TINJAUAN SISTEMATIS TREN, METODE, DAN DATA PADA PREDIKSI KELULUSAN MAHASISWA
Abstract
Penelitian tentang prediksi kelulusan mahasiswa banyak dipublikasikan akan tetapi biasanya metode beserta data yang dihasilkan dikemas secara terpisah dan kompleks sehingga gambaran tentang topik prediksi kelulusan mahasiswa saat ini kurang komprehensif. Tinjauan literatur ini bertujuan untuk mengidentifikasi dan menganalisis tren penelitian, dataset, dan metode tentang prediksi kelulusan mahasiswa yang dipublikasikan antara tahun 2020-2025. Berdasarkan kriteria inklusi dan ekslusi, tercatat sebanyak 75 artikel dari 199 artikel yang bersumber pada jurnal kuartil 1-4. Tinjauan literatur sistematis dapat didefinisikan sebagai proses mengidentifikasi, menilai, dan menginterpretasikan semua bukti penelitian yang tersedia untuk memberikan jawaban atas pertanyaan penelitian yang spesifik. Hasil analisis dalam lima tahun terakhir mengungkapkan bahwa penelitian prediksi kelulusan mahasiswa terdapat empat topik yaitu prediksi/klasifikasi, analisis dataset, pengelompokan (clustering), dan estimasi. Selain itu, terdapat juga dua tren yang dibahas yaitu pemilihan fitur (feature selection) dan data tidak seimbang (imbalance data). Kategori data yang digunakan pada lima tahun terakhir lebih banyak menggunakan data private atau data real sebanyak 91% daripada data public. Metode yang paling sering digunakan pada topik-topik tersebut adalah Random Forest (RF), dan paling jarang yaitu metode Artificial Neural Network (ANN). Terdapat juga penggabungan metode untuk optimasi parameter di beberapa klasifikasi.
References
[1] L. R. Pelima, Y. Sukmana, and Y. Rosmansyah, “Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review,†IEEE Access, vol. 12, pp. 23451–23465, 2024, doi: 10.1109/ACCESS.2024.3361479.
[2] Y. Yang, D. Hooshyar, M. Pedaste, M. Wang, Y.-M. Huang, and H. Lim, “Predicting course achievement of university students based on their procrastination behaviour on Moodle,†Soft Comput., vol. 24, no. 24, pp. 18777–18793, Dec. 2020, doi: 10.1007/s00500-020-05110-4.
[3] D. Uliyan, A. S. Aljaloud, A. Alkhalil, H. S. A. Amer, M. A. E. A. Mohamed, and A. F. M. Alogali, “Deep Learning Model to Predict Students Retention Using BLSTM and CRF,†IEEE Access, vol. 9, pp. 135550–135558, 2021, doi: 10.1109/ACCESS.2021.3117117.
[4] E. Ahmed, “Student Performance Prediction Using Machine Learning Algorithms,†Appl. Comput. Intell. Soft Comput., vol. 2024, no. 1, p. 4067721, Jan. 2024, doi: 10.1155/2024/4067721.
[5] S. Alturki, L. Cohausz, and H. Stuckenschmidt, “Predicting Master’s students’ academic performance: an empirical study in Germany,†Smart Learn. Environ., vol. 9, no. 1, p. 38, Dec. 2022, doi: 10.1186/s40561-022-00220-y.
[6] R. Mehdi and M. Nachouki, “A Neuro Fuzzy Model for Predicting and Analyzing Student Graduation Performance in Computing Programs,†Educ. Inf. Technol., vol. 28, no. 3, pp. 2455–2484, Mar. 2023, doi: 10.1007/s10639-022-11205-2.
[7] M. Kamal et al., “Metaheuristics Method for Classification and Prediction of Student Performance Using Machine Learning Predictors,†Math. Probl. Eng., vol. 2022, pp. 1–5, July 2022, doi: 10.1155/2022/2581951.
[8] R. Asad, S. Altaf, S. Ahmad, and H. Mahmoud, “Machine Learning-Based Hybrid Ensemble Model Achieving Precision Education for Online Education Amid the Lockdown Period of COVID-19 Pandemic in Pakistan,†MDPI Sustain., Mar. 2023, doi: https://doi.org/10.3390/su15065431.
[9] A. M. Rabelo and L. E. Zárate, “A model for predicting dropout of higher education students,†Data Sci. Manag., vol. 8, no. 1, pp. 72–85, Mar. 2025, doi: 10.1016/j.dsm.2024.07.001.
[10] L. Yan and Y. Liu, “An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning,†Symmetry, vol. 12, no. 5, p. 728, May 2020, doi: 10.3390/sym12050728.
[11] L. Zhao et al., “Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data,†IEEE Access, vol. 9, pp. 5453–5465, 2021, doi: 10.1109/ACCESS.2020.3002791.
[12] L. S. Maurya, M. S. Hussain, and S. Singh, “Developing Classifiers through Machine Learning Algorithms for Student Placement Prediction Based on Academic Performance,†Appl. Artif. Intell., vol. 35, no. 6, pp. 403–420, May 2021, doi: 10.1080/08839514.2021.1901032.
[13] L. K. Smirani, H. A. Yamani, L. J. Menzli, and J. A. Boulahia, “Using Ensemble Learning Algorithms to Predict Student Failure and Enabling Customized Educational Paths,†Sci. Program., vol. 2022, pp. 1–15, Apr. 2022, doi: 10.1155/2022/3805235.
[14] N. Messaoudi, G. Moukhliss, J. K. Naciri, and B. Bensassi, “Machine Learning Algorithms for Quantifying the Role of Prerequisites in University Success,†Int. J. Mod. Educ. Comput. Sci., vol. 14, no. 6, pp. 1–12, Dec. 2022, doi: 10.5815/ijmecs.2022.06.01.
[15] J. Dai, “Improving Random Forest Algorithm for University Academic Affairs Management System Platform Construction,†Adv. Multimed., vol. 2022, pp. 1–9, July 2022, doi: 10.1155/2022/8064844.
[16] N. Messaoudi, J. K. Naciri, and B. Bensassi, “Students’ Results Prediction Using MachineLearning Algorithms and Online Learning duringthe COVID-19 Pandemic,†Int. J. Mod. Educ. Comput. Sci., vol. 16, no. 4, pp. 17–34, Aug. 2024, doi: 10.5815/ijmecs.2024.04.02.
[17] A. Kumar, B. Naqvi, and A. Wolff, “Exploring the energy informatics and energy citizenship domains: a systematic literature review,†Energy Inform., vol. 6, no. 1, Aug. 2023, doi: 10.1186/s42162-023-00268-1.
