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  • Call on Student Dropout (11)
  • Call on Competency-based Higher Education
  • Call on Leaders of Tomorrow
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More information about this call:
"Bringing New Solutions to the Challenges of Predicting and Countering Student Dropout in Higher Education"

Data Descriptor:

Alvarado-Uribe, J., Mejía-Almada, P., Masetto Herrera, A. L., Molontay, R., Hilliger, I., Hegde, V., Montemayor Gallegos, J. E., Ramírez Díaz, R. A., & Ceballos, H. G. (2022). Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education. Data, 7(9), 119. https://doi.org/10.3390/data7090119

Related publications:

  1. Lopez, J., Lecca, N., & Castañeda, P. (2025). Predictive analysis of student dropout in higher education. Proceedings of the 2024 2nd International Conference on Information Education and Artificial Intelligence (pp. 303–309). Association for Computing Machinery. https://doi.org/10.1145/3724504.372455
  2. Maqueo-Huerta, A. M., Martínez-Gutiérrez, E. Y., Sariñana-Hernández, R. Z., Dileva-Charles, F. M., & Hernandez-Gress, N. (2025). Analysis of predictive factors in university dropout rates using data science techniques. In L. Martínez-Villaseñor & G. Ochoa-Ruiz (Eds.), Advances in computational intelligence (pp. 184–195). Springer. https://doi.org/10.1007/978-3-031-75540-8_13
  3. Sánchez-Arévalo, M. L., Ferro-Escobar, R., & Chaparro-Sierra, L. F. (2025). Analysis of academic dropout data to estimate relevant variables using computer tools at a higher education institution. Cultura Educación y Sociedad, 16(1), e5777. http://doi.org/10.17981/cultedusoc.16.1.2025.577
  4. Cruz-Netro, Z. G., Martínez-Maldonado, C. E., & Caballero-Morales, S. O. (2024). Exploring school dropout dynamics: A case study using self-organizing maps. In G. Rivera, W. Pedrycz, J. Moreno-Garcia, & J. P. Sánchez-Solís (Eds.), Innovative applications of artificial neural networks to data analytics and signal processing (pp. 1–15). Springer. https://doi.org/10.1007/978-3-031-69769-2_1
  5. Mestizo, F., Orozco, A., González, B., Santos, E., & Hernandez-Gress, N. (2024). Predicting university student dropout with extracurricular activities participation using machine learning models: A case study at Tecnológico de Monterrey. Research in Computing Science, 153(12), 107–118. https://rcs.cic.ipn.mx/2024_153_12/Predicting%20University%20Student%20Dropout%20with%20Extracurricular%20Activities.pdf
  6. Talamás-Carvajal, J. A. (2024). Research plan on the effects of interventions on dropout predictions for higher education institutions. In J. A. D. C. Gonçalves, J. L. S. D. M. Lima, J. P. Coelho, F. J. García-Peñalvo, & A. García-Holgado (Eds.), Proceedings of TEEM 2023 (pp. 790–799). Springer. https://doi.org/10.1007/978-981-97-1814-6_7
  7. Velarde-Camaqui, D., Peláez-Sánchez, I. C., & Viehmann, C. (2024). Unveiling success: An analysis of academic performance predictors in a private high school in Mexico through learning analytics. In J. A. D. C. Gonçalves, J. L. S. D. M. Lima, J. P. Coelho, F. J. García-Peñalvo, & A. García-Holgado (Eds.), Proceedings of TEEM 2023 (pp. 839–848). Springer. https://doi.org/10.1007/978-981-97-1814-6_82
  8. Gonzalez-Nucamendi, A., Noguez, J., Neri, L., Robledo-Rella, V., & García-Castelán, R. M. G. (2023). Analysis of categorical and numerical variables for dropout intervention in educational settings. 2023 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE. https://doi.org/10.1109/FIE58773.2023.10343519
  9. Gonzalez-Nucamendi, A., Noguez, J., Neri, L., Robledo-Rella, V., & García-Castelán, R. M. G. (2023). Predictive analytics study to determine undergraduate students at risk of dropout. Frontiers in Education, 8, 1244686. https://doi.org/10.3389/feduc.2023.1244686
  10. Karabacak, E. S., & Yaslan, Y. (2023). Comparison of machine learning methods for early detection of student dropouts. 2023 8th International Conference on Computer Science and Engineering (UBMK) (pp. 376–381). IEEE. https://doi.org/10.1109/UBMK59864.2023.10286747
  11. Kuz, A., & Morales, R. (2023). Ciencia de datos educativos y aprendizaje automático: Un caso de estudio sobre la deserción estudiantil universitaria en México. Education in the Knowledge Society (EKS), 24, e30080. https://doi.org/10.14201/eks.30080
  12. Rodríguez-Hernández, C. F., Musso, M., & Cascallar, E. (2023, March). An artificial neural network approach to analyze students' dropout in higher education. International Convention of Psychological Science (ICPS) 2023. ResearchGate. https://www.researchgate.net/publication/369170217_An_Artificial_Neural_Network_Approach_to_Analyze_Students%27_Dropout_in_Higher_Education
  13. Talamás-Carvajal, J. A. (2023). The middle-man between models and mentors: Using SHAP values to explain dropout prediction models in higher education. Companion Proceedings 13th International Conference on Learning Analytics & Knowledge (LAK23) (pp. 68–70). SOLAR. https://www.solaresearch.org/wp-content/uploads/2023/03/LAK23_CompanionProceedings.pdf
  14. Talamás-Carvajal, J. A., & Ceballos, H. G. (2023). A stacking ensemble machine learning method for early identification of students at risk of dropout. Education and Information Technologies, 28(9), 12169–12189. https://doi.org/10.1007/s10639-023-11682-z


