[1] X. Chen, D. Zou, H. Xie, G. Cheng, and C. Liu, “Two decades of artificial intelligence in education,” Educ. Technol. Soc., vol. 25, no. 1, pp. 28-47, 2022.
[2] B. Albreiki, N. Zaki, and H. Alashwal, “A systematic literature review of student' performance prediction using machine learning techniques,” Educ. Sci., vol. 11, no. 9, p. 552, 2021.
[3] E. Alyahyan and D. Dustegor, “Predicting academic success in higher education: Literature review and best practices,” Int. J. Educ. Technol. High. Educ., vol. 17, pp. 1-21, 2020.
[4] S. Hussain and M.Q. Khan, “Student-performulator: Predicting students' academic performance at secondary and intermediate level using machine learning,” Ann. Data Sci., vol. 10, no. 3, pp. 637-655, 2023.
[5] X. Bai, F. Zhang, J. Li, T. Guo, A. Aziz, A. Jin, and F. Xia, “Educational big data: Predictions, applications and challenges,” Big Data Res., vol. 26, p. 100270, 2021.
[6] C. Huang, J. Zhou, J. Chen, J. Yang, K. Clawson, and Y. Peng, “A feature weighted support vector machine and artificial neural network algorithm for academic course performance prediction,” Neural Comput. Appl., vol. 35, no. 16, pp. 11517-11529, 2023.
[7] A. Rivas, A. Gonzalez-Briones, G. Hernandez, J. Prieto, and P. Chamoso, “Artificial neural network analysis of the academic performance of students in virtual learning environments,” Neurocomputing, vol. 423, pp. 713-720, 2021.
[8] H. Zeineddine, U. Braendle, and A. Farah, “Enhancing prediction of student success: Automated machine learning approach,” Comput. Electr. Eng., vol. 89, p. 106903, 2021.
[9] R. Campagni, D. Merlini, R. Sprugnoli, and M.C. Verri, “Data mining models for student careers,” Expert Syst. Appl., vol. 42, no. 13, pp. 5508-5521, 2015.
[10] S. Natek and M. Zwilling, “Student data mining solution-knowledge management system related to higher education institutions,” Expert Syst. Appl., vol. 41, no. 14, pp. 6400-6407, 2014.
[11] A.M. Shahiri, W. Husain, and N.A. Rashid, “A review on predicting student's performance using data mining techniques,” Procedia Comput. Sci., vol. 72, pp. 414-422, 2015.
[12] J. Berens, S. Oster, K. Schneider, and J. Burghoff, “Early detection of students at risk - Predicting student dropouts using administrative student data and machine learning methods,” Schumpeter Sch. Bus. Econ., pp. 0-32, 2018.
[13] S. Helal, J. Li, L. Liu, E. Ebrahimie, S. Dawson, and D.J. Murray, “Identifying key factors of student academic performance by subgroup discovery,” Int. J. Data Sci. Anal., vol. 7, no. 3, pp. 227-245, 2019.
[14] G.W. Dekker, M. Pechenizkiy, and J.M. Vleeshouwers, “Predicting students drop out: A case study,” Proc. 2nd Int. Conf. Educ. Data Mining, (EDM), 2009, Cordoba, Spain, 2009, pp. 41-50.
[15] A. Abu, S. Mostafa, A. Emran, and K. Shaalan, Factors Affecting Students' Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques, Springer Netherlands, 2019.
[16] S. Kotsiantis, C. Pierrakeas, and P. Pintelas, “Predicting students’ performance in distance learning using machine learning techniques,” Appl. Artif. Intell., vol. 18, pp. 411-426, 2004, doi: 10.1080/08839510490442058.
[17] J. Navamani and A. Kannammal, “Predicting performance of schools by applying data mining techniques on public examination results,” Res. J. Appl. Sci. Eng. Technol., vol. 9, pp. 262-271, 2015.
[18] L. Moseley and D. Mead, “Predicting who will drop out of nursing courses: A machine learning exercise,” Nurse Educ. Today, vol. 28, pp. 469-475, 2008, doi: 10.1016/j.nedt.2007.07.012.