[18] S. Nirmani, M. Shahin, H. Khalajzadeh, and X. Liu, “A systematic literature review on task recommendation systems for crowdsourced software engineering,†Inf. Softw. Technol., vol. 184, p. 107753, Aug. 2025, doi: 10.1016/j.infsof.2025.107753.
[19] R. Shrestha, R. Knez, T. Acharya, and P. C. Bhattarai, “Ethical discourse on pro-environmental behavior: a systematic literature review using PRISMA,†Discov. Sustain., vol. 6, no. 1, p. 461, May 2025, doi: 10.1007/s43621-025-01296-5.
[20] S. Yu, L. Zhong, and Y. Liu, “User Behavior Analysis and Prediction Model Construction in Higher Education Management Information Systems,†Appl. Math. Nonlinear Sci., vol. 9, no. 1, p. 20243300, Jan. 2024, doi: 10.2478/amns-2024-3300.
[21] N. T. H. Thuy, N. T. V. Ha, N. N. Trung, V. T. T. Binh, N. T. Hang, and V. T. Binh, “Comparing the Effectiveness of Machine Learning and Deep Learning Models in Student Credit Scoring: A Case Study in Vietnam,†Risks, vol. 13, no. 5, p. 99, May 2025, doi: 10.3390/risks13050099.
[22] G. Wyness, L. Macmillan, J. Anders, and C. Dilnot, “Grade expectations: how well can past performance predict future grades?,†Educ. Econ., vol. 31, no. 4, pp. 397–418, July 2023, doi: 10.1080/09645292.2022.2113861.
[23] R. Rois, M. Ray, A. Rahman, and S. K. Roy, “Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms,†J. Health Popul. Nutr., vol. 40, no. 1, p. 50, Dec. 2021, doi: 10.1186/s41043-021-00276-5.
[24] S. Fida, N. Masood, N. Tariq, and F. Qayyum, “A Novel Hybrid Ensemble Clustering Technique for Student Performance Prediction,†JUCS - J. Univers. Comput. Sci., vol. 28, no. 8, pp. 777–798, Aug. 2022, doi: 10.3897/jucs.73427.
[25] S. Yang and W. Xie, “Applications and Challenges of Statistics in Large-Scale Data Mining,†Appl. Math. Nonlinear Sci., vol. 9, no. 1, p. 20241653, Jan. 2024, doi: 10.2478/amns-2024-1653.
[26] H. Sokout, T. Usagawa, and S. Mukhtar, “Learning Analytics: Analyzing Various Aspects of Learners’ Performance in Blended Courses. The Case of Kabul Polytechnic University, Afghanistan,†Int. J. Emerg. Technol. Learn. IJET, vol. 15, no. 12, p. 168, June 2020, doi: 10.3991/ijet.v15i12.13473.
[27] P. K. Sharma et al., “IndoAirSense: A framework for indoor air quality estimation and forecasting,†Atmospheric Pollut. Res., vol. 12, no. 1, pp. 10–22, Jan. 2021, doi: 10.1016/j.apr.2020.07.027.
[28] V. P. Romero and C. M. L. Alvarez, “Developing a Model to Predict Self-Reported Student Performance during Online Education Based on the Acoustic Environment,†MDPI Sustain., May 2024, doi: https://doi.org/10.3390/su16114411.
[29] B. Tang, S. Li, and C. Zhao, “Predicting the Performance of Students Using Deep Ensemble Learning,†J. Intell., vol. 12, no. 12, p. 124, Dec. 2024, doi: 10.3390/jintelligence12120124.
[30] J. E. Alderman et al., “Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations,†Lancet Digit. Health, vol. 7, no. 1, pp. e64–e88, Jan. 2025, doi: 10.1016/S2589-7500(24)00224-3.
[31] I. Riadi and R. Umar, “Prediksi Kelulusan Tepat Waktu Berdasarkan Riwayat Akademik Menggunakan Metode Naïve Bayes,†Decode J. Pendidik. Teknol. Inf., vol. 4, no. 1, pp. 191–203, Jan. 2024, doi: 10.51454/decode.v4i1.308.
[32] I. Riadi, R. Umar, and R. Anggara, “Prediksi Kelulusan Tepat Waktu Berdasarkan Riwayat Akademik Menggunakan Metode K-Nearest Neighbor,†J. Teknol. Inf. Dan Ilmu Komput., vol. 11, no. 2, pp. 249–256, Apr. 2024, doi: 10.25126/jtiik.20241127330.
[33] I. Riadi, R. Umar, and R. Anggara, “Comparative Analysis of Naive Bayes and K-NN Approaches to Predict Timely Graduation using Academic History,†Int. J. Comput. Digit. Syst., vol. 15, no. 1, pp. 1163–1174, Sept. 2024, doi: 10.12785/ijcds/160185.
[34] Huawei Technologies Co., Ltd., Artificial Intelligence Technology. Singapore: Springer Nature Singapore, 2023. doi: 10.1007/978-981-19-2879-6.
[35] B. Albreiki, T. Habuza, and N. Zaki, “Extracting topological features to identify at-risk students using machine learning and graph convolutional network models,†Int. J. Educ. Technol. High. Educ., vol. 20, no. 1, p. 23, Apr. 2023, doi: 10.1186/s41239-023-00389-3.
[36] M. A. Aslam, F. Murtaza, M. E. U. Haq, A. Yasin, and M. A. Azam, “A Human-Centered Approach to Academic Performance Prediction Using Personality Factors in Educational AI,†Information, vol. 15, no. 12, p. 777, Dec. 2024, doi: 10.3390/info15120777.
[37] S. A. A. Balabied and H. F. Eid, “Utilizing random forest algorithm for early detection of academic underperformance in open learning environments,†PeerJ Comput. Sci., vol. 9, p. e1708, Nov. 2023, doi: 10.7717/peerj-cs.1708.
[38] M. Bellaj, A. B. Dahmane, S. Boudra, and M. L. Sefian, “Educational Data Mining: Employing Machine Learning Techniques and Hyperparameter Optimization to Improve Students’ Academic Performance,†Int. J. Online Biomed. Eng. IJOE, vol. 20, no. 03, pp. 55–74, Feb. 2024, doi: 10.3991/ijoe.v20i03.46287.
[39] K. R. Mahmudah, F. Indriani, Y. Takemori-Sakai, Y. Iwata, T. Wada, and K. Satou, “Classification of Imbalanced Data Represented as Binary Features,†Appl. Sci., vol. 11, no. 17, p. 7825, Aug. 2021, doi: 10.3390/app11177825.
