Educational Data Mining on Student Academic Performance Prediction: A Survey
Educational Data Mining Pada Prediksi Kinerja Akademik Mahasiswa : Sebuah Survey
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(1) * Uce Indahyanti
Universitas Muhammadiyah Sidoarjo
Indonesia
(2) Nuril Lutvi Azizah Universitas Muhammadiyah Sidoarjo
Indonesia
(3) Hamzah Setiawan Universitas Muhammadiyah Sidoarjo
Indonesia
(*) Corresponding Author
Abstract
Student academic performance prediction has become a hot research topic, and is still a research trend in the field of educational data mining (EDM). The application of data mining in the education domain can find some hidden knowledge and patterns, which help in decision making for management to improve the education system. This study presents survey results in the form of systematic mapping of literature related to EDM, which aims to identify methods, datasets, and results obtained by researchers in the last five years.
References
[1] S. M. S. Samuel-Soma M. Ajibade, Nor Bahiah Ahmad, A Data Mining Approach to Predict Academic Performance of Students Using Ensemble Techniques. Springer International Publishing, 2020.
[2] M. N. Injadat, A. Moubayed, A. B. Nassif, and A. Shami, “Systematic ensemble model selection approach for educational data mining,” Knowledge-Based Syst., vol. 200, p. 105992, 2020, doi: 10.1016/j.knosys.2020.105992.
[3] M. Riestra-González, M. del P. Paule-Ruíz, and F. Ortin, “Massive LMS log data analysis for the early prediction of course-agnostic student performance,” Comput. Educ., vol. 163, no. December 2020, 2021, doi: 10.1016/j.compedu.2020.104108.
[4] D. Monllaó Olivé, D. Q. Huynh, M. Reynolds, M. Dougiamas, and D. Wiese, “A supervised learning framework: using assessment to identify students at risk of dropping out of a MOOC,” J. Comput. High. Educ., vol. 32, no. 1, pp. 9–26, 2020, doi: 10.1007/s12528-019-09230-1.
[5] J. López-Zambrano, J. A. Lara, and C. Romero, “Towards portability of models for predicting students’ final performance in university courses starting from moodle logs,” Appl. Sci., vol. 10, no. 1, 2020, doi: 10.3390/app10010354.
[6] G. S. Suwardika and I. K. P. Suniantara, “Analisis Random Forest Pada Klasifikasi Cart Ketidaktepatan Waktu Kelulusan Mahasiswa Universitas Terbuka,” BAREKENG J. Ilmu Mat. dan Terap., vol. 13, no. 3, pp. 177–184, 2019, doi: 10.30598/barekengvol13iss3pp177-184ar910.
[7] X. Du, J. Yang, J. L. Hung, and B. Shelton, “Educational data mining: a systematic review of research and emerging trends,” Inf. Discov. Deliv., vol. 48, no. 4, pp. 225–236, 2020, doi: 10.1108/IDD-09-2019-0070.
[8] N. Kondo, M. Okubo, and T. Hatanaka, “Early Detection of At-Risk Students Using Machine Learning Based on LMS Log Data,” Proc. - 2017 6th IIAI Int. Congr. Adv. Appl. Informatics, IIAI-AAI 2017, pp. 198–201, 2017, doi: 10.1109/IIAI-AAI.2017.51.
[9] R. Conijn, C. Snijders, A. Kleingeld, and U. Matzat, “Predicting student performance from LMS data: A comparison of 17 blended courses using moodle LMS,” IEEE Trans. Learn. Technol., vol. 10, no. 1, pp. 17–29, 2017, doi: 10.1109/TLT.2016.2616312.
[10] E. Digna S, “LEARNING MANAGEMENT SYSTEM WITH PREDICTION MODEL AND COURSE-CONTENT RECOMMENDATION MODULE,” Learn. Manag. Syst. WITH Predict. Model COURSE-CONTENT Recomm. Modul., vol. 16, no. 1, pp. 437–457, 2017, doi: https://doi.org/10.28945/3883.
[11] B. S. Amandeep Kaur, Nitin Umesh, “Machine Learning Approach to Predict Student Academic Performance Amandeep,” Int. J. Res. Appl. Sci. Eng. Technol., 2018, doi: http://doi.org/10.22214/ijraset.2018.4125.
[12] L. H. Son and H. Fujita, “Neural-fuzzy with representative sets for prediction of student performance,” Appl. Intell., vol. 49, no. 1, pp. 172–187, 2019, doi: 10.1007/s10489-018-1262-7.
[13] H. Hassan, S. Anuar, and N. B. Ahmad, Students’ performance prediction model using meta-classifier approach, vol. 1000. Springer International Publishing, 2019.
[14] N. Benediktus and R. S. Oetama, “Algoritma Klasifikasi Decision Tree C5 . 0 untuk Memprediksi Performa Akademik Siswa,” Ultimatics, vol. XII, no. 1, pp. 14–19, 2020.
[15] A. S. Hashim, W. A. Awadh, and A. K. Hamoud, “Student Performance Prediction Model based on Supervised Machine Learning Algorithms,” IOP Conf. Ser. Mater. Sci. Eng., vol. 928, no. 3, 2020, doi: 10.1088/1757-899X/928/3/032019.