[19] A. Nandeshwar, T. Menzies, and A. Nelson, “Learning patterns of university student retention,” Expert Syst. Appl., vol. 38, pp. 14984-14996, 2011, doi: 10.1016/j.eswa.2011.05.048.
[20] D. Thammasiri, D. Delen, P. Meesad, and N. Kasap, “A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition,” Expert Syst. Appl., vol. 41, pp. 321-330, 2014, doi: 10.1016/j.eswa.2013.07.046.
[21] M. Dewan, F. Lin, D. Wen, and Kinshuk, “Predicting dropout-prone students in e-learning education system,” in Proc. 2015 IEEE 12th Int. Conf. Ubiquitous Intell. Comput. (UIC) and Related Conferences, Beijing, China, 2016, pp. 1735-1740, doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP.2015.315.
[22] M. Tan and P. Shao, “Prediction of student dropout in E-learning program through the use of machine learning method,” Int. J. Emerg. Technol. Learn., vol. 10, pp. 11-17, 2015, doi:10.3991/ijet.v10i1.4189.
[23] S. Sultana, S. Khan, and M. Abbas, “Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts,” Int. J. Electr. Eng. Educ., vol. 54, pp. 105-118, 2017, doi: 10.1177/0020720916688484.
[24] Y. Chen, A. Johri, and H. Rangwala, “Running out of STEM: A comparative study across STEM majors of college students At-Risk of dropping out early,” in Proc. 8th Int. Conf. Learn. Analytics Knowl., Sydney, Australia, 2018, pp. 270-279.
[25] M. Nagy and R. Molontay, “Predicting dropout in higher education based on secondary school performance,” in Proc. 2018 IEEE 22nd Int. Conf. Intell. Eng. Syst. (INES), Las Palmas de Gran Canaria, Spain, 2018, pp. 000389-000394, doi: 10.1109/INES.2018.8523888.
[26] A. Serra, P. Perchinunno, and M. Bilancia, “Predicting student dropouts in higher education using supervised classification algorithms,” Lect. Notes Comput. Sci., vol. 10962, pp. 18-33, 2018, doi: 10.1007/978-3-319-95168-3_2.
[27] C. Gray and D. Perkins, “Utilizing early engagement and machine learning to predict student outcomes,” Comput. Educ., vol. 131, pp. 22-32, 2019, doi: 10.1016/j.compedu.2018.12.006.
[28] J. Chung and S. Lee, “Dropout early warning systems for high school students using machine learning,” Child. Youth Serv. Rev., vol. 96, pp. 346-353, 2019, doi: 10.1016/j.childyouth.2018.11.030.
[29] M. Hussain, W. Zhu, W. Zhang, S. Abidi, and S. Ali, “Using machine learning to predict student difficulties from learning session data,” Artif. Intell. Rev., vol. 52, pp. 1-27, 2018, doi: 10.1007/s10462-018-9620-8.
[30] S. Huang and N. Fang, “Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive mathematical models,” Comput. Educ., vol. 61, pp. 133-145, 2013, doi: 10.1016/j.compedu.2012.08.015. 
[31] A. Slim, G.L. Heileman, J. Kozlick, and C.T. Abdallah, “Predicting student success based on prior performance,” in Proc. 2014 IEEE Symp. Comput. Intell. Data Min. (CIDM), Singapore, 2015, pp. 410-415, doi: 10.1109/CIDM.2014.7008697.
[32] C. Zhao, J. Yang, J. Liang, and C. Li, “Discover learning behavior patterns to predict certification,” in Proc. 2016 11th Int. Conf. Comput. Sci. Educ. (ICCSE), Nagoya, Japan, 2016, pp. 69-73, doi: 10.1109/ICCSE.2016.7581557.
