STUDENT PERFORMANCE PREDICTION USING MACHINE LEARNING ALGORITHMS
DOI:
https://doi.org/10.29121/shodhkosh.v5.i6.2024.4552Keywords:
Student Performance Prediction, Machine Learning, Educational Data Mining, Predictive Analytics, Data-Driven Decision MakingAbstract [English]
The accurate prediction of student performance is a critical component in enhancing educational outcomes, enabling timely interventions, and personalizing learning experiences. This research paper investigates the application of various machine learning algorithms to predict student performance, addressing the limitations of traditional methods that often fail to handle large datasets and multiple variables effectively. By leveraging data from student academic records, attendance, and socio-economic factors, this study evaluates the efficacy of decision trees, random forests, support vector machines, and neural networks in identifying at-risk students. The methodology includes data preprocessing, model training, and rigorous evaluation using metrics such as accuracy, precision, recall, and F1 score. Cross-validation techniques ensure the robustness of the predictive models. The findings reveal that machine learning models, particularly random forests and neural networks, significantly outperform traditional methods in prediction accuracy. Key factors influencing student success, including attendance and socio-economic background, are identified, providing actionable insights for educators and policymakers. This study contributes to the field of educational data mining by offering a comprehensive analysis of machine learning applications in education and proposing a robust predictive model for practical implementation. The implications of this research highlight the potential of machine learning to revolutionize educational practices by enabling data-driven decision-making and fostering an environment conducive to student success. Future research directions include addressing model biases and exploring the integration of additional data sources to further enhance prediction accuracy.
References
L. Aulck, N. Velagapudi, J. Blumenstock, and J. West, “Predicting Student Dropout in Higher Education,” Jun. 2016, [Online]. Available: http://arxiv.org/abs/1606.06364
M. Delogu, R. Lagravinese, D. Paolini, and G. Resce, “Predicting dropout from higher education: Evidence from Italy,” Econ Model, vol. 130, Jan. 2024, doi: 10.1016/j.econmod.2023.106583. DOI: https://doi.org/10.1016/j.econmod.2023.106583
D. K. Dake and C. Buabeng-Andoh, “Using Machine Learning Techniques to Predict Learner Drop-out Rate in Higher Educational Institutions,” Mobile Information Systems, vol. 2022, 2022, doi: 10.1155/2022/2670562. DOI: https://doi.org/10.1155/2022/2670562
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.
I. Lykourentzou, I. Giannoukos, V. Nikolopoulos, G. Mpardis, and V. Loumos, “Dropout prediction in e-learning courses through the combination of machine learning techniques,” ComputEduc, vol. 53, no. 3, pp. 950–965, Nov. 2009, doi: 10.1016/j.compedu.2009.05.010. DOI: https://doi.org/10.1016/j.compedu.2009.05.010
S. B. Kotsiantis, C. J. Pierrakeas, and P. E. Pintelas, “Preventing student dropout in distance learning using machine learning techniques,” in Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), Springer Verlag, 2003, pp. 267–274. doi: 10.1007/978-3-540-45226-3_37. DOI: https://doi.org/10.1007/978-3-540-45226-3_37
B. Albreiki, N. Zaki, and H. Alashwal, “A systematic literature review of student’ performance prediction using machine learning techniques,” Educ Sci (Basel), vol. 11, no. 9, Sep. 2021, doi: 10.3390/educsci11090552. DOI: https://doi.org/10.3390/educsci11090552
F. Del Bonifro, M. Gabbrielli, G. Lisanti, and S. P. Zingaro, “Student dropout prediction,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2020, pp. 129–140. doi: 10.1007/978-3-030-52237-7_11. DOI: https://doi.org/10.1007/978-3-030-52237-7_11