[16] Á. C. Hidalgo, P. M. Ger, and L. D. L. F. Valentín, “Using Meta-Learning to predict student performance in virtual learning environments,” Appl. Intell., vol. 52, no. 3, pp. 3352–3365, 2022, doi: 10.1007/s10489-021-02613-x.
[17] A. A. Mubarak, H. Cao, I. M. Hezam, and F. Hao, “Modeling students’ performance using graph convolutional networks,” Complex Intell. Syst., 2022, doi: 10.1007/s40747-022-00647-3.
[18] Y. Abubakar, N. Bahiah, and H. Ahmad, “Prediction of Students’ Performance in E-Learning Environment Using Random Forest,” Int. J. Innov. Comput., vol. 7, no. 2, pp. 1–5, 2017, [Online]. Available: http://se.fsksm.utm.my/ijic/index.php/ijic.
[19] A. Salini, U. Jeyapriya, S. M. College, and S. M. College, “A Majority Vote Based Ensemble Classifier for Predicting Students Academic Performance,” Int. J. Pure Appl. Math., vol. 118, no. 24, pp. 1–11, 2018.
[20] P. Kumari, P. K. Jain, and R. Pamula, “An efficient use of ensemble methods to predict students academic performance,” Proc. 4th IEEE Int. Conf. Recent Adv. Inf. Technol. RAIT 2018, pp. 1–6, 2018, doi: 10.1109/RAIT.2018.8389056.
[21] A. R. Arrahimi, M. K. Ihsan, D. Kartini, M. R. Faisal, and F. Indriani, “Teknik Bagging Dan Boosting Pada Algoritma CART Untuk Klasifikasi Masa Studi Mahasiswa,” J. Sains dan Inform., vol. 5, no. 1, pp. 21–30, 2019, doi: 10.34128/jsi.v5i1.171.
[22] B. K. Verma and H. K. Singh, “Prediction of Students ’ Performance in e - Learning Environment using Data Mining / Machine Learning Techniques,” vol. 23, no. 5, pp. 586–593, 2021.
[23] A. Asselman, M. Khaldi, and S. Aammou, “Enhancing the prediction of student performance based on the machine learning XGBoost algorithm,” Interact. Learn. Environ., vol. 0, no. 0, pp. 1–20, 2021, doi: 10.1080/10494820.2021.1928235.
[24] S. S. Shreem, H. Turabieh, S. Al Azwari, and F. Baothman, “Enhanced binary genetic algorithm as a feature selection to predict student performance,” Soft Comput., vol. 26, no. 4, pp. 1811–1823, 2022, doi: 10.1007/s00500-021-06424-7.
[2] M. N. Injadat, A. Moubayed, A. B. Nassif, and A. Shami, “Systematic ensemble model selection approach for educational data mining,” Knowledge-Based Syst., vol. 200, p. 105992, 2020, doi: 10.1016/j.knosys.2020.105992.
[3] M. Riestra-González, M. del P. Paule-Ruíz, and F. Ortin, “Massive LMS log data analysis for the early prediction of course-agnostic student performance,” Comput. Educ., vol. 163, no. December 2020, 2021, doi: 10.1016/j.compedu.2020.104108.
[4] D. Monllaó Olivé, D. Q. Huynh, M. Reynolds, M. Dougiamas, and D. Wiese, “A supervised learning framework: using assessment to identify students at risk of dropping out of a MOOC,” J. Comput. High. Educ., vol. 32, no. 1, pp. 9–26, 2020, doi: 10.1007/s12528-019-09230-1.
[5] J. López-Zambrano, J. A. Lara, and C. Romero, “Towards portability of models for predicting students’ final performance in university courses starting from moodle logs,” Appl. Sci., vol. 10, no. 1, 2020, doi: 10.3390/app10010354.
[6] G. S. Suwardika and I. K. P. Suniantara, “Analisis Random Forest Pada Klasifikasi Cart Ketidaktepatan Waktu Kelulusan Mahasiswa Universitas Terbuka,” BAREKENG J. Ilmu Mat. dan Terap., vol. 13, no. 3, pp. 177–184, 2019, doi: 10.30598/barekengvol13iss3pp177-184ar910.
[7] X. Du, J. Yang, J. L. Hung, and B. Shelton, “Educational data mining: a systematic review of research and emerging trends,” Inf. Discov. Deliv., vol. 48, no. 4, pp. 225–236, 2020, doi: 10.1108/IDD-09-2019-0070.
[8] N. Kondo, M. Okubo, and T. Hatanaka, “Early Detection of At-Risk Students Using Machine Learning Based on LMS Log Data,” Proc. - 2017 6th IIAI Int. Congr. Adv. Appl. Informatics, IIAI-AAI 2017, pp. 198–201, 2017, doi: 10.1109/IIAI-AAI.2017.51.