[33] P. Chaudhury, S. Mishra, H. Tripathy, and B. Kishore, “Enhancing the capabilities of student result prediction system,” in Proc. 2nd Int. Conf. Inf. Commun. Technol. Competitive Strategies, Udaipur, India, 2016, doi: 10.1145/2905055.2905150.
[34] C. Nespereira, E. Elhariri, N. El‑Bendary, A. Vilas, and R. Redondo, “Machine learning based classification approach for predicting students performance in blended learning,” Adv. Intell. Syst. Comput., vol. 407, pp. 47-56, 2016, doi: 10.1007/978-3-319-26690-9_5.
[35] M. Sagar, A. Gupta, and R. Kaushal, “Performance prediction and behavioral analysis of student programming ability,” in Proc. 2016 Int. Conf. Adv. Comput., Commun. Inf. (ICACCI), Jaipur, India, 2016, pp. 1039-1045, doi: 10.1109/ICACCI.2016.7732181.
[36] V. Verhun, A. Batyuk, and V. Voityshyn, “Learning analysis as a tool for predicting student performance,” in Proc. IEEE 13th Int. Sci. Tech. Conf. Comput. Sci. Inf. Technol. (CSIT), 2018, vol. 2, pp. 76-79, doi: 10.1109/STC-CSIT.2018.8526741.
[37] M. Backenkohler and V. Wolf, “Student performance prediction and optimal course selection: An MDP approach,” Lect. Notes Comput. Sci., vol. 10729, pp. 40-47, 2018, doi: 10.1007/978-3-319-74781-1_3.
[38] Y.Z. Hsieh, M.C. Su, and Y.L. Jeng, “The jacobian matrix-based learning machine in student,” Lect. Notes Comput. Sci., vol. 10676, pp. 469-474, 2017, doi: 10.1007/978-3-319-71084-6_55.
[39] M. Han, M. Tong, M. Chen, J. Liu, and C. Liu, “Application of ensemble algorithm in students’ performance prediction,” in Proc. 6th IIAI Int. Congr. Adv. Appl. Inf. (IIAI‑AAI), Hamamatsu, Japan, 2017, pp. 735-740, doi: 10.1109/IIAI-AAI.2017.73.
[40] A. Shanthini, G. Vinodhini, and R. Chandrasekaran, “Predicting students’ academic performance in the university using meta decision tree classifiers,” J. Comput. Sci., vol. 14, pp. 654-662, 2018, doi: 10.3844/jcssp.2018.654.662.
[41] C. Ma, B. Yao, F. Ge, Y. Pan, and Y. Guo, “Improving prediction of student performance based on multiple feature selection approaches,” in Proc. ICEBT 2017, Toronto, ON, Canada, 2017, pp. 36-41, doi: 10.1145/3141151.3141160.
[42] A. Tekin, “Early prediction of students’ grade point averages at graduation: A data mining approach [Ogrencinin mezuniyet notunun erken tahmini: Bir veri madenciligi yaklasımı],” Eurasian J. Educ. Res., no. 54, pp. 207-226, 2014, doi: 10.14689/ejer.2014.54.12.
[43] S. Pushpa, T. Manjunath, T. Mrunal, A. Singh, and C. Suhas, “Class result prediction using machine learning,” in Proc. 2017 Int. Conf. Smart Technol. Smart Nation (SmartTechCon), Bengaluru, India, 2018, pp. 1208-1212, doi: 10.1109/SmartTechCon.2017.8358559.
[44] E. Howard, M. Meehan, and A. Parnell, “Contrasting prediction methods for early warning systems at undergraduate level,” Internet High. Educ., vol. 37, pp. 66-75, 2018, doi: 10.1016/j.iheduc.2018.02.001.
[45] C. Villagra-Arnedo, F. Gallego-Duran, P. Compan-Rosique, F. Llorens-Largo, and R. Molina-Carmona, “Predicting academic performance from Behavioural and learning data,” Int. J. Des. Nat. Ecodyn., vol. 11, no. 3, pp. 239-249, 2016, doi: 10.2495/DNE-V11-N3-239-249.
