Predictive model based on artificial intelligence and contextual awareness to identify students at risk of dropping out of university in Panama
Introduction: This article presents the findings from the research on the “Analysis, design, and development of a context-aware intelligent system for university dropout prediction using a microservices architecture” conducted at the Technological University of Panama in 2024. The study focuses on creating a predictive model using artificial intelligence that integrates students’ academic, sociodemographic, and psychological context to identify students at risk of early dropout.
Problem: In Latin America, the educational system struggles with high dropout rates, particularly at the university level. The early years of university education are crucial, and dropping out during this period significantly impacts a country’s social, labor, and economic development.
Objective: The main goal of this research was to develop a context-aware predictive model using artificial intelligence to identify students at risk of dropping out at the Technological University of Panama. The model incorporates academic, socioeconomic, and psychological factors to predict dropout risks more accurately.
Methodology: The study follows the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology, starting with problem understanding and followed by the collection and processing of students’ academic, socioeconomic, and psychological data. Three machine learning models were implemented and evaluated: Logistic Regression, Random Forest, and Gradient Boosting. Each model was tested under different scenarios to identify the most effective one.
Results: The Random Forest model in Scenario 3 demonstrated the best performance, offering a strong balance between accuracy and generalization. This model was the most effective for predicting university dropouts based on the collected data.
Conclusion: The developed predictive model is a relevant and innovative tool that can help the Technological University of Panama identify students at risk of dropping out early. By using artificial intelligence and a context-aware approach, this tool can contribute to reducing dropout rates and improve student retention.
Originality: This research introduces a novel approach to university dropout prediction by integrating context-awareness with artificial intelligence. The multidimensional nature of the model, considering academic, sociodemographic, and psychological factors, sets it apart from traditional predictive models.
Limitations: One of the key limitations of this research was the restricted availability of real psychological data, which limited the comprehensiveness of the model.
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[1] P. A. Flores Mora, “Abordando el Desafío de la Deserción: una revisión sistemática de la Deserción en Instituciones de Educación Superior,” Ciencia Latina Revista Científica Multidisciplinar, vol. 8, no. 1, pp. 9813–9823, Apr. 2024. doi: 10.37811/cl_rcm.v8i2.10299
[2] E. Himmel, “Modelos de Análisis de la deserción estudiantil en la educación superior,” Calidad en la Educación, pp. 1, 2002
[3] I. Quintero, “Análisis de las causas de deserción universitaria,” Bogotá, Colombia, 2016
[4] K. S. Selim and S. S. Rezk, “On predicting school dropouts in Egypt: A machine learning approach,” Educ Inf Technol (Dordr), vol. 28, no. 7, pp. 9235–9266, Jul. 2023. doi: 10.1007/s10639-022-11571-x
[5] A. F. Núñez-Naranjo, “Analysis of the determinant factors in university dropout: a case study of Ecuador,” Front Educ (Lausanne), vol. 9, p. 1444534, Oct. 2024. doi: 10.3389/FEDUC.2024.1444534/BIBTEX
[6] World Bank, “Acting Now to Protect the Human Capital of Our Children: The Costs of and Response to COVID-19 Pandemic’s Impact on the Education Sector in Latin America and the Caribbean,” 2021. [Online]. Available: www.worldbank.org
[7] P. Díaz and A. Tejedor, “Programas de análisis y prevención de la deserción estudiantil universitaria dirigidos al contexto panameño,” pp. 1, Panamá, 2018. doi: http://ridda2.utp.ac.pa/handle/123456789/5530
[8] L. Reisberg, “Diagnóstico de la educación superior en Panamá: Retos y Oportunidades,” Panamá, pp. 1, Mar. 2021. doi: http://dx.doi.org/10.18235/0003329
[9] A. A. Pérez Aguirre, M. De Gracia, M. Aguirre, I. Ordas, and A. Aranzazu, “LA DESERCIÓN EN LA EDUCACIÓN SUPERIOR EN PANAMÁ Y SUS CAUSAS,” Universidad Internacional de Ciencia y Tecnología, Jan. 2023, pp. 314–325. doi: 10.47300/actasidi-unicyt-2022-47