[40] A. Nabil, M. Seyam, and A. Abou Elfetouh, “Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks,†IEEE Access, vol. 9, pp. 140731–140746, 2021, doi: 10.1109/ACCESS.2021.3119596.
[41] L. Vives et al., “Prediction of Students’ Academic Performance in the Programming Fundamentals Course Using Long Short-Term Memory Neural Networks,†IEEE Access, vol. 12, pp. 5882–5898, 2024, doi: 10.1109/ACCESS.2024.3350169.
[42] A. Zanellati, S. P. Zingaro, and M. Gabbrielli, “Balancing Performance and Explainability in Academic Dropout Prediction,†IEEE Trans. Learn. Technol., vol. 17, pp. 2086–2099, 2024, doi: 10.1109/TLT.2024.3425959.
[43] S. Farshidi et al., “Understanding user intent modeling for conversational recommender systems: a systematic literature review,†User Model. User-Adapt. Interact., vol. 34, no. 5, pp. 1643–1706, Nov. 2024, doi: 10.1007/s11257-024-09398-x.
[44] S. Sarmini, S. Sunardi, and A. Fadlil, “Performa Random Forest dan XGBoost pada Deteksi Penipuan E-Commerce Menggunakan Augmentasi Data CGAN,†Build. Inform. Technol. Sci. BITS, vol. 6, no. 3, pp. 1919–1931, Dec. 2024, doi: 10.47065/bits.v6i3.6430.
[45] S. Sarmini, S. Sunardi, and A. Fadlil, “Evaluating The Effectiveness of Augmentation and Classifier Algorithms for Fraud Detection: Comparing CGAN and SMOTE with Random Forest and XGBoost,†Appl. Inf. Syst. Manag. AISM, vol. 8, no. 2, pp. 221–230, Oct. 2025, doi: 10.15408/aism.v8i2.46308.
[46] S. Sunardi, A. Ramadhan, E. Abdurachman, A. Trisetyarso, and M. Zarlis, “Acceptance of augmented reality in video conference based learning during COVID-19 pandemic in higher education,†Bull. Electr. Eng. Inform., vol. 11, no. 6, pp. 3598–3608, Dec. 2022, doi: 10.11591/eei.v11i6.4035.
[47] A. I. Adekitan and O. Salau, “Toward an improved learning process: the relevance of ethnicity to data mining prediction of students’ performance,†SN Appl. Sci., vol. 2, no. 1, p. 8, Jan. 2020, doi: 10.1007/s42452-019-1752-1.
[48] A. Al-Azawei and M. A. A. Al-Masoudy, “Predicting Learners’ Performance in Virtual Learning Environment (VLE) based on Demographic, Behavioral and Engagement Antecedents,†Int. J. Emerg. Technol. Learn. IJET, vol. 15, no. 09, p. 60, May 2020, doi: 10.3991/ijet.v15i09.12691.
[49] Y. He et al., “Online At-Risk Student Identification using RNN-GRU Joint Neural Networks,†Information, vol. 11, no. 10, p. 474, Oct. 2020, doi: 10.3390/info11100474.
[50] 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, p. 354, Jan. 2020, doi: 10.3390/app10010354.
[51] H. A. Mengash, “Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems,†IEEE Access, vol. 8, pp. 55462–55470, 2020, doi: 10.1109/ACCESS.2020.2981905.
[52] M. Injadat, A. Moubayed, A. B. Nassif, and A. Shami, “Multi-split optimized bagging ensemble model selection for multi-class educational data mining,†Appl. Intell., vol. 50, no. 12, pp. 4506–4528, Dec. 2020, doi: 10.1007/s10489-020-01776-3.
[53] I. Smirnov, “Estimating educational outcomes from students’ short texts on social media,†EPJ Data Sci., vol. 9, no. 1, p. 27, Dec. 2020, doi: 10.1140/epjds/s13688-020-00245-8.
[54] A. Esteban, C. Romero, and A. Zafra, “Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses,†Appl. Sci., vol. 11, no. 21, p. 10145, Oct. 2021, doi: 10.3390/app112110145.
[55] P. Hai tao et al., “Predicting academic performance of students in Chinese-foreign cooperation in running schools with graph convolutional network,†Neural Comput. Appl., vol. 33, no. 2, pp. 637–645, Jan. 2021, doi: 10.1007/s00521-020-05045-9.
[56] P. Nuankaew, P. Nasa ngium, and W. S. Nuankaew, “Application for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning,†Int. J. Interact. Mob. Technol. IJIM, vol. 15, no. 22, p. 22, Nov. 2021, doi: 10.3991/ijim.v15i22.24069.
[57] J. Kabathova and M. Drlik, “Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques,†Appl. Sci., vol. 11, no. 7, p. 3130, Apr. 2021, doi: 10.3390/app11073130.
[58] B. K. Yousafzai et al., “Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network,†Sustainability, vol. 13, no. 17, p. 9775, Aug. 2021, doi: 10.3390/su13179775.
[59] Y. Baashar et al., “Evaluation of postgraduate academic performance using artificial intelligence models,†Alex. Eng. J., vol. 61, no. 12, pp. 9867–9878, Dec. 2022, doi: 10.1016/j.aej.2022.03.021.
[60] E. Çakıt and M. Dağdeviren, “Predicting the percentage of student placement: A comparative study of machine learning algorithms,†Educ. Inf. Technol., vol. 27, no. 1, pp. 997–1022, Jan. 2022, doi: 10.1007/s10639-021-10655-4.
[61] C. Liu, H. Wang, and Z. Yuan, “A Method for Predicting the Academic Performances of College Students Based on Education System Data,†Mathematics, vol. 10, no. 20, p. 3737, Oct. 2022, doi: 10.3390/math10203737.
[62] G. Feng, M. Fan, and C. Ao, “Exploration and Visualization of Learning Behavior Patterns From the Perspective of Educational Process Mining,†IEEE Access, vol. 10, pp. 65271–65283, 2022, doi: 10.1109/ACCESS.2022.3184111.
[63] S. Gaftandzhieva et al., “Exploring Online Activities to Predict the Final Grade of Student,†Mathematics, vol. 10, no. 20, p. 3758, Oct. 2022, doi: 10.3390/math10203758.
[64] S. Garmpis, M. Maragoudakis, and A. Garmpis, “Assisting Educational Analytics with AutoML Functionalities,†Computers, vol. 11, no. 6, p. 97, June 2022, doi: 10.3390/computers11060097.