F. Dalipi, A. S. Imran, and Z. Kastrati, “MOOC dropout prediction using machine learning techniques: Review and research challenges,” in IEEE Global Engineering Education Conference, EDUCON, IEEE Computer Society, May 2018, pp. 1007–1014. doi: 10.1109/EDUCON.2018.8363340. DOI: https://doi.org/10.1109/EDUCON.2018.8363340
D. Delen, B. Davazdahemami, and E. Rasouli Dezfouli, “Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework,” Information Systems Frontiers, vol. 26, no. 2, pp. 641–662, Apr. 2024, doi: 10.1007/s10796-023-10397-3. DOI: https://doi.org/10.1007/s10796-023-10397-3
M. Vaarma and H. Li, “Predicting student dropouts with machine learning: An empirical study in Finnish higher education,” Technol Soc, vol. 76, Mar. 2024, doi: 10.1016/j.techsoc.2024.102474. DOI: https://doi.org/10.1016/j.techsoc.2024.102474
D. Opazo, S. Moreno, E. Álvarez-Miranda, and J. Pereira, “Analysis of first-year university student dropout through machine learning models: A comparison between universities,” Mathematics, vol. 9, no. 20, Oct. 2021, doi: 10.3390/math9202599. DOI: https://doi.org/10.3390/math9202599
M. Segura, J. Mello, and A. Hernández, “Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role?,” Mathematics, vol. 10, no. 18, Sep. 2022, doi: 10.3390/math10183359. DOI: https://doi.org/10.3390/math10183359
P. Patel, T. Thakkar, M. Patel, and A. Trivedi, “International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING A Review: An Approach for Secondary School Students Performance using Machine Learning and Data Mining.” [Online]. Available: www.ijisae.org
A. Atel, S. Ascarenhas, A. Homas, and D. Arghese, “Student Performance Analysis And Prediction Of Employable Domains Using Machine Learning.” [Online]. Available: https://ssrn.com/abstract=3682499
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. DOI: https://doi.org/10.1109/ACCESS.2024.3361479
V. Nakhipovaet al., “Use of the Naive Bayes Classifier Algorithm in Machine Learning for Student Performance Prediction,” International Journal of Information and Education Technology, vol. 14, no. 1, pp. 92–98, 2024, doi: 10.18178/ijiet.2024.14.1.2028. DOI: https://doi.org/10.18178/ijiet.2024.14.1.2028
P. Guleria and M. Sood, “Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling,” Educ Inf Technol (Dordr), vol. 28, no. 1, pp. 1081–1116, Jan. 2023, doi: 10.1007/s10639-022-11221-2. DOI: https://doi.org/10.1007/s10639-022-11221-2
S. Balkhis Banu, K. Suresh Kumar, M. Rizvi, S. Kumar Rai, P. Rana, and A. Professor, “Towards A Framework for Performance Management and Machine Learning in A Higher Education Institution,” 2024. [Online]. Available: http://jier.orghttp://www.orcid.org/0000-0002-3912-3687http://jier.org
A. Trivedi, T. Thakkar, P. Patel, and M. Patel, “International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING Charting Futures: A Comprehensive Review of Guided Pathways in Undergraduate Programs for Career Selection using Machine Learning.” [Online]. Available: www.ijisae.org
“62.EPRA+JOURNALS+15589”.
J. Park, Y. Feng, and S. P. Jeong, “Developing an advanced prediction model for new employee turnover intention utilizing machine learning techniques,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-023-50593-4. DOI: https://doi.org/10.1038/s41598-023-50593-4
Z. Tang, A. Jain, and F. E. Colina, “A Comparative Study of Machine Learning Techniques for College Student Success Prediction,” Journal of Higher Education Theory and Practice, vol. 24, no. 1, pp. 101–116, Jan. 2024, doi: 10.33423/jhetp.v24i1.6764. DOI: https://doi.org/10.33423/jhetp.v24i1.6764
M. Bhushan, U. Verma, C. Garg, and A. Negi, “Machine Learning-Based Academic Result Prediction System,” International Journal of Software Innovation, vol. 12, no. 1, 2023, doi: 10.4018/IJSI.334715. DOI: https://doi.org/10.4018/IJSI.334715
B. Assiri, M. Bashraheel, and A. Alsuri, “Enhanced Student Admission Procedures at Universities Using Data Mining and Machine Learning Techniques,” Applied Sciences, vol. 14, no. 3, p. 1109, Jan. 2024, doi: 10.3390/app14031109. DOI: https://doi.org/10.3390/app14031109