 

 

More information about this call:
"Fostering the Analysis of Competency-based Higher Education"

Related publications:

  1. Quintana-Zaez, J. C., Vázquez-Villegas, P., & Valdes-Ramirez, D. (2025). Identifying key predictors of students' competency achievement using machine learning models: A bioengineering case study. IEEE Access, 13, 168855-168877. https://doi.org/10.1109/ACCESS.2025.3613250

  2. Solera, E., Menasalvas, E., Martín, M., Zorrilla, M., Valdés-Ramírez, D., Zavala, G., & Monroy, R. (2025). Evaluating Competency Development and Academic Outcomes: Insights from Six Semesters of Data-Driven Analysis. Education Sciences, 15(4), 513. https://doi.org/10.3390/educsci15040513

  3. Glasserman-Morales, L. D., Alcantar-Nieblas, C., & Sisto, M. I. (2024). Demographic and school factors associated with digital competences in higher education students. Contemporary Educational Technology, 16(2), ep498. https://doi.org/10.30935/cedtech/14288

  4. Molina-Espinosa, J. M., Suárez-Brito, P., Gutiérrez-Padilla, B., López-Caudana, E. O., & González-Mendoza, M. (2024). Academic performance as a driver for the development of reasoning for complexity and digital transformation competencies. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1426183

  5. Talamás-Carvajal, J. A., Ceballos, H. G., & Ramírez-Montoya, M.-S. (2024). Identification of Complex Thinking Related Competencies: The Building Blocks of Reasoning for Complexity. Journal of Learning Analytics, 11(1), 37–48. https://doi.org/10.18608/jla.2024.8079

  6. Valdes-Ramirez, D., De Armas Jacomino, L., Monroy, R., & Zavala, G. (2024). Assessing sustainability competencies in contemporary STEM higher education: A data-driven analysis at Tecnologico de Monterrey. Frontiers in Education, 9, 1415755. https://doi.org/10.3389/feduc.2024.1415755

  7. Mejía-Manzano, L. A., Vázquez-Villegas, P., Díaz-Arenas, I. E., Escalante-Vázquez, E. J., & Membrillo-Hernández, J. (2023). Disciplinary Competencies Overview of the First Cohorts of Undergraduate Students in the Biotechnology Engineering Program under the Tec 21 Model. Education Sciences, 14(1), 30.https://doi.org/10.3390/educsci14010030

More information about this call:
"Encouraging Research on Higher Education Students' Profiles with Social Commitment"

Related publications:

  1. Gonzalez-Nucamendi, A., Neri, L., García-Castelán, R. M. G., Robledo-Rella, V., Valverde-Rebaza, J., & Noguez, J. (2024). Impact of demographic and co-curricular factors on the academic success of students from low-income families: A scholarship program study. 2024 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE. https://doi.org/10.1109/FIE61694.2024.10893057

  2. Gonzalez-Nucamendi, A., Neri-Vitela, L. J., Robledo-Rella, V., García-Castelán, R. M. G., Noguez, J., & Valverde-Rebaza, J. C. (2024). A factor analysis of student motivations of the Tecnológico de Monterrey leaders of tomorrow scholarship. ICERI2024 Proceedings, 2886-2893. https://doi.org/10.21125/iceri.2024.0752

  3. Quintero-Gámez, L., Tariq, R., Sánchez-Escobedo, P., & Sanabria-Z, J. (2024). Data analytics and Artificial Neural Network framework to profile academic success: Case study. Cogent Education, 11(1), 2433807. https://doi.org/10.1080/2331186X.2024.2433807

  4. Ramirez-Montoya, M. S., Morales-Menendez, R., Tworek, M., Escobar, C. A., Tariq, R., & Tenorio-Sepulveda, G. C. (2024). Complex competencies for leader education: Artificial intelligence analysis in student achievement profiling. Cogent Education, 11(1), 2378508. https://doi.org/10.1080/2331186X.2024.2378508

