Student performance classification and prediction in fully online environment using Decision tree
Main Article Content
Abstract
Knowledge Discovery and Data Mining (KDD) is a multidisciplinary field of study that focuses on methodologies for extracting useful knowledge from data. During the latest Covid-19 pandemic, there was a significant uptick in online-based learning (e-learning) operations as every educational institution moved its operations to digital channels. To increase the quality of education in this new normal, it is necessary to determine the key factors in students’ performance. The main objective of this study is to exploit the regulating factors of education via digital platforms during the covid-19 pandemic by extracting knowledge and a set of rules by using the Decision Tree (j48) classifier. In this study, we developed a conceptual framework using four datasets, each with a different set of attributes and instances, collected from “X-University” and Microsoft teams. ‘Final term’ and ‘Mid-term’ examinations acted as the root node for all four datasets. The findings of this study would benefit higher education institutions by helping instructors and students to recognize the shortcomings and influences controlling students' performance in the online platforms during the covid-19 pandemic, as well as serve as an early warning framework for predicting students' deficiencies and low school performance.
Article Details
How to Cite
[1]
M. Al Karim, M. Y. Ara, M. M. Masnad, M. Rasel, and D. Nandi, “Student performance classification and prediction in fully online environment using Decision tree”, AJSE, vol. 20, no. 3, pp. 70 - 76, Sep. 2021.
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AJSE contents are under the terms of the Creative Commons Attribution License. This permits anyone to copy, distribute, transmit and adapt the worknon-commercially provided the original work and source is appropriately cited.
References
REFERENCES
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[22] Yassein, N. A., M Helali, R. G., & Mohomad, S. B. (2017). Predicting Student Academic Performance in KSA using Data Mining Techniques. Journal of Information Technology & Software Engineering, 07(05). https://doi.org/10.4172/2165-7866.1000213.
[23] Patil, R., & Tamane, S. (2018). A comparative analysis on the evaluation of classification algorithms in the prediction of diabetes. International Journal of Electrical and Computer Engineering, 8(5), 3966–3975. https://doi.org/10.11591/ijece.v8i5.pp3966-3975.
[24] Hooshyar, D., Pedaste, M., & Yang, Y. (2020). Mining educational data to predict students’ performance through procrastination behavior. Entropy, 22(1), 12. https://doi.org/10.3390/e22010012.
[25] Nandi, D., Hamilton, M., Harland, J., & Mahmood, S. (2015). Investigation of Participation and Quality of Online Interaction. International Journal of Modern Education and Computer Science, 7(8), 25–37. https://doi.org/10.5815/ijmecs.2015.08.04.
[26] Teräs, M., Suoranta, J., Teräs, H., & Curcher, M. (2020). Post-Covid-19 Education and Education Technology ‘Solutionism’: a Seller’s Market. Postdigital Science and Education, 2(3), 863–878. https://doi.org/10.1007/s42438-020-00164-x.
[27] Abdalla, Jihan. “US universities switch to online courses due to coronavirus”, AL JAZEERA, 10 Mar 2020.
[28] Li,Cathy ; Lalani, Farah. “The COVID-19 pandemic has changed education forever. This is how”, WORLD ECONOMIC FORUM, 29 Apr 2020.
[29] Han, Jiawei, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques, Third Edition. 3rd ed. Waltham, Mass.: Morgan Kaufmann Publishers, 2012.
[30] Baker, R. S., & Yacef, K. (2009). The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1(1), 3-17. https://doi.org/10.5281/zenodo.3554657.
[1] Khan, A., & Ghosh, S. K. (2018). Data mining based analysis to explore the effect. Springer.
[2] Angeline, D. M. D. (2013). Association Rule Generation for Student Performance Analysis using Apriori Algorithm. The SIJ Transactions on Computer Science Engineering & Its Applications (CSEA), 01(01), 16–20. https://doi.org/10.9756/sijcsea/v1i1/01010252.
[3] Pandey, M., & Kumar Sharma, V. (2013). A Decision Tree Algorithm Pertaining to the Student Performance Analysis and Prediction. International Journal of Computer Applications, 61(13), 1–5. https://doi.org/10.5120/9985-4822.
[4] Angeline, D. M. D. (2013). Association Rule Generation for Student Performance Analysis using Apriori Algorithm. The SIJ Transactions on Computer Science Engineering & Its Applications (CSEA), 01(01), 16–20. https://doi.org/10.9756/sijcsea/v1i1/01010252.
[5] Raut, A. B., & Nichat, A. A. (2017). Students Performance Prediction Using Decision Tree Technique. International Journal of Computational Intelligence Research, 13(7), 1735–1741. Retrieved from http://www.ripublication.com.
[6] Badr El Din Ahmed, A., & Sayed Elaraby, I. (2014). PER: A prediction for Student’s Performance Using Decision Tree ID3 Method. India - World Journal of Computer Application and Technology, 2(2), 43–47. Retrieved from http://www.hrpub.org
[7] Al-Barrak, M. A., & Al-Razgan, M. (2016). Predicting Students Final GPA Using Decision Trees: A Case Study. International Journal of Information and Education Technology, 6(7), 528–533. https://doi.org/10.7763/ijiet.2016.v6.745.