[65] M. Yagcı, “Educational data mining: prediction of students’ academic performance using machine learning algorithms,†Smart Learn. Environ., vol. 9, no. 1, p. 11, Dec. 2022, doi: 10.1186/s40561-022-00192-z.
[66] M. Gollapalli et al., “SUNFIT: A Machine Learning-Based Sustainable University Field Training Framework for Higher Education,†MDPI Sustain., May 2023, doi: https://doi.org/10.3390/su15108057.
[67] S. Issaro and P. Wannapiroon, “Intelligent Student Relationship Management Platform with Machine Learning for Student Empowerment,†Int. J. Emerg. Technol. Learn. IJET, vol. 18, no. 04, pp. 66–87, Feb. 2023, doi: 10.3991/ijet.v18i04.32583.
[68] S. Atalla et al., “An Intelligent Recommendation System for Automating Academic Advising Based on Curriculum Analysis and Performance Modeling,†Mathematics, vol. 11, no. 5, p. 1098, Feb. 2023, doi: 10.3390/math11051098.
[69] R. Mehdi and M. Nachouki, “A neuro-fuzzy model for predicting and analyzing student graduation performance in computing programs,†Educ. Inf. Technol., vol. 28, no. 3, pp. 2455–2484, Mar. 2023, doi: 10.1007/s10639-022-11205-2.
[70] K. Qin, X. Xie, Q. He, and G. Deng, “Early Warning of Student Performance With Integration of Subjective and Objective Elements,†IEEE Access, vol. 11, pp. 72601–72617, 2023, doi: 10.1109/ACCESS.2023.3295580.
[71] Z. Fan and R. Chiong, “Identifying digital capabilities in university courses: An automated machine learning approach,†Educ. Inf. Technol., vol. 28, no. 4, pp. 3937–3952, Apr. 2023, doi: 10.1007/s10639-022-11075-8.
[72] T. S. Lopez, D. P. Perez, V. G. Ruiz, and L. F. Carvajal, “Implementation of Machine Learning Techniques and Creation of an Artificial Neural Network for The Prediction of The Academic Performance of Students in University Environments That use E-Learning and Streaming,†Dyna Publ., vol. 98, no. 3, pp. 282–287, May 2023, doi: 10.6036/10760.
[73] K. Wang, “Academic Early Warning Model for Students Based on Big Data Analysis,†Int. J. Emerg. Technol. Learn. IJET, vol. 18, no. 12, pp. 16–31, June 2023, doi: 10.3991/ijet.v18i12.41087.
[74] D. Sun et al., “A University Student Performance Prediction Model and Experiment Based on Multi-Feature Fusion and Attention Mechanism,†IEEE Access, vol. 11, pp. 112307–112319, 2023, doi: 10.1109/ACCESS.2023.3323365.
[75] N. U. R. Junejo et al., “SAPPNet: students’ academic performance prediction during COVID-19 using neural network,†Sci. Rep., vol. 14, no. 1, p. 24605, Oct. 2024, doi: 10.1038/s41598-024-75242-2.
[76] D. Khairy, N. Alharbi, M. A. Amasha, M. F. Areed, S. Alkhalaf, and R. A. Abougalala, “Prediction of student exam performance using data mining classification algorithms,†Educ. Inf. Technol., vol. 29, no. 16, pp. 21621–21645, Nov. 2024, doi: 10.1007/s10639-024-12619-w.
[77] J. A. I. S. Masood, N. S. Kalyan Chakravarthy, D. Asirvatham, M. Marjani, D. Abdulkareem Shafiq, and S. Nidamanuri, “A Hybrid Deep Learning Model to Predict High-Risk Students in Virtual Learning Environments,†IEEE Access, vol. 12, pp. 103687–103703, 2024, doi: 10.1109/ACCESS.2024.3434644.
[78] M. R. Borna, H. Saadat, A. T. Hojjati, and E. Akbari, “Analyzing click data with AI: implications for student performance prediction and learning assessment,†Front. Educ., vol. 9, p. 1421479, Dec. 2024, doi: 10.3389/feduc.2024.1421479.
[79] D. Monteverde Suárez et al., “Predicting students’ academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes,†BMC Med. Educ., vol. 24, no. 1, p. 74, Jan. 2024, doi: 10.1186/s12909-023-04918-6.
[80] M. Milicevic, B. Marinovic, and L. Jeftic, “Machine learning methods as auxiliary tool for effective mathematics teaching,†Comput. Appl. Eng. Educ., vol. 32, no. 6, p. e22787, Nov. 2024, doi: 10.1002/cae.22787.
[81] N. G. Cu, T. L. Nghiem, T. H. Ngo, M. T. L. Nguyen, and H. Q. Phung, “Increment of Academic Performance Prediction of Atâ€Risk Student by Dealing With Data Imbalance Problem,†Appl. Comput. Intell. Soft Comput., vol. 2024, no. 1, p. 4795606, Jan. 2024, doi: 10.1155/2024/4795606.
[82] M. S. Ramirez Montoya, R. Morales Menendez, M. Tworek, C. A. Escobar, R. Tariq, and G. C. Tenorio Sepulveda, “Complex competencies for leader education: artificial intelligence analysis in student achievement profiling,†Cogent Educ., vol. 11, no. 1, p. 2378508, Dec. 2024, doi: 10.1080/2331186X.2024.2378508.
[83] T. Zhang, Z. Zhong, W. Mao, Z. Zhang, and Z. Li, “A New Machine-Learning-Driven Grade-Point Average Prediction Approach for College Students Incorporating Psychological Evaluations in the Post-COVID-19 Era,†Electronics, vol. 13, no. 10, p. 1928, May 2024, doi: 10.3390/electronics13101928.
[84] M. A. Aslam, F. Murtaza, M. E. U. Haq, A. Yasin, and N. Ali, “SAPEx-D: A Comprehensive Dataset for Predictive Analytics in Personalized Education Using Machine Learning,†Data, vol. 10, no. 3, p. 27, Feb. 2025, doi: 10.3390/data10030027.
[85] B. Alnasyan, M. Basheri, M. Alassafi, and K. Alnasyan, “Kanformer: an attention-enhanced deep learning model for predicting student performance in virtual learning environments,†Soc. Netw. Anal. Min., vol. 15, no. 1, p. 25, Mar. 2025, doi: 10.1007/s13278-025-01446-7.
[86] T. Jin, “Methods and reliability study of moral education assessment in universities: A machine learning-based approach,†Alex. Eng. J., vol. 125, pp. 20–28, June 2025, doi: 10.1016/j.aej.2025.03.095.