[46] S. Sorour, K. Goda, and T. Mine, “Estimation of Student Performance by Considering Consecutive Lessons,” in Proc. 4th Int. Congr. Adv. Appl. Informat., Okayama, Japan, 2016, pp. 121-126, doi: 10.1109/IIAI-AAI.2015.170.
[47] B. Guo, R. Zhang, G. Xu, C. Shi, and L. Yang, “Predicting Students Performance in Educational Data Mining,” in Proc. 2015 Int. Symp. Educ. Technol. (ISET), Wuhan, China, 2016, pp. 125-128, doi: 10.1109/ISET.2015.33.
[48] S. Rana and R. Garg, “Prediction of students performance of an institute using ClassificationViaClustering and ClassificationViaRegression,” Adv. Intell. Syst. Comput., vol. 508, pp. 333-343, 2017, doi: 10.1007/978-981-10-2750-5_35.
[49] V.K. Anand, S.K. Abdul Rahiman, E. Ben George, and A.S. Huda, “Recursive clustering technique for students’ performance evaluation in programming courses,” in Proc. 2018 Majan Int. Conf. (MIC), Muscat, Oman, 2018, pp. 1-5, doi: 10.1109/MINTC.2018.8363153.
[50] H. Bydzovska, “Student performance prediction using collaborative filtering methods,” Lect. Notes Comput. Sci., vol. 9112, pp. 550-553, 2015, doi: 10.1007/978-3-319-19773-9_59.
[51] H. Bydzovska, “Are collaborative filtering methods suitable for student performance prediction?” Lect. Notes Comput. Sci., vol. 9273, pp. 425-430, 2015, doi: 10.1007/978-3-319-23485-4_42.
[52] Y. Park, “Predicting personalized student performance in computing-related majors via collaborative filtering,” in Proc. 19th Annu. SIG Conf. Inf. Technol. Educ., Fort Lauderdale, FL, USA, 2018, p. 151, doi: 10.1145/3241815.3241875.
[53] C.H. Liou, “Personalized article recommendation based on student’s rating mechanism in an online discussion forum,” in Proc. 2016 49th Hawaii Int. Conf. Syst. Sci. (HICSS), Koloa, HI, USA, 2016, pp. 60-65, doi: 10.1109/HICSS.2016.16.
[54] A. Elbadrawy and G. Karypis, “Domain-aware grade prediction and top-n course recommendation,” in Proc. 10th ACM Conf. Recommender Syst., Boston, MA, USA, 2016, pp. 183-190, doi: 10.1145/2959100.2959133.
[55] V. Pero and T. Horvath, “Comparison of collaborative-filtering techniques for small-scale student performance prediction task,” Lect. Notes Electr. Eng., vol. 313, pp. 111-116, 2015, doi: 10.1007/978-3-319-06773-5_16.
[56] Y. Song, Y. Jin, X. Zheng, H. Han, Y. Zhong, and X. Zhao, “PSFK: A student performance prediction scheme for first-encounter knowledge in ITS,” Lect. Notes Comput. Sci., vol. 9403, pp. 639-650, 2015, doi: 10.1007/978-3-319-25159-2_58.
[57] K. Xu, R. Liu, Y. Sun, K. Zou, Y. Huang, and X. Zhang, “Improve the prediction of student performance with hint’s assistance based on an efficient non-negative factorization,” IEICE Trans. Inf. Syst., vol. E100D, no. 4, pp. 768-775, 2017, doi: 10.1587/transinf.2016DAP0026.
[58] M. Sheehan and Y. Park, “pGPA: A personalized grade prediction tool to aid student success,” in Proc. 6th ACM Conf. Recommender Syst., Dublin, Ireland, 2012, pp. 309-310, doi: 10.1145/2365952.2366027.
[59] S. Lorenzen, N. Pham, and S. Alstrup, “On predicting student performance using low-rank matrix factorization techniques,” in Proc. 9th Eur. Conf. e-Learning (ECEL 2010), Porto, Portugal, 2010, pp. 326-334.
