2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 10 , Issue 2 , PP: 24-37, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Rahul Sharma 1 , Shiv Shakti Shrivastava 2 , Aditi Sharma 3 *
Doi: https://doi.org/10.54216/JISIoT.100203
Data analysis is an essential component of decision support in various industries that includes industrial and educational institutions. This research proposes Data Mining (DM) techniques to improve the efficiency of higher education (HE) institutions. DM has a substantial impact on different higher education activities including student performances, management of student’s life cycle, selection of courses, monitoring of retention rate, grants & funds management by using technique’s such as clustering, decision trees (DT), and association. Educational Data Mining (EDM) is an interdisciplinary study topic that focuses on getting DM to the fields of education by leveraging methods from (ML) statistics, (DM), and (DA) to get important insights from educational sets of data. EDM is critical in transforming raw data into useful information, allowing for a greater knowledge of students and their academic settings, as well as promoting better teacher assistance and ESD (Educational System Decisions). The study's goal is to provide a complete overview of EDM (Educational Data Mining), highlighting its various applications and benefits in the context of higher education.
EDM (Educational Data Mining) , DM (Data mining) techniques , Data processing methods , Knowledge discovery in databases (KDD) , Learning analytics (LA) , EDM tools , Visualizations , tools were all examples of data mining strategies (DMS)
[1] Dr. P. Nithya, B. Umamaheswari, A. Umadevi – “A Survey on Educational Data Mining in Field of Education” – International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 5 Issue 1, January 2016.
[2] Aykroyd, R.G.; Leiva, V.; Ruggeri, F. Recent developments of control charts, identification of big data sources and future trends of current research. Technol. Forecast. Soc. Chang., 144, 221–232, 2019.
[3] Hooshyar, D.; Pedaste, M.; Yang, Y. Mining educational data to predict students’ performance through procrastination behavior, Entropy, Volume 22, Issue 12, 2020.
[4] Reem Atassi,Aditi Sharma. "Intelligent Traffic Management using IoT and Machine Learning." Journal of Intelligent Systems and Internet of Things, Vol. 8, No. 2, 2023 ,PP. 08-19.
[5] V. Gupta, N. Kumar, A. Sharma and A. Abraham, "Sensor Routing Protocol with Optimized Delay and Overheads in Mobile based WSN", Journal of Information Assurance & Security, vol. 16, no. 4, 2021.
[6] Bakhshinategh, B.; Zaiane, O.R.; Elatia, S.; Ipperciel, D. Educational data mining applications and tasks: A survey of the last 10 years. Educ. Inf. Technol., pp. 537–553, Volume 23, 2018.
[7] Del Bonifro, F.; Gabbrielli, M.; Lisanti, G.; Zingaro, S.P. Student Dropout Prediction. In Artificial Intelligence in Education; Bittencourt, I., Cukurova, M., Muldner, K., Luckin, R., Millán E., Eds.; Springer: Cham, Switzerland, 2020.
[8] V. Goar, A. Sharma, N. S. Yadav, S. Chowdhury and Y.-C. Hu, "IOT-based smart mask protection against the waves of covid-19", Journal of Ambient Intelligence and Humanized Computing, 2022.
[9] Lázaro, N.; Callejas, Z.; Griol, D. Predicting computer engineering student’s dropout in cuban higher education with pre-enrollment and early performance data. J. Technol. Sci. Educ. 2020, 10, 241–258.
[10] J. R. Albert and A. Sharma, "Investigation on load harmonic reduction through solar-power utilization in intermittent SSFI using particle swarm genetic and modified firefly optimization algorithms", Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4117-4133, 2022.
[11] Mduma, N.; Kalegele, K.; Machuve, D. Machine learning approach for reducing student’s dropout rates. Int. J. Adv. Comput. Res., Volume 9, 156–169, 2019.
[12] P.Sinha, M. Arora, N. Mishra, “Framework for a Knowledge Management Platform in Higher Education Institutions”, Volume 2, Issue 4, September 2012
[13] Goyal, R. Vohra, “Applications of Data Mining in Higher Education”, International Journal of Computer Science Issues, Vol. 9, Issue 2, No1, March 2012.
[14] Reich, J., Tingley, D., Leder-Luis, J., Roberts, M. E., & Stewart, B. (2014). Computer-Assisted Reading and Discovery for Student Generated Text in Massive Open Online Courses. Journal of Learning Analytics, 2(1), 156–184.
[15] A. U. Khasanah et al., “A comparative study to predict student’s performance using educational data mining techniques,” in IOP Conference Series: Materials Science and Engineering, vol. 215, p. 012036, IOP Publishing, 2017.
[16] M. Makhtar, H. Nawang, and S. N. Wan Shamsuddin, “Analysis on students’ performance using naive bayes classifier.” Journal of Theoretical & Applied Information Technology, vol. 95, no. 16, 2017.
[17] S. Hussain, N. A. Dahan, F. M. Ba-Alwib, and N. Ribata, “Educational data mining and analysis of students’ academic performance using weka,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 9, no. 2, pp. 447– 459, 2018.
[18] N D Lynn and A W R Emanuel, Using Data Mining Techniques to Predict Students' Performance. A Review, IOP Conf. Series: Materials Science and Engineering 1096 (2021). doi:10.1088/1757-899X/1096/1/012083.
[19] Snježana Križanić, Educational data mining using cluster analysis and decision tree technique: A case study, International Journal of Engineering Business Management (2020). https://doi.org/10.1177/1847979020908675.
[20] Rahul Sharma and Dr. Shiv Shakti Shrivastava Predicting of Student Performance using Data Mining Classification Techniques, World Journal of Engineering Research and Technology (WJERT), Vol. 9, Issue 3, 2023.
[21] Yang, F., & Li, F. W. B. (2018). Study on student performance estimation, student progress analysis, and student potential prediction based on data mining. Computers and Education, 123(April): 97–108. https://doi.org/10.1016/j.compedu.2018.04.006.
[22] B. Patel, C. Gondaliya, “Student Performance Analysis Using Data Mining Technique”, International Journal of Computer Science and Mobile Computing IJCSMC, Vol.6 Issue.5, ISSN 2320–088X IMPACT FACTOR: 6.017, May- 2017, pg. 64-71.
[23] Vilanova, R., Dominguez, M., Vicario, J., Prada, M. A., Barbu, M., Varanda, M. J., Alves, P., Podpora, M., Spagnolini, U., & Paganoni, A. (2019). Data-driven tool for monitoring of student’s performance. IFAC-PapersOnLine, 52(9): 190–195. https://doi.org/10.1016/j.ifacol.2019.08.188
[24] M. C. R, S. Sharma, A. Sharma, M. Sunil Kumar, S. Kelkar and S. Vishal Deshmukh, "Cloud Top Management Role in Reducing Mobile Broadband Transmission Hazards and Offering Safety," 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2023, pp. 1064-1068, doi: 10.1109/ICACITE57410.2023.10182893.
[25] Babandi Usman, Rabi’u Adamu and Sani Salisu, Prediction of Student Performance Using Classification Technique, International Journal of Information Processing and Communication (IJIPC) 8(1): (May, 2020).
[26] Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., Murray, D. J., & Long, Q. (2018). Predicting academic performance by considering student heterogeneity. KnowledgeBased Systems, 161: 134–146. https://doi.org/10.1016/j.knosys.2018.0