[87] A. Kord, A. Aboelfetouh, and S. M. Shohieb, “Academic course planning recommendation and students’ performance prediction multi-modal based on educational data mining techniques,†J. Comput. High. Educ., Jan. 2025, doi: 10.1007/s12528-024-09426-0.
[88] L. S. Chen, T. T. Huynh Cam, V. C. Nguyen, T. C. Lu, and D. K. Le Huynh, “Predicting Early Employability of Vietnamese Graduates: Insights from Data-Driven Analysis Through Machine Learning Methods,†Big Data Cogn. Comput., vol. 9, no. 5, p. 134, May 2025, doi: 10.3390/bdcc9050134.
[89] S. E. Hooper, N. Ragland, and E. Artemiou, “Random forest models reveal academic and financial factors outweigh demographics in predicting completion of a year-round veterinary program,†J. Am. Vet. Med. Assoc., vol. 263, no. 2, pp. 1–9, Feb. 2025, doi: 10.2460/javma.24.08.0501.
[2] Y. Yang, D. Hooshyar, M. Pedaste, M. Wang, Y.-M. Huang, and H. Lim, “Predicting course achievement of university students based on their procrastination behaviour on Moodle,†Soft Comput., vol. 24, no. 24, pp. 18777–18793, Dec. 2020, doi: 10.1007/s00500-020-05110-4.
[3] D. Uliyan, A. S. Aljaloud, A. Alkhalil, H. S. A. Amer, M. A. E. A. Mohamed, and A. F. M. Alogali, “Deep Learning Model to Predict Students Retention Using BLSTM and CRF,†IEEE Access, vol. 9, pp. 135550–135558, 2021, doi: 10.1109/ACCESS.2021.3117117.
[4] E. Ahmed, “Student Performance Prediction Using Machine Learning Algorithms,†Appl. Comput. Intell. Soft Comput., vol. 2024, no. 1, p. 4067721, Jan. 2024, doi: 10.1155/2024/4067721.
[5] S. Alturki, L. Cohausz, and H. Stuckenschmidt, “Predicting Master’s students’ academic performance: an empirical study in Germany,†Smart Learn. Environ., vol. 9, no. 1, p. 38, Dec. 2022, doi: 10.1186/s40561-022-00220-y.
[6] R. Mehdi and M. Nachouki, “A Neuro Fuzzy Model for Predicting and Analyzing Student Graduation Performance in Computing Programs,†Educ. Inf. Technol., vol. 28, no. 3, pp. 2455–2484, Mar. 2023, doi: 10.1007/s10639-022-11205-2.
[7] M. Kamal et al., “Metaheuristics Method for Classification and Prediction of Student Performance Using Machine Learning Predictors,†Math. Probl. Eng., vol. 2022, pp. 1–5, July 2022, doi: 10.1155/2022/2581951.
[8] R. Asad, S. Altaf, S. Ahmad, and H. Mahmoud, “Machine Learning-Based Hybrid Ensemble Model Achieving Precision Education for Online Education Amid the Lockdown Period of COVID-19 Pandemic in Pakistan,†MDPI Sustain., Mar. 2023, doi: https://doi.org/10.3390/su15065431.
[9] A. M. Rabelo and L. E. Zárate, “A model for predicting dropout of higher education students,†Data Sci. Manag., vol. 8, no. 1, pp. 72–85, Mar. 2025, doi: 10.1016/j.dsm.2024.07.001.
[10] L. Yan and Y. Liu, “An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning,†Symmetry, vol. 12, no. 5, p. 728, May 2020, doi: 10.3390/sym12050728.
[11] L. Zhao et al., “Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data,†IEEE Access, vol. 9, pp. 5453–5465, 2021, doi: 10.1109/ACCESS.2020.3002791.
[12] L. S. Maurya, M. S. Hussain, and S. Singh, “Developing Classifiers through Machine Learning Algorithms for Student Placement Prediction Based on Academic Performance,†Appl. Artif. Intell., vol. 35, no. 6, pp. 403–420, May 2021, doi: 10.1080/08839514.2021.1901032.
[13] L. K. Smirani, H. A. Yamani, L. J. Menzli, and J. A. Boulahia, “Using Ensemble Learning Algorithms to Predict Student Failure and Enabling Customized Educational Paths,†Sci. Program., vol. 2022, pp. 1–15, Apr. 2022, doi: 10.1155/2022/3805235.
[14] N. Messaoudi, G. Moukhliss, J. K. Naciri, and B. Bensassi, “Machine Learning Algorithms for Quantifying the Role of Prerequisites in University Success,†Int. J. Mod. Educ. Comput. Sci., vol. 14, no. 6, pp. 1–12, Dec. 2022, doi: 10.5815/ijmecs.2022.06.01.
[15] J. Dai, “Improving Random Forest Algorithm for University Academic Affairs Management System Platform Construction,†Adv. Multimed., vol. 2022, pp. 1–9, July 2022, doi: 10.1155/2022/8064844.
[16] N. Messaoudi, J. K. Naciri, and B. Bensassi, “Students’ Results Prediction Using MachineLearning Algorithms and Online Learning duringthe COVID-19 Pandemic,†Int. J. Mod. Educ. Comput. Sci., vol. 16, no. 4, pp. 17–34, Aug. 2024, doi: 10.5815/ijmecs.2024.04.02.
[17] A. Kumar, B. Naqvi, and A. Wolff, “Exploring the energy informatics and energy citizenship domains: a systematic literature review,†Energy Inform., vol. 6, no. 1, Aug. 2023, doi: 10.1186/s42162-023-00268-1.
[18] S. Nirmani, M. Shahin, H. Khalajzadeh, and X. Liu, “A systematic literature review on task recommendation systems for crowdsourced software engineering,†Inf. Softw. Technol., vol. 184, p. 107753, Aug. 2025, doi: 10.1016/j.infsof.2025.107753.
[19] R. Shrestha, R. Knez, T. Acharya, and P. C. Bhattarai, “Ethical discourse on pro-environmental behavior: a systematic literature review using PRISMA,†Discov. Sustain., vol. 6, no. 1, p. 461, May 2025, doi: 10.1007/s43621-025-01296-5.
[20] S. Yu, L. Zhong, and Y. Liu, “User Behavior Analysis and Prediction Model Construction in Higher Education Management Information Systems,†Appl. Math. Nonlinear Sci., vol. 9, no. 1, p. 20243300, Jan. 2024, doi: 10.2478/amns-2024-3300.