[9] R. Conijn, C. Snijders, A. Kleingeld, and U. Matzat, “Predicting student performance from LMS data: A comparison of 17 blended courses using moodle LMS,” IEEE Trans. Learn. Technol., vol. 10, no. 1, pp. 17–29, 2017, doi: 10.1109/TLT.2016.2616312.
[10] E. Digna S, “LEARNING MANAGEMENT SYSTEM WITH PREDICTION MODEL AND COURSE-CONTENT RECOMMENDATION MODULE,” Learn. Manag. Syst. WITH Predict. Model COURSE-CONTENT Recomm. Modul., vol. 16, no. 1, pp. 437–457, 2017, doi: https://doi.org/10.28945/3883.
[11] B. S. Amandeep Kaur, Nitin Umesh, “Machine Learning Approach to Predict Student Academic Performance Amandeep,” Int. J. Res. Appl. Sci. Eng. Technol., 2018, doi: http://doi.org/10.22214/ijraset.2018.4125.
[12] L. H. Son and H. Fujita, “Neural-fuzzy with representative sets for prediction of student performance,” Appl. Intell., vol. 49, no. 1, pp. 172–187, 2019, doi: 10.1007/s10489-018-1262-7.
[13] H. Hassan, S. Anuar, and N. B. Ahmad, Students’ performance prediction model using meta-classifier approach, vol. 1000. Springer International Publishing, 2019.
[14] N. Benediktus and R. S. Oetama, “Algoritma Klasifikasi Decision Tree C5 . 0 untuk Memprediksi Performa Akademik Siswa,” Ultimatics, vol. XII, no. 1, pp. 14–19, 2020.
[15] A. S. Hashim, W. A. Awadh, and A. K. Hamoud, “Student Performance Prediction Model based on Supervised Machine Learning Algorithms,” IOP Conf. Ser. Mater. Sci. Eng., vol. 928, no. 3, 2020, doi: 10.1088/1757-899X/928/3/032019.
[16] Á. C. Hidalgo, P. M. Ger, and L. D. L. F. Valentín, “Using Meta-Learning to predict student performance in virtual learning environments,” Appl. Intell., vol. 52, no. 3, pp. 3352–3365, 2022, doi: 10.1007/s10489-021-02613-x.
[17] A. A. Mubarak, H. Cao, I. M. Hezam, and F. Hao, “Modeling students’ performance using graph convolutional networks,” Complex Intell. Syst., 2022, doi: 10.1007/s40747-022-00647-3.
[18] Y. Abubakar, N. Bahiah, and H. Ahmad, “Prediction of Students’ Performance in E-Learning Environment Using Random Forest,” Int. J. Innov. Comput., vol. 7, no. 2, pp. 1–5, 2017, [Online]. Available: http://se.fsksm.utm.my/ijic/index.php/ijic.
[19] A. Salini, U. Jeyapriya, S. M. College, and S. M. College, “A Majority Vote Based Ensemble Classifier for Predicting Students Academic Performance,” Int. J. Pure Appl. Math., vol. 118, no. 24, pp. 1–11, 2018.
[20] P. Kumari, P. K. Jain, and R. Pamula, “An efficient use of ensemble methods to predict students academic performance,” Proc. 4th IEEE Int. Conf. Recent Adv. Inf. Technol. RAIT 2018, pp. 1–6, 2018, doi: 10.1109/RAIT.2018.8389056.
[21] A. R. Arrahimi, M. K. Ihsan, D. Kartini, M. R. Faisal, and F. Indriani, “Teknik Bagging Dan Boosting Pada Algoritma CART Untuk Klasifikasi Masa Studi Mahasiswa,” J. Sains dan Inform., vol. 5, no. 1, pp. 21–30, 2019, doi: 10.34128/jsi.v5i1.171.
[22] B. K. Verma and H. K. Singh, “Prediction of Students ’ Performance in e - Learning Environment using Data Mining / Machine Learning Techniques,” vol. 23, no. 5, pp. 586–593, 2021.
[23] A. Asselman, M. Khaldi, and S. Aammou, “Enhancing the prediction of student performance based on the machine learning XGBoost algorithm,” Interact. Learn. Environ., vol. 0, no. 0, pp. 1–20, 2021, doi: 10.1080/10494820.2021.1928235.
[24] S. S. Shreem, H. Turabieh, S. Al Azwari, and F. Baothman, “Enhanced binary genetic algorithm as a feature selection to predict student performance,” Soft Comput., vol. 26, no. 4, pp. 1811–1823, 2022, doi: 10.1007/s00500-021-06424-7.
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Published
2022-07-18
How to Cite
Indahyanti, U., Azizah, N. L., & Setiawan, H. (2022). Educational Data Mining on Student Academic Performance Prediction: A Survey: Educational Data Mining Pada Prediksi Kinerja Akademik Mahasiswa : Sebuah Survey. Procedia of Social Sciences and Humanities, 3, 1442-1447. https://doi.org/10.21070/pssh.v3i.344
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Articles
Copyright (c) 2022 Uce Indahyanti, Nuril Lutvi Azizah, Hamzah Setiawan
This work is licensed under a Creative Commons Attribution 4.0 International License.