M. Maphosa, W. Doorsamy, and B. Paul, “Improving Academic Advising in Engineering Education with Machine Learning Using a Real-World Dataset,” Algorithms, vol. 17, no. 2, Feb. 2024, doi: 10.3390/a17020085. DOI: https://doi.org/10.3390/a17020085
A. Zingoni, J. Taborri, and G. Calabrò, “A machine learning-based classification model to support university students with dyslexia with personalized tools and strategies,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-023-50879-7. DOI: https://doi.org/10.1038/s41598-023-50879-7
I. Alnomay, A. Alfadhly, and A. Alqarni, “A Comparative Analysis for GPA Prediction of Undergraduate Students Using Machine and Deep Learning,” International Journal of Information and Education Technology, vol. 14, no. 2, pp. 287–292, 2024, doi: 10.18178/ijiet.2024.14.2.2050. DOI: https://doi.org/10.18178/ijiet.2024.14.2.2050
L. Tan, F. Chen, and B. Wei, “Examining key capitals contributing to students’ science-related career expectations and their relationship patterns: A machine learning approach,” J Res Sci Teach, 2024, doi: 10.1002/tea.21939. DOI: https://doi.org/10.1002/tea.21939
S. Kotsiantis, C. Pierrakeas, and P. Pintelas, “Predicting students’ performance in distance learning using machine learning techniques,” Applied Artificial Intelligence, vol. 18, no. 5, pp. 411–426, May 2004, doi: 10.1080/08839510490442058. DOI: https://doi.org/10.1080/08839510490442058
L. Hickman, R. Saef, V. Ng, S. E. Woo, L. Tay, and N. Bosch, “Developing and evaluating language-based machine learning algorithms for inferring applicant personality in video interviews,” Human Resource Management Journal, vol. 34, no. 2, pp. 255–274, Apr. 2024, doi: 10.1111/1748-8583.12356. DOI: https://doi.org/10.1111/1748-8583.12356
Y. A. Alsariera, Y. Baashar, G. Alkawsi, A. Mustafa, A. A. Alkahtani, and N. Ali, “Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance,” Computational Intelligence and Neuroscience, vol. 2022. Hindawi Limited, 2022. doi: 10.1155/2022/4151487. DOI: https://doi.org/10.1155/2022/4151487
J. L. Rastrollo-Guerrero, J. A. Gómez-Pulido, and A. Durán-Domínguez, “Analyzing and predicting students’ performance by means of machine learning: A review,” Applied Sciences (Switzerland), vol. 10, no. 3. MDPI AG, Feb. 01, 2020. doi: 10.3390/app10031042. DOI: https://doi.org/10.3390/app10031042
M. Yağcı, “Educational data mining: prediction of students’ academic performance using machine learning algorithms,” Smart Learning Environments, vol. 9, no. 1, Dec. 2022, doi: 10.1186/s40561-022-00192-z. DOI: https://doi.org/10.1186/s40561-022-00192-z
S. Nanavaty and A. Khuteta, “International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING A Deep Learning Dive into Online Learning: Predicting Student Success with Interaction-Based Neural Networks.” [Online]. Available: www.ijisae.org
Y. Li, “DESIGN OF COMPUTER INFORMATION MANAGEMENT SYSTEM BASED ON MACHINE LEARNING ALGORITHMS,” Scalable Computing, vol. 25, no. 2, pp. 944–951, 2024, doi: 10.12694/scpe.v25i2.2615. DOI: https://doi.org/10.12694/scpe.v25i2.2615
K. Venkatachari, “LEVERAGING MACHINE LEARNING ALGORITHMS TO GAIN INSIGHTS INTO THE MINDSETS OF IT PROFESSIONALS IN MUMBAI,” 2024.
L. Yang, Q. Wang, B. Zheng, X. Li, X. Ma, and T. Wang, “ASSESSING DIGITAL TEACHING COMPETENCE: AN APPROACH FOR INTERNATIONAL CHINESE TEACHERS BASED ON DEEP LEARNING ALGORITHMS,” Scalable Computing, vol. 25, no. 1, pp. 495–509, 2024, doi: 10.12694/scpe.v25i1.2424. DOI: https://doi.org/10.12694/scpe.v25i1.2424
Chinenye Gbemisola Okatta, Funmilayo Aribidesi Ajayi, and Olufunke Olawale, “NAVIGATING THE FUTURE: INTEGRATING AI AND MACHINE LEARNING IN HR PRACTICES FOR A DIGITAL WORKFORCE,” Computer Science & IT Research Journal, vol. 5, no. 4, pp. 1008–1030, Apr. 2024, doi: 10.51594/csitrj.v5i4.1085. DOI: https://doi.org/10.51594/csitrj.v5i4.1085
N. Gurung, R. Hasan, ✉ Md, S. Gazi, and F. R. Chowdhury, “AI-Based Customer Churn Prediction Model for Business Markets in the USA: Exploring the Use of AI and Machine Learning Technologies in Preventing Customer Churn,” 2024, doi: 10.32996/jcsts.