[21] N. T. H. Thuy, N. T. V. Ha, N. N. Trung, V. T. T. Binh, N. T. Hang, and V. T. Binh, “Comparing the Effectiveness of Machine Learning and Deep Learning Models in Student Credit Scoring: A Case Study in Vietnam,†Risks, vol. 13, no. 5, p. 99, May 2025, doi: 10.3390/risks13050099.
[22] G. Wyness, L. Macmillan, J. Anders, and C. Dilnot, “Grade expectations: how well can past performance predict future grades?,†Educ. Econ., vol. 31, no. 4, pp. 397–418, July 2023, doi: 10.1080/09645292.2022.2113861.
[23] R. Rois, M. Ray, A. Rahman, and S. K. Roy, “Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms,†J. Health Popul. Nutr., vol. 40, no. 1, p. 50, Dec. 2021, doi: 10.1186/s41043-021-00276-5.
[24] S. Fida, N. Masood, N. Tariq, and F. Qayyum, “A Novel Hybrid Ensemble Clustering Technique for Student Performance Prediction,†JUCS - J. Univers. Comput. Sci., vol. 28, no. 8, pp. 777–798, Aug. 2022, doi: 10.3897/jucs.73427.
[25] S. Yang and W. Xie, “Applications and Challenges of Statistics in Large-Scale Data Mining,†Appl. Math. Nonlinear Sci., vol. 9, no. 1, p. 20241653, Jan. 2024, doi: 10.2478/amns-2024-1653.
[26] H. Sokout, T. Usagawa, and S. Mukhtar, “Learning Analytics: Analyzing Various Aspects of Learners’ Performance in Blended Courses. The Case of Kabul Polytechnic University, Afghanistan,†Int. J. Emerg. Technol. Learn. IJET, vol. 15, no. 12, p. 168, June 2020, doi: 10.3991/ijet.v15i12.13473.
[27] P. K. Sharma et al., “IndoAirSense: A framework for indoor air quality estimation and forecasting,†Atmospheric Pollut. Res., vol. 12, no. 1, pp. 10–22, Jan. 2021, doi: 10.1016/j.apr.2020.07.027.
[28] V. P. Romero and C. M. L. Alvarez, “Developing a Model to Predict Self-Reported Student Performance during Online Education Based on the Acoustic Environment,†MDPI Sustain., May 2024, doi: https://doi.org/10.3390/su16114411.
[29] B. Tang, S. Li, and C. Zhao, “Predicting the Performance of Students Using Deep Ensemble Learning,†J. Intell., vol. 12, no. 12, p. 124, Dec. 2024, doi: 10.3390/jintelligence12120124.
[30] J. E. Alderman et al., “Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations,†Lancet Digit. Health, vol. 7, no. 1, pp. e64–e88, Jan. 2025, doi: 10.1016/S2589-7500(24)00224-3.
[31] I. Riadi and R. Umar, “Prediksi Kelulusan Tepat Waktu Berdasarkan Riwayat Akademik Menggunakan Metode Naïve Bayes,†Decode J. Pendidik. Teknol. Inf., vol. 4, no. 1, pp. 191–203, Jan. 2024, doi: 10.51454/decode.v4i1.308.
[32] I. Riadi, R. Umar, and R. Anggara, “Prediksi Kelulusan Tepat Waktu Berdasarkan Riwayat Akademik Menggunakan Metode K-Nearest Neighbor,†J. Teknol. Inf. Dan Ilmu Komput., vol. 11, no. 2, pp. 249–256, Apr. 2024, doi: 10.25126/jtiik.20241127330.
[33] I. Riadi, R. Umar, and R. Anggara, “Comparative Analysis of Naive Bayes and K-NN Approaches to Predict Timely Graduation using Academic History,†Int. J. Comput. Digit. Syst., vol. 15, no. 1, pp. 1163–1174, Sept. 2024, doi: 10.12785/ijcds/160185.
[34] Huawei Technologies Co., Ltd., Artificial Intelligence Technology. Singapore: Springer Nature Singapore, 2023. doi: 10.1007/978-981-19-2879-6.
[35] B. Albreiki, T. Habuza, and N. Zaki, “Extracting topological features to identify at-risk students using machine learning and graph convolutional network models,†Int. J. Educ. Technol. High. Educ., vol. 20, no. 1, p. 23, Apr. 2023, doi: 10.1186/s41239-023-00389-3.
[36] M. A. Aslam, F. Murtaza, M. E. U. Haq, A. Yasin, and M. A. Azam, “A Human-Centered Approach to Academic Performance Prediction Using Personality Factors in Educational AI,†Information, vol. 15, no. 12, p. 777, Dec. 2024, doi: 10.3390/info15120777.
[37] S. A. A. Balabied and H. F. Eid, “Utilizing random forest algorithm for early detection of academic underperformance in open learning environments,†PeerJ Comput. Sci., vol. 9, p. e1708, Nov. 2023, doi: 10.7717/peerj-cs.1708.
[38] M. Bellaj, A. B. Dahmane, S. Boudra, and M. L. Sefian, “Educational Data Mining: Employing Machine Learning Techniques and Hyperparameter Optimization to Improve Students’ Academic Performance,†Int. J. Online Biomed. Eng. IJOE, vol. 20, no. 03, pp. 55–74, Feb. 2024, doi: 10.3991/ijoe.v20i03.46287.
[39] K. R. Mahmudah, F. Indriani, Y. Takemori-Sakai, Y. Iwata, T. Wada, and K. Satou, “Classification of Imbalanced Data Represented as Binary Features,†Appl. Sci., vol. 11, no. 17, p. 7825, Aug. 2021, doi: 10.3390/app11177825.
[40] A. Nabil, M. Seyam, and A. Abou Elfetouh, “Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks,†IEEE Access, vol. 9, pp. 140731–140746, 2021, doi: 10.1109/ACCESS.2021.3119596.
[41] L. Vives et al., “Prediction of Students’ Academic Performance in the Programming Fundamentals Course Using Long Short-Term Memory Neural Networks,†IEEE Access, vol. 12, pp. 5882–5898, 2024, doi: 10.1109/ACCESS.2024.3350169.
[42] A. Zanellati, S. P. Zingaro, and M. Gabbrielli, “Balancing Performance and Explainability in Academic Dropout Prediction,†IEEE Trans. Learn. Technol., vol. 17, pp. 2086–2099, 2024, doi: 10.1109/TLT.2024.3425959.
[43] S. Farshidi et al., “Understanding user intent modeling for conversational recommender systems: a systematic literature review,†User Model. User-Adapt. Interact., vol. 34, no. 5, pp. 1643–1706, Nov. 2024, doi: 10.1007/s11257-024-09398-x.