[60] M. Houbraken, C. Sun, E. Smirnov, and K. Driessens, “Discovering hidden course requirements and student competences from grade data,” in Proc. UMAP’17: Adjunct Publ. 25th Conf. User Modeling, Adaptation and Personalization, Bratislava, Slovakia, 2017, pp. 147-152, doi: 10.1145/3099023.3099034.
[61] J. Gomez-Pulido, E. Cortes-Toro, A. Duran-Dominguez, B. Crawford, and R. Soto, “Novel and Classic Metaheuristics for Tunning a Recommender System for Predicting Student Performance in Online Campus,” Lect. Notes Comput. Sci., vol. 11314, pp. 125-133, 2018, doi: 10.1007/978-3-030-03493-1_14.
[62] O. Chavarriaga, B. Florian-Gaviria, and O. Solarte, “A recommender system for students based on social knowledge and assessment data of competences,” Lect. Notes Comput. Sci., vol. 8719, pp. 56-69, 2014, doi: 10.1007/978-3-319-11200-8_5.
[63] E. Jembere, R. Rawatlal, and A. Pillay, “Matrix Factorisation for Predicting Student Performance,” in Proc. 2017 7th World Eng. Educ. Forum (WEEF), Kuala Lumpur, Malaysia, 2018, pp. 513-518, doi: 10.1109/WEEF.2017.8467150.
[64] M. Sweeney, J. Lester, and H. Rangwala, “Next-term student grade prediction,” in Proc. 2015 IEEE Int. Conf. Big Data, Santa Clara, CA, USA, 2015, pp. 970-975, doi: 10.1109/BigData.2015.7363847.
[65] J. Adan-Coello and C. Tobar, “Using collaborative filtering algorithms for predicting student performance,” Lect. Notes Comput. Sci., vol. 9831, pp. 206-218, 2016, doi: 10.1007/978-3-319-44159-7_15.
[66] L. Rechkoski, V. Ajanovski, and M. Mihova, “Evaluation of grade prediction using model-based collaborative filtering methods,” in Proc. 2018 IEEE Global Eng. Educ. Conf. (EDUCON), Santa Cruz de Tenerife, Spain, 2018, pp. 1096-1103, doi: 10.1109/EDUCON.2018.8363352.
[67] M. Adewale Amoo, A. Olumuyiwa, and U. Lateef, “Predictive modelling and analysis of academic performance of secondary school students: Artificial Neural Network approach,” Int. J. Sci. Technol. Educ. Res., vol. 9, pp. 1-8, 2018, doi: 10.5897/IJSTER2017.0415.
[68] T. Gedeon and H. Turner, “Explaining student grades predicted by a neural network,” in Proc. 1993 Int. Conf. Neural Netw., Nagoya, Japan, 1993, vol. 1, pp. 609-612.
[69] P.M. Arsad, N. Buniyamin, and J.A. Manan, “A neural network students’ performance prediction model (NNSPPM),” in Proc. 2013 IEEE Int. Conf. Smart Instrum., Meas. Anal. (ICSIMA), Kuala Lumpur, Malaysia, 2013, pp. 1-5, doi: 10.1109/ICSIMA.2013.6717966.
[70] A. Iyanda, O.D. Ninan, A. Ajayi, and O.G. Anyabolu, “Predicting Student Academic Performance in Computer Science Courses: A Comparison of Neural Network Models,” Int. J. Mod. Educ. Comput. Sci., vol. 10, pp. 1-9, 2018, doi: 10.5815/ijmecs.2018.06.01.
[71] P. Dharmasaroja and N. Kingkaew, “Application of artificial neural networks for prediction of learning performances,” in Proc. 2016 12th Int. Conf. Nat. Comput., Fuzzy Syst. Knowl. Discov. (ICNC-FSKD), Changsha, China, 2016, pp. 745-751, doi: 10.1109/FSKD.2016.7603268.
[72] A. Abdiansah and R. Wardoyo, “Time complexity analysis of support vector machines (SVM) in LibSVM,” Int. J. Comput. Appl., vol. 128, no. 3, pp. 28-34, 2015.