Z. Ziyi, “Application of neural network algorithm based on sensor networks in performance evaluation simulation of rural teachers,” Measurement: Sensors, vol. 32, Apr. 2024, doi: 10.1016/j.measen.2024.101049. DOI: https://doi.org/10.1016/j.measen.2024.101049
K. Sankara Narayanan and A. Kumaravel, “International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING A Novel Chaotic Optimized Boost Long Short-Term Memory (COB-LSTM) Model for Students Academic Performance Prediction in Educational Sectors.” [Online]. Available: www.ijisae.org
“A_Reinforcement_Learning_Based_RecommendationSystem_to_Improve_Performance_of_Students_in_Outcome_Based_Education_Model”.
C. Grace and M. Garces, “International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING A AI based Model for Achieving High Reliability Faculty Performance Using Various Machine Learning Algorithms.” [Online]. Available: www.ijisae.org
T. Revandi and H. Gunawan, “JURNAL MEDIA INFORMATIKA BUDIDARMA Classification of Company Level Based on Student Competencies in Tracer Study 2022 using SVM and XGBoost Method,” 2024, doi: 10.30865/mib.v8i1.7237. DOI: https://doi.org/10.30865/mib.v8i1.7237
A. A. Imianvanet al., “Enhancing Job Recruitment Prediction through Supervised Learning and Structured Intelligent System: A Data Analytics Approach,” Journal of Advances in Mathematics and Computer Science, vol. 39, no. 2, pp. 72–88, Feb. 2024, doi: 10.9734/jamcs/2024/v39i21869. DOI: https://doi.org/10.9734/jamcs/2024/v39i21869
S. Ramos-Pulido, N. Hernández-Gress, and G. Torres-Delgado, “Exploring the Relationship between Career Satisfaction and University Learning Using Data Science Models,” Informatics, vol. 11, no. 1, Mar. 2024, doi: 10.3390/informatics11010006. DOI: https://doi.org/10.3390/informatics11010006
A. Villar and C. R. V. de Andrade, “Supervised machine learning algorithms for predicting student dropout and academic success: a comparative study,” Discover Artificial Intelligence, vol. 4, no. 1, Jan. 2024, doi: 10.1007/s44163-023-00079-z. DOI: https://doi.org/10.1007/s44163-023-00079-z
J. A. Idowu, “Debiasing Education Algorithms,” Int J ArtifIntellEduc, 2024, doi: 10.1007/s40593-023-00389-4. DOI: https://doi.org/10.1007/s40593-023-00389-4
G. Ibarra-Vazquez, M. S. Ramí¬rez-Montoya, and H. Terashima, “Gender prediction based on University students’ complex thinking competency: An analysis from machine learning approaches,” Educ Inf Technol (Dordr), vol. 29, no. 3, pp. 2721–2739, Feb. 2024, doi: 10.1007/s10639-023-11831-4. DOI: https://doi.org/10.1007/s10639-023-11831-4
M. Ouahi, S. Khoulji, and M. L. Kerkeb, “Analysis of Deep Learning Development Platforms and Their Applications in Sustainable Development within the Education Sector,” in E3S Web of Conferences, EDP Sciences, Jan. 2024. doi: 10.1051/e3sconf/202447700098. DOI: https://doi.org/10.1051/e3sconf/202447700098
D. Musleh et al., “Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students,” Big Data and Cognitive Computing, vol. 8, no. 3, Mar. 2024, doi: 10.3390/bdcc8030031. DOI: https://doi.org/10.3390/bdcc8030031
W. Forero-Corba and F. N. Bennasar, “Techniques and applications of Machine Learning and Artificial Intelligence in education: a systematic review,” RIED-RevistaIberoamericana de Educacion a Distancia, vol. 27, no. 1, pp. 209–253, Jan. 2024, doi: 10.5944/ried.27.1.37491. DOI: https://doi.org/10.5944/ried.27.1.37491
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