[44] S. Sarmini, S. Sunardi, and A. Fadlil, “Performa Random Forest dan XGBoost pada Deteksi Penipuan E-Commerce Menggunakan Augmentasi Data CGAN,†Build. Inform. Technol. Sci. BITS, vol. 6, no. 3, pp. 1919–1931, Dec. 2024, doi: 10.47065/bits.v6i3.6430.
[45] S. Sarmini, S. Sunardi, and A. Fadlil, “Evaluating The Effectiveness of Augmentation and Classifier Algorithms for Fraud Detection: Comparing CGAN and SMOTE with Random Forest and XGBoost,†Appl. Inf. Syst. Manag. AISM, vol. 8, no. 2, pp. 221–230, Oct. 2025, doi: 10.15408/aism.v8i2.46308.
[46] S. Sunardi, A. Ramadhan, E. Abdurachman, A. Trisetyarso, and M. Zarlis, “Acceptance of augmented reality in video conference based learning during COVID-19 pandemic in higher education,†Bull. Electr. Eng. Inform., vol. 11, no. 6, pp. 3598–3608, Dec. 2022, doi: 10.11591/eei.v11i6.4035.
[47] A. I. Adekitan and O. Salau, “Toward an improved learning process: the relevance of ethnicity to data mining prediction of students’ performance,†SN Appl. Sci., vol. 2, no. 1, p. 8, Jan. 2020, doi: 10.1007/s42452-019-1752-1.
[48] A. Al-Azawei and M. A. A. Al-Masoudy, “Predicting Learners’ Performance in Virtual Learning Environment (VLE) based on Demographic, Behavioral and Engagement Antecedents,†Int. J. Emerg. Technol. Learn. IJET, vol. 15, no. 09, p. 60, May 2020, doi: 10.3991/ijet.v15i09.12691.
[49] Y. He et al., “Online At-Risk Student Identification using RNN-GRU Joint Neural Networks,†Information, vol. 11, no. 10, p. 474, Oct. 2020, doi: 10.3390/info11100474.
[50] 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, p. 354, Jan. 2020, doi: 10.3390/app10010354.
[51] H. A. Mengash, “Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems,†IEEE Access, vol. 8, pp. 55462–55470, 2020, doi: 10.1109/ACCESS.2020.2981905.
[52] M. Injadat, A. Moubayed, A. B. Nassif, and A. Shami, “Multi-split optimized bagging ensemble model selection for multi-class educational data mining,†Appl. Intell., vol. 50, no. 12, pp. 4506–4528, Dec. 2020, doi: 10.1007/s10489-020-01776-3.
[53] I. Smirnov, “Estimating educational outcomes from students’ short texts on social media,†EPJ Data Sci., vol. 9, no. 1, p. 27, Dec. 2020, doi: 10.1140/epjds/s13688-020-00245-8.
[54] A. Esteban, C. Romero, and A. Zafra, “Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses,†Appl. Sci., vol. 11, no. 21, p. 10145, Oct. 2021, doi: 10.3390/app112110145.
[55] P. Hai tao et al., “Predicting academic performance of students in Chinese-foreign cooperation in running schools with graph convolutional network,†Neural Comput. Appl., vol. 33, no. 2, pp. 637–645, Jan. 2021, doi: 10.1007/s00521-020-05045-9.
[56] P. Nuankaew, P. Nasa ngium, and W. S. Nuankaew, “Application for Identifying Students Achievement Prediction Model in Tertiary Education: Learning Strategies for Lifelong Learning,†Int. J. Interact. Mob. Technol. IJIM, vol. 15, no. 22, p. 22, Nov. 2021, doi: 10.3991/ijim.v15i22.24069.
[57] J. Kabathova and M. Drlik, “Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques,†Appl. Sci., vol. 11, no. 7, p. 3130, Apr. 2021, doi: 10.3390/app11073130.
[58] B. K. Yousafzai et al., “Student-Performulator: Student Academic Performance Using Hybrid Deep Neural Network,†Sustainability, vol. 13, no. 17, p. 9775, Aug. 2021, doi: 10.3390/su13179775.
[59] Y. Baashar et al., “Evaluation of postgraduate academic performance using artificial intelligence models,†Alex. Eng. J., vol. 61, no. 12, pp. 9867–9878, Dec. 2022, doi: 10.1016/j.aej.2022.03.021.
[60] E. Çakıt and M. Dağdeviren, “Predicting the percentage of student placement: A comparative study of machine learning algorithms,†Educ. Inf. Technol., vol. 27, no. 1, pp. 997–1022, Jan. 2022, doi: 10.1007/s10639-021-10655-4.
[61] C. Liu, H. Wang, and Z. Yuan, “A Method for Predicting the Academic Performances of College Students Based on Education System Data,†Mathematics, vol. 10, no. 20, p. 3737, Oct. 2022, doi: 10.3390/math10203737.
[62] G. Feng, M. Fan, and C. Ao, “Exploration and Visualization of Learning Behavior Patterns From the Perspective of Educational Process Mining,†IEEE Access, vol. 10, pp. 65271–65283, 2022, doi: 10.1109/ACCESS.2022.3184111.
[63] S. Gaftandzhieva et al., “Exploring Online Activities to Predict the Final Grade of Student,†Mathematics, vol. 10, no. 20, p. 3758, Oct. 2022, doi: 10.3390/math10203758.
[64] S. Garmpis, M. Maragoudakis, and A. Garmpis, “Assisting Educational Analytics with AutoML Functionalities,†Computers, vol. 11, no. 6, p. 97, June 2022, doi: 10.3390/computers11060097.
[65] M. Yagcı, “Educational data mining: prediction of students’ academic performance using machine learning algorithms,†Smart Learn. Environ., vol. 9, no. 1, p. 11, Dec. 2022, doi: 10.1186/s40561-022-00192-z.
[66] M. Gollapalli et al., “SUNFIT: A Machine Learning-Based Sustainable University Field Training Framework for Higher Education,†MDPI Sustain., May 2023, doi: https://doi.org/10.3390/su15108057.
[67] S. Issaro and P. Wannapiroon, “Intelligent Student Relationship Management Platform with Machine Learning for Student Empowerment,†Int. J. Emerg. Technol. Learn. IJET, vol. 18, no. 04, pp. 66–87, Feb. 2023, doi: 10.3991/ijet.v18i04.32583.
[68] S. Atalla et al., “An Intelligent Recommendation System for Automating Academic Advising Based on Curriculum Analysis and Performance Modeling,†Mathematics, vol. 11, no. 5, p. 1098, Feb. 2023, doi: 10.3390/math11051098.
