, R. Lawrance (2016), "Classification model to predict the learners' academic performance using

big data", Computing Technologies and Intelligent Data Engineering (ICCTIDE), International Conference on

7-9 Jan. 2016

[13] Manju Jose, PreethySinu Kurian, Biju V. (2016), "Progression analysis of students in a higher education

institution using big data open source predictive modeling tool", Big Data and Smart City (ICBDSC), 2016 3rd MEC International Conference on 15-16 March 2016.

[14] Jie Xu, KyeongHo Moon, Mihaela van der Schaar (2017), "A Machine Learning Approach for Tracking and

Predicting Student Performance in Degree Programs", IEEE Journal of Selected Topics in Signal Processing,

Volume: 11, Issue: 5, Aug. 2017, Pages 742 - 753.

[15] Radhika R Halde, Arti Deshpande, and Anjali Mahajan (2016), "Psychology-assisted Prediction of Academic

Performance using Machine Learning", 2016 IEEE International Conference on Recent Trends in Electronics,

Information & Communication Technology (RTEICT)

Downloads

Published

18.09.2024

How to Cite

Naren, J., Vithya, G., & Reddy, B. (2024). Predicting Academic Performance in students by an Analytical study on Big Data Machine Learning Techniques. International Journal of Psychosocial Rehabilitation, 23(1), 371-376. https://doi.org/10.61841/6gc2qy06