[8] Long, X., & Wu, Y. (2012). Application of decision tree in student achievement evaluation. Proceedings - 2012 International Conference on Computer Science and Electronics Engineering, ICCSEE 2012, 2, 243–247. https://doi.org/10.1109/ICCSEE.2012.169.
[9] Long, X., & Wu, Y. (2012). Application of decision tree in student achievement evaluation. Proceedings - 2012 International Conference on Computer Science and Electronics Engineering, ICCSEE 2012, 2, 243–247. https://doi.org/10.1109/ICCSEE.2012.169.
[10] Lakshmi, T. M., Martin, A., Begum, R. M., & Venkatesan, V. P. (2013). An Analysis on Performance of Decision Tree Algorithms using Student’s Qualitative Data. International Journal of Modern Education and Computer Science, 5(5), 18–27. https://doi.org/10.5815/ijmecs.2013.05.03.
[11] Kumar, B., & Pal, S. (2011). Mining Educational Data to Analyze Students Performance. International Journal of Advanced Computer Science and Applications, 2(6). https://doi.org/10.14569/ijacsa.2011.020609.
[12] Saa, A. A., Al-Emran, M., & Shaalan, K. (2020). Mining Student Information System Records to Predict Students’ Academic Performance. Advances in Intelligent Systems and Computing (Vol. 921). Springer International Publishing. https://doi.org/10.1007/978-3-030-14118-9_23.
[13] Ramaswami, M., & Bhaskaran, R. (2010). A CHAID Based Performance Prediction Model in Educational Data Mining, 7(1), 10–18. Retrieved from http://arxiv.org/abs/1002.1144.
[14] Shahiri, A. M., Husain, W., & Rashid, N. A. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157.
[15] Suljic, M. (2012). Data Mining Approach for Predicting Student Performance. Economic Review: Journal of Economics and Business, X (1), 3–12.
[16] Pisani, S., Fioriti, D., Conte, M. P., Chiarini, F., Seganti, L., & Degener, A. M. (2002). Involvement of herpes simplex virus type 2 in modulation of gene expression of human papillomavirus type 18. International Journal of Immunopathology and Pharmacology, 15(1), 59–63. https://doi.org/10.1177/039463200201500108.
[17] Kabakchieva, D. (2013). Predicting student performance by using data mining methods for classification. Cybernetics and Information Technologies, 13(1), 61–72. https://doi.org/10.2478/cait-2013-0006.
[18] Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques. International Journal of Modern Education and Computer Science, 8(11), 36–42. https://doi.org/10.5815/ijmecs.2016.11.05.
[19] Hasan, R., Palaniappan, S., Raziff, A. R. A., Mahmood, S., & Sarker, K. U. (2018). Student Academic Performance Prediction by using Decision Tree Algorithm. 2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS 2018 - Proceedings, 1–5. https://doi.org/10.1109/ICCOINS.2018.8510600.
[20] Abu, A. (2016). Educational Data Mining & Students’ Performance Prediction. International Journal of Advanced Computer Science and Applications,7(5),212–220. https://doi.org/10.14569/ijacsa.2016.070531.
[21] Salal, Y. K., Abdullaev, S. M., & Kumar, M. (2019). Educational data mining: Student performance prediction in academic. International Journal of Engineering and Advanced Technology, 8(4C), 54–59.
[22] Yassein, N. A., M Helali, R. G., & Mohomad, S. B. (2017). Predicting Student Academic Performance in KSA using Data Mining Techniques. Journal of Information Technology & Software Engineering, 07(05). https://doi.org/10.4172/2165-7866.1000213.
[23] Patil, R., & Tamane, S. (2018). A comparative analysis on the evaluation of classification algorithms in the prediction of diabetes. International Journal of Electrical and Computer Engineering, 8(5), 3966–3975. https://doi.org/10.11591/ijece.v8i5.pp3966-3975.
[24] Hooshyar, D., Pedaste, M., & Yang, Y. (2020). Mining educational data to predict students’ performance through procrastination behavior. Entropy, 22(1), 12. https://doi.org/10.3390/e22010012.
[25] Nandi, D., Hamilton, M., Harland, J., & Mahmood, S. (2015). Investigation of Participation and Quality of Online Interaction. International Journal of Modern Education and Computer Science, 7(8), 25–37. https://doi.org/10.5815/ijmecs.2015.08.04.
[26] Teräs, M., Suoranta, J., Teräs, H., & Curcher, M. (2020). Post-Covid-19 Education and Education Technology ‘Solutionism’: a Seller’s Market. Postdigital Science and Education, 2(3), 863–878. https://doi.org/10.1007/s42438-020-00164-x.
[27] Abdalla, Jihan. “US universities switch to online courses due to coronavirus”, AL JAZEERA, 10 Mar 2020.
[28] Li,Cathy ; Lalani, Farah. “The COVID-19 pandemic has changed education forever. This is how”, WORLD ECONOMIC FORUM, 29 Apr 2020.
[29] Han, Jiawei, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques, Third Edition. 3rd ed. Waltham, Mass.: Morgan Kaufmann Publishers, 2012.
[30] Baker, R. S., & Yacef, K. (2009). The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1(1), 3-17. https://doi.org/10.5281/zenodo.3554657.