[69] R. Mehdi and M. Nachouki, “A neuro-fuzzy model for predicting and analyzing student graduation performance in computing programs,†Educ. Inf. Technol., vol. 28, no. 3, pp. 2455–2484, Mar. 2023, doi: 10.1007/s10639-022-11205-2.
[70] K. Qin, X. Xie, Q. He, and G. Deng, “Early Warning of Student Performance With Integration of Subjective and Objective Elements,†IEEE Access, vol. 11, pp. 72601–72617, 2023, doi: 10.1109/ACCESS.2023.3295580.
[71] Z. Fan and R. Chiong, “Identifying digital capabilities in university courses: An automated machine learning approach,†Educ. Inf. Technol., vol. 28, no. 4, pp. 3937–3952, Apr. 2023, doi: 10.1007/s10639-022-11075-8.
[72] T. S. Lopez, D. P. Perez, V. G. Ruiz, and L. F. Carvajal, “Implementation of Machine Learning Techniques and Creation of an Artificial Neural Network for The Prediction of The Academic Performance of Students in University Environments That use E-Learning and Streaming,†Dyna Publ., vol. 98, no. 3, pp. 282–287, May 2023, doi: 10.6036/10760.
[73] K. Wang, “Academic Early Warning Model for Students Based on Big Data Analysis,†Int. J. Emerg. Technol. Learn. IJET, vol. 18, no. 12, pp. 16–31, June 2023, doi: 10.3991/ijet.v18i12.41087.
[74] D. Sun et al., “A University Student Performance Prediction Model and Experiment Based on Multi-Feature Fusion and Attention Mechanism,†IEEE Access, vol. 11, pp. 112307–112319, 2023, doi: 10.1109/ACCESS.2023.3323365.
[75] N. U. R. Junejo et al., “SAPPNet: students’ academic performance prediction during COVID-19 using neural network,†Sci. Rep., vol. 14, no. 1, p. 24605, Oct. 2024, doi: 10.1038/s41598-024-75242-2.
[76] D. Khairy, N. Alharbi, M. A. Amasha, M. F. Areed, S. Alkhalaf, and R. A. Abougalala, “Prediction of student exam performance using data mining classification algorithms,†Educ. Inf. Technol., vol. 29, no. 16, pp. 21621–21645, Nov. 2024, doi: 10.1007/s10639-024-12619-w.
[77] J. A. I. S. Masood, N. S. Kalyan Chakravarthy, D. Asirvatham, M. Marjani, D. Abdulkareem Shafiq, and S. Nidamanuri, “A Hybrid Deep Learning Model to Predict High-Risk Students in Virtual Learning Environments,†IEEE Access, vol. 12, pp. 103687–103703, 2024, doi: 10.1109/ACCESS.2024.3434644.
[78] M. R. Borna, H. Saadat, A. T. Hojjati, and E. Akbari, “Analyzing click data with AI: implications for student performance prediction and learning assessment,†Front. Educ., vol. 9, p. 1421479, Dec. 2024, doi: 10.3389/feduc.2024.1421479.
[79] D. Monteverde Suárez et al., “Predicting students’ academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes,†BMC Med. Educ., vol. 24, no. 1, p. 74, Jan. 2024, doi: 10.1186/s12909-023-04918-6.
[80] M. Milicevic, B. Marinovic, and L. Jeftic, “Machine learning methods as auxiliary tool for effective mathematics teaching,†Comput. Appl. Eng. Educ., vol. 32, no. 6, p. e22787, Nov. 2024, doi: 10.1002/cae.22787.
[81] N. G. Cu, T. L. Nghiem, T. H. Ngo, M. T. L. Nguyen, and H. Q. Phung, “Increment of Academic Performance Prediction of Atâ€Risk Student by Dealing With Data Imbalance Problem,†Appl. Comput. Intell. Soft Comput., vol. 2024, no. 1, p. 4795606, Jan. 2024, doi: 10.1155/2024/4795606.
[82] M. S. Ramirez Montoya, R. Morales Menendez, M. Tworek, C. A. Escobar, R. Tariq, and G. C. Tenorio Sepulveda, “Complex competencies for leader education: artificial intelligence analysis in student achievement profiling,†Cogent Educ., vol. 11, no. 1, p. 2378508, Dec. 2024, doi: 10.1080/2331186X.2024.2378508.
[83] T. Zhang, Z. Zhong, W. Mao, Z. Zhang, and Z. Li, “A New Machine-Learning-Driven Grade-Point Average Prediction Approach for College Students Incorporating Psychological Evaluations in the Post-COVID-19 Era,†Electronics, vol. 13, no. 10, p. 1928, May 2024, doi: 10.3390/electronics13101928.
[84] M. A. Aslam, F. Murtaza, M. E. U. Haq, A. Yasin, and N. Ali, “SAPEx-D: A Comprehensive Dataset for Predictive Analytics in Personalized Education Using Machine Learning,†Data, vol. 10, no. 3, p. 27, Feb. 2025, doi: 10.3390/data10030027.
[85] B. Alnasyan, M. Basheri, M. Alassafi, and K. Alnasyan, “Kanformer: an attention-enhanced deep learning model for predicting student performance in virtual learning environments,†Soc. Netw. Anal. Min., vol. 15, no. 1, p. 25, Mar. 2025, doi: 10.1007/s13278-025-01446-7.
[86] T. Jin, “Methods and reliability study of moral education assessment in universities: A machine learning-based approach,†Alex. Eng. J., vol. 125, pp. 20–28, June 2025, doi: 10.1016/j.aej.2025.03.095.
[87] A. Kord, A. Aboelfetouh, and S. M. Shohieb, “Academic course planning recommendation and students’ performance prediction multi-modal based on educational data mining techniques,†J. Comput. High. Educ., Jan. 2025, doi: 10.1007/s12528-024-09426-0.
[88] L. S. Chen, T. T. Huynh Cam, V. C. Nguyen, T. C. Lu, and D. K. Le Huynh, “Predicting Early Employability of Vietnamese Graduates: Insights from Data-Driven Analysis Through Machine Learning Methods,†Big Data Cogn. Comput., vol. 9, no. 5, p. 134, May 2025, doi: 10.3390/bdcc9050134.
[89] S. E. Hooper, N. Ragland, and E. Artemiou, “Random forest models reveal academic and financial factors outweigh demographics in predicting completion of a year-round veterinary program,†J. Am. Vet. Med. Assoc., vol. 263, no. 2, pp. 1–9, Feb. 2025, doi: 10.2460/javma.24.08.0501.
Authors
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.