[10] N. Reyes and A. Meneses, “Una revisión crítica de los factores psicosociales asociados al abandono universitario en primer año,” pp. 1
[11] N. Chiarino et al., “Abandono y permanencia estudiantil en universidades de Latinoamérica y el Caribe: Una revisión sistemática mixta,” Actualidades Investigativas en Educación, vol. 24, no. 2, pp. 1–37, May 2024. doi: 10.15517/aie.v24i2.57306
[12] F. Sáez and J. Mella, “Revisión sistemática sobre intención de abandono en educación superior,” pp. 1
[13] G. Paramo and C. Correa, “Deserción estudiantil universitaria. Conceptualización,” Revista Universidad EAFIT Colombia, vol. 35, pp. 65–78, Jul. 2012
[14] M. Biancardi, “Complejidad del concepto de contexto,” 1998
[15] S. W. Nava-Díaz and G. Chavira, “Hacia los entornos conscientes del contexto: Un marco de trabajo para etiquetar consciencia del contexto,” Revista Colombiana de Computación, vol. 14, no. 2, pp. 61–78, Nov. 2013
[16] N. Díaz and S. S. M. Wilfrido, “Modelado de un ambiente inteligente: un entorno consciente del contexto a través del etiquetado,” pp. 1, 2012. [Online]. Available: https://api.semanticscholar.org/CorpusID:177669972
[17] T. Mitchell, Machine Learning, pp. 1, vol. 1. 1997
[18] P. Mussida and P. L. Lanzi, “A computational tool for engineer dropout prediction,” in IEEE Global Engineering Education Conference, EDUCON, IEEE Computer Society, 2022, pp. 1571–1576. doi: 10.1109/EDUCON52537.2022.9766632
[19] E. Cevallos Medina, C. Barahona Chunga, J. Armas-Aguirre, and E. Gradón, “Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees,” 5th Iberian Conference on Information Systems and Technologies (CISTI), vol. 15, pp. 1–7, Jun. 2020. doi: 10.23919/CISTI49556.2020.9141095
[20] S. Merchán Rubiano, A. Beltrán Gómez, and J. Duarte García, “Engineering Students’ Academic Performance Prediction using ICFES Test Scores and Demographic Data,” Ingeniería Solidaria, vol. 13, no. 21, pp. 53–61, Jan. 2017. doi: 10.16925/in.v13i21.1729
[21] Y. Zheng, Z. Gao, Y. Wang, and Q. Fu, “MOOC Dropout Prediction Using FWTS-CNN Model Based on Fused Feature Weighting and Time Series,” IEEE Access, vol. 8, pp. 225324–225335, 2020. doi: 10.1109/ACCESS.2020.3045157
[22] Á. Rabelo Lopes, R. De Sousa, D. Carvalho, and R. Vaccare, “Context-aware Ubiquitous Learning Literature Systematic Mapping on Ubiquitous Learning Environments,” International Symposium on Computers in Education (SIIE), pp. 1–6, 2017. doi: 10.1109/SIIE.2017.8259662
[23] Á. R. Lopes, D. C. de Oliveira, R. C. de Sousa Aguiar, and R. T. Vaccare Braga, “Context-aware ubiquitous learning: Literature systematic mapping on ubiquitous learning environments,” in 2017 International Symposium on Computers in Education (SIIE), 2017, pp. 1–6. doi: 10.1109/SIIE.2017.8259662
[24] Z. Yu, X. Zhou, and L. Shu, “Towards a Semantic Infrastructure for Context-Aware e-Learning,” Multimedia Tools Appl., vol. 47, no. 1, pp. 71–86, Mar. 2010. doi: 10.1007/s11042-009-0407-4
[25] P. Chapman, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth, “CRISP-DM 1.0 Step-by-step data mining guide,” DaimlerChrysler, pp. 1, 2000
[26] “Dirección General de Tecnología de la Información y Comunicaciones | Universidad Tecnológica de Panamá,” pp. 1, Accessed: Jun. 22, 2024. [Online]. Available: https://utp.ac.pa/direccion-general-de-tecnologia-de-la-informacion-y-comunicaciones
[27] “Dirección de Bienestar Estudiantil | Universidad Tecnológica de Panamá,” Accessed: Jun. 22, 2024. [Online]. Available: https://utp.ac.pa/direccion-de-bienestar-estudiantil
[28] “Dirección de Orientación Psicológica | Universidad Tecnológica de Panamá,” Accessed: Jun. 22, 2024. [Online]. Available: https://utp.ac.pa/direccion-de-orientacion-psicologica
[29] I. Hodashinsky and R. Ostapenko, “Extracting Fuzzy Classifier Rules from Mixed Continuous and Categorical Data,” in 2024 X International Conference on Information Technology and Nanotechnology (ITNT), 2024, pp. 1–6. doi: 10.1109/ITNT60778.2024.10582321
[30] A. Taha and O. M. Hegazy, “A proposed outliers identification algorithm for categorical data sets,” in 2010 The 7th International Conference on Informatics and Systems (INFOS), 2010, pp. 1–5
[31] R. Pavithrakannan, N. B. Fenn, S. Raman, V. Kalyanaraman, V. K. Murugananthan, and J. Janarthanan, “Imputation Analysis of Central Tendencies for Classification,” in 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), 2021, pp. 1–7. doi: 10.1109/IEMTRONICS52119.2021.9422507
[32] S. Guo, X. Wu, and Y. Li, “Deriving Private Information from Perturbed Data Using IQR Based Approach,” in 22nd International Conference on Data Engineering Workshops (ICDEW’06), 2006, p. 92. doi: 10.1109/ICDEW.2006.47
