International Journal of Mathematical Sciences and Computing (IJMSC)
IJMSC Vol. 8, No. 2, 8 Jun. 2022
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A Comparative Analysis among Online and On-Campus Students Using Decision Tree
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Author(s)
Rifat-Ibn-Alam
1,*
Md. Golam Ahsan Akib
1
Nyme Ahmed
1
Syed Nafiul Shefat
1
Dip Nandi
2
Index Terms
Educational data mining, Data Analytics, Decision Tree, E-learning.
Abstract
COVID-19 hit the world unexpectedly, forcing humans to isolate themselves. It has placed the lives of people in jeopardy with its fury. The global pandemic had a detrimental effect on the worlds' education spheres. It has imposed a global lockdown, with a negative impact on the students' lives. Continuing regular classes on-campus was out of the question. At that moment, online learning came to us as a savior. The quality of online education was yet to be tested on a large scale compared to regular schooling. Educational data mining is a modern arena that holds promise for those who work in education. Data mining strategies are developed to uncover latent information and identify valuable trends that can increase students' performance and, in turn, contribute to the improvement of the educational system in the long run. This research mainly aims to identify a comparative analysis of the students' academic performance between online and on-campus environments and distinguish the significant characteristics that influence their academic endeavors. The impact of the factors on the students' performance is visualized with the help of the Decision Tree Classification Model. This paper will assist in giving a good overview that influences the distinguished factors on students' academic performance. Moreover, educators will also be benefited from this paper while making any important decision regarding the educational activity.
Cite This Paper
Rifat-Ibn-Alam, Md. Golam Ahsan Akib, Nyme Ahmed, Syed Nafiul Shefat, Dip Nandi," A Comparative Analysis among Online and On-Campus Students Using Decision Tree ", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.8, No.2, pp. 11-27, 2022. DOI: 10.5815/ijmsc.2022.02.02
Reference
[1]Syed Tahir Hijaz and SMM Raza Naqvi. Factors affecting students' performance: A case of private colleges in bangladesh. Journal of sociology, 3(1):44–45, 2006.
[2]Brijesh Kumar Bhardwaj and Saurabh Pal. Data mining: A prediction for performance improvement using classification. arXiv preprint arXiv:1201.3418, 2012.
[3]Pedro Strecht, LuÃs Cruz, Carlos Soares, Joà Mendes-Moreira, et al. A comparative study of classification and regression algorithms for modelling students' academic performance. International Educational Data Mining Society, 2015.
[4]Jiawei Han, Jian Pei, and Micheline Kamber. Data mining: concepts and techniques. Elsevier, 2011.
[5]Max Bramer. Principles of data mining, volume 180. Springer, 2007.
[6]Sathe, M.T. and Adamuthe, A.C., 2021. Comparative Study of Supervised Algorithms for Prediction of Students' Performance. International Journal of Modern Education & Computer Science, 13(1).
[7]Nandi, D., Hamilton, M., Harland, J. and Mahmood, S., 2015. Investigation of participation and quality of online interaction. International Journal of Modern Education and Computer Science, 7(8), p.25.
[8]Ali, M. T., Rahman, M. A. and Lamagna, C. Z. (2021) "Comparative Analysis of Program Outcomes Achievement between Face-to-Face and Virtual Classes during COVID-19 Pandemic", AIUB Journal of Science and Engineering (AJSE), 20(1), pp. 1 - 7. Available at: http://ajse.aiub.edu/index.php/ajse/article/view/150 (Accessed: 8 July 2021).
[9]Rivas, A., Gonzalez-Briones, A., Hernandez, G., Prieto, J. and Chamoso, P., 2021. Artificial neural network analysis of the academic performance of students in virtual learning environments. Neurocomputing, 423, pp.713-720.
[10]Moreno-Marcos, P.M., Pong, T.C., Munoz-Merino, P.J. and Kloos, C.D., 2020. Analysis of the factors influencing learners' performance prediction with learning analytics. IEEE Access, 8, pp.5264-5282.
[11]Sadiq Hussain, Neama Abdulaziz Dahan, Fadl Mutaher Ba-Alwib, and Najoua Ribata. Educational data mining and analysis of students' academic performance using weka. Indonesian Journal of Electrical Engineering and Computer Science, 9(2):447–459, 2018.
[12]Zainab Mohammed Ali, Noor Hasan Hassoon, Wasan Saad Ahmed, and Hazim Noman Abed. The application of data mining for predicting academic performance using k-means clustering and naïve bayes classification. International Journal of Psychosocial Rehabilitation, 24(03), 2020.
[13]T Miranda Lakshmi, A Martin, R Mumtaj Begum, and V Prasanna Venkatesan. An analysis on performance of decision tree algorithms using student's qualitative data. International journal of modern education and computer science, 5(5):18, 2013.
[14]Slamet Wiyono and Taufiq Abidin. Comparative study of machine learning knn, svm, and decision tree algorithm to predict student's performance. International Journal of ResearchGranthaalayah, 7(1):190–196, 2019.
[15]Ahmed Mueen, Bassam Zafar, and Umar Manzoor. Modeling and predicting students' academic performance using data mining techniques. International journal of modern education & computer science, 8(11), 2016.
[16]Dipta Das, Asif Khan Shakir, Md Shah Golam Rabbani, Mostafijur Rahman, Syamimi Mardiah Shaharum, Sabira Khatun, Norasyikin Binti Fadilah, Khandker M Qaiduzzaman, Md Shariful Islam, and Md Shohel Arman. A comparative analysis of four classification algorithms for university students performance detection. In InECCE2019, pages 415–424. Springer, 2020.
[17]Evandro B Costa, Baldoino Fonseca, Marcelo Almeida Santana, Fabrísia Ferreira de Araújo, and Joilson Rego. Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses. Computers in Human Behavior, 73:247–256, 2017.
[18]Raheela Asif, Agathe Merceron, Syed Abbas Ali, and Najmi Ghani Haider. Analyzing undergraduate students' performance using educational data mining. Computers & Education, 113:177–194, 2017.
[19]Jena, P.K., 2020. Impact of pandemic COVID-19 on education in India. International Journal of Current Research (IJCR), 12.
[20]Sakpere, A.B., Oluwadebi, A.G., Ajilore, O.H. and Malaka, L.E., 2021. The Impact of COVID-19 on the Academic Performance of Students: A Psychosocial Study Using Association and Regression Model.