[73] M. Musso, E. Kyndt, E. Cascallar, and F. Dochy, “Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks,” Frontline Learn. Res., vol. 1, pp. 42-71, 2013, doi: 10.14786/flr.v1i1.13.
[74] E. Mala Sari Rochman, A. Rachmad, and F. Damayanti, “Predicting the Final result of Student National Test with Extreme Learning Machine,” Pancar. Pendidik., vol. 7, no. 1, 2018, doi: 10.25037/pancaran.v7i1.159.
[75] W. Villegas-Ch, S. Lujan-Mora, D. Buenano-Fernandez, and M. Roman-Canizares, “Analysis of web-based learning systems by data mining,” in Proc. 2017 IEEE 2nd Ecuador Tech. Chapters Meeting (ETCM), Guayas, Ecuador, 2018, pp. 1-5, doi: 10.1109/ETCM.2017.8247553.
[76] I. Karagiannis and M. Satratzemi, “An adaptive mechanism for Moodle based on automatic detection of learning styles,” Educ. Inf. Technol., vol. 23, pp. 1331-1357, 2018, doi: 10.1007/s10639-017-9663-5.
[77] C. Masci, G. Johnes, and T. Agasisti, “Student and school performance across countries: A machine learning approach,” Eur. J. Oper. Res., vol. 269, pp. 1072-1085, 2018, doi: 10.1016/j.ejor.2018.02.031.
[78] W. Johnson, “Data mining and machine learning in education with focus in undergraduate cs student success,” in Proc. 2018 ACM Conf. Int. Comput. Educ. Res., Espoo, Finland, 2018, pp. 270-271, doi: 10.1145/3230977.3231012.
[79] D. Liu, D. Richards, C. Froissard, and A. Atif, “Validating the effectiveness of the moodle engagement analytics plugin to predict student academic performance,” in Proc. 21st Americas Conf. Inf. Syst. (AMCIS 2015), Fajardo, Puerto Rico, 2015.
[80] D. Delen and H. M. Zolbanin, “The analytics paradigm in business research,” J. Bus. Res., vol. 90, pp. 186-195, 2018.
[81] J. Bao, S. Wei, J. Lv, and W. Zhang, “Optimized faster-RCNN in real-time facial expression classification,” IOP Conf. Ser. Mater. Sci. Eng., vol. 790, no. 1, p. 012148, Mar. 2020.
[82] H.M. Sani, C. Lei, and D. Neagu, “Computational complexity analysis of decision tree algorithms,” in Artificial Intelligence XXXV: Proc. 38th SGAI Int. Conf. Artif. Intell. (AI 2018), Cambridge, UK, 2018, pp. 191-197.
[83] H. Alizadeh, B. Minaei-Bidgoli, and S.K. Amirgholipour, “A New Method for Improving the Performance of K Nearest Neighbor using Clustering Technique,” J. Convergence Inf. Technol., vol. 4, no. 2, pp. 84-92, 2009.
[84] M.M. AlyanNezhadi, M. Hosseini, H. Qazanfari, and A. Kamandi, “Content-based image retrieval using support vector machine and texture difference histogram features,” Soft Comput. J., vol. 11, no. 1, pp. 10-21, 2022, doi: 10.22052/scj.2022.246175.1053 [In Persian].
[85] M. Ebtia, S. M. Hoseini, and R. Khochiani, “Credit rating of bank customers using a new ensemble method based on support vector machine: a case study of Pasargad bank,” Soft Comput. J., vol. 10, no. 2, pp. 2-15, 2022, doi: 10.22052/scj.2022.243227.1016 [In Persian].
[86] A. Khosravi, H. Abdulmaleki, and M. Fayazi, “Predicting the academic status of admitted applicants based on educational and admission data using data mining techniques,” Soft Comput. J., vol. 9, no. 2, pp. 94-113, 2021, doi: 10.22052/scj.2021.242837.0 [In Persian].