[33] A. Sadhu, R. Soni, and M. Mishra, “Pattern-based Comparative Analysis of Techniques for Missing Value Imputation,” in 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), 2020, pp. 513–518. doi: 10.1109/ICCCA49541.2020.9250825
[34] J. Yu, Y. He, and J. Z. Huang, “A Two-Stage Missing Value Imputation Method Based on Autoencoder Neural Network,” in 2021 IEEE International Conference on Big Data (Big Data), 2021, pp. 6064–6066. doi: 10.1109/BigData52589.2021.9671338
[35] M. S. Santos, R. C. Pereira, A. F. Costa, J. P. Soares, J. Santos, and P. H. Abreu, “Generating Synthetic Missing Data: A Review by Missing Mechanism,” IEEE Access, vol. 7, pp. 11651–11667, 2019. doi: 10.1109/ACCESS.2019.2891360
[36] “Tratamiento de valores vacíos II con R y Python: Estrategias de imputación estadística (moda, mediana y media). | by Nicolás Urrego | Medium,” pp. 1, Accessed: Sep. 14, 2024. [Online]. Available: https://nicolasurrego.medium.com/tratamiento-de-valores-vac%C3%ADos-ii-estrategias-de-imputaci%C3%B3n-estad%C3%ADstica-moda-mediana-y-media-2665b0f53a4c
[37] H. Chen et al., “Personal-Zscore: Eliminating Individual Difference for EEG-Based Cross-Subject Emotion Recognition,” IEEE Trans. Affect. Comput., vol. 14, no. 3, pp. 2077–2088, 2023. doi: 10.1109/TAFFC.2021.3137857
[38] V. Gajera, Shubham, R. Gupta, and P. K. Jana, “An effective Multi-Objective task scheduling algorithm using Min-Max normalization in cloud computing,” in 2016 2nd Int. Conf. Applied and Theoretical Computing and Communication Technology (iCATccT), 2016, pp. 812–816. doi: 10.1109/ICATCCT.2016.7912111
[39] A. Aich, A. Krishna, V. Akhilesh, and C. Hegde, “Encoding Web-based Data for Efficient Storage in Machine Learning Applications,” in 2019 Fifteenth Int. Conf. Information Processing (ICINPRO), 2019, pp. 1–6. doi: 10.1109/ICInPro47689.2019.9092264
[40] B. Sun, J. Wu, C. Guo, and K. Chen, “A One-Hot Encoding Approach for Signal Integrity Enhancement of Intra-chip Interconnects,” in 2024 25th Int. Conf. Electronic Packaging Technology (ICEPT), 2024, pp. 1–6. doi: 10.1109/ICEPT63120.2024.10668712
[41] B. Ujkani, D. Minkovska, and L. Stoyanova, “Application of Logistic Regression Technique for Predicting Student Dropout,” in 2022 31st Int. Scientific Conf. Electronics, ET 2022, IEEE, 2022. doi: 10.1109/ET55967.2022.9920280
[42] L. Breiman, “Random Forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001. doi: 10.1023/A:1010933404324
[43] E. Cruz, M. González, and J. C. Rangel, “Técnicas de machine learning aplicadas a la evaluación del rendimiento y a la predicción de la deserción de estudiantes universitarios, una revisión,” Prisma Tecnológico, vol. 13, no. 1, pp. 77–87, Feb. 2022. doi: 10.33412/pri.v13.1.3039
[44] R. Blagus and L. Lusa, “SMOTE for high-dimensional class-imbalanced data,” BMC Bioinformatics, vol. 14, p. 106, Mar. 2013. doi: 10.1186/1471-2105-14-106
[45] N. Awasthi, P. R. Gautam, and A. K. Sharma, “RFECV-DT: Recursive Feature Selection with Cross Validation using Decision Tree based Android Malware Detection,” in 2024 15th Int. Conf. Computing Communication and Networking Technologies (ICCCNT), 2024, pp. 1–6. doi: 10.1109/ICCCNT61001.2024.10725127
[46] V. F. Ochkov, A. Stevens, and A. I. Tikhonov, “Jupyter Notebook, JupyterLab – Integrated Environment for STEM Education,” in 2022 VI Int. Conf. Information Technologies in Engineering Education (Inforino), 2022, pp. 1–5. doi: 10.1109/Inforino53888.2022.9782924
[47] P. Prathanrat and C. Polprasert, “Performance Prediction of Jupyter Notebook in JupyterHub using Machine Learning,” in 2018 Int. Conf. Intelligent Informatics and Biomedical Sciences (ICIIBMS), 2018, pp. 157–162. doi: 10.1109/ICIIBMS.2018.8550030
[48] F. B. Fava et al., “Assessing the Performance of Docker in Docker Containers for Microservice-Based Architectures,” in 2024 32nd Euromicro Int. Conf. Parallel, Distributed and Network-Based Processing (PDP), 2024, pp. 137–142. doi: 10.1109/PDP62718.2024.00026
[49] A. Suryanarayanan, A. Chala, L. Xu, G. Shobha, J. Shetty, and R. Dev, “Design and Implementation of Machine Learning Evaluation Metrics on HPCC Systems,” in 2019 4th Int. Conf. Computational Systems and Information Technology for Sustainable Solution (CSITSS), 2019, pp. 1–7. doi: 10.1109/CSITSS47250.2019.9031056
[50] M. F. Uddin, “Addressing Accuracy Paradox Using Enhanched Weighted Performance Metric in Machine Learning,” in 2019 Sixth HCT Information Technology Trends (ITT), 2019, pp. 319–324. doi: 10.1109/ITT48889.2019.9075071