  1. Coronel-Santos, M. A., Ruiz-Ramirez, J. A., Rodríguez-Macías, J. C., & Glasserman-Morales, D. L. (2025). Explanatory model of the attitudinal process towards entrepreneurship: A systematic literature review approach. The International Journal of Management Education, 23(2), 101152. https://doi.org/10.1016/j.ijme.2025.101152
  2. Glasserman-Morales, L. D., Ruiz-Ramirez, J. A., Pacheco-Velazquez, E. A., & Carlos-Arroyo, M. (2025). Gamified logistics simulation for the advancement of decision making and complex thinking in learning environments. 2025 Institute for the Future of Education Conference (IFE Conference) (pp. 1–6). IEEE. https://doi.org/10.1109/IFE63672.2025.11024667
  3. Heredia, A. S., García-Chitiva, M. del P., Camacho-Zúñiga, C., Morán-Mirabal, L. F., & Vázquez-Villegas, P. (2025). Remediation of mathematics knowledge in engineering students through an AI-based self-study educational intervention. 2025 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–8). IEEE. https://doi.org/10.1109/EDUCON62633.2025.11016455
  4. Hernandez, D., Flores-Vazquez, M., Hernandez-Mena, C., Olivares Avalos, M., Coutinho, G. S., Reyes-Avendaño, J. A., do Rego Dias, V., & Morán-Mirabal, L. F. (2025). Digital shadow as a didactic resource for control engineering. 2025 Institute for the Future of Education Conference (IFE Conference) (pp. 1–7). IEEE. https://doi.org/10.1109/IFE63672.2025.1102491
  5. Morán-Mirabal, L. F., Güemes-Frese, L. E., Favarony-Avila, M., Torres-Rodríguez, S. N., & Ruiz-Ramirez, J. A. (2025). NPFC-Test: A multimodal dataset from an interactive digital assessment using wearables and self-reports. Data, 10(7), 103. https://doi.org/10.3390/data10070103
  6. Morán-Mirabal, L. F., Ruiz-Ramírez, J. A., González-Grez, A. A., Torres-Rodríguez, S. N., & Ceballos, H. G. (2025). Applying the living lab methodology for evidence-based educational technologies. 2025 IEEE Global Engineering Education Conference (EDUCON) (pp. 1–9). IEEE. https://doi.org/10.1109/EDUCON62633.2025.11016413
  7. Tamayo-Preval, D., & Ruiz-Ramirez, J. A. (2025). The self-directed learning of English teachers at a Mexican teaching training college. 2025 Institute for the Future of Education Conference (IFE Conference) (pp. 1–9). IEEE. https://doi.org/10.1109/IFE63672.2025.11024847
  8. Torres-Rodríguez, S. N., Ruiz-Ramírez, J. A., Morán-Mirabal, L. F., & Castillo, R. D. (2025). Gamified simulators and EEG: Exploring the relationship between concentration and socio-emotional skills learning. 2025 IEEE Engineering Education World Conference (EDUNINE) (pp. 1–6). IEEE. https://doi.org/10.1109/EDUNINE62377.2025.10981399
  9. Ruiz-Ramirez, J. A., Ponce-Naranjo, D., Calderón-Gurubel, J. E., González-Díaz, K. A., López-Andrade, A. D., Rivera-Cerros, E. A., Martínez-Giorgetti, J. E., & Ramirez Moreno, M. A. (2024). Use of multimodal learning analytics and biometric data as a contribution to the development of pedagogical activities in entrepreneurship area. In J. A. de C. Gonçalves, J. L. S. de M. Lima, J. P. Coelho, F. J. García-Peñalvo, & A. García-Holgado (Eds.), Proceedings of TEEM 2023 (pp. 849–859). Springer. https://doi.org/10.1007/978-981-97-1814-6_83
  10. Torres-Rodríguez, S. N., Morán-Mirabal, L. F., & Ruiz-Ramírez, J. A. (2024). Analysis of collaborative work through conversational patterns. 2024 IEEE Workshop on Complexity in Engineering (pp. 1–6). IEEE. https://doi.org/10.1109/COMPENG60905.2024.10741512
  11. Assaf, N., & Morán-Mirabal, L. F. (2023). Instructional usability and learner-user experience assessment in a virtual reality educational milieu: A deductive tech-instructionality model from EdTech. 2023 Future of Educational Innovation-Workshop Series Data in Action (pp. 1–8). IEEE. https://doi.org/10.1109/IEEECONF56852.2023.10104873
  12. Morán-Mirabal, L. F., Alvarado-Uribe, J., & Ceballos, H. G. (2023). Using AI for educational research in multimodal learning analytics. In M. Cebral-Loureda, E. G. Rincón-Flores, & G. Sánchez-Ante (Eds.), What AI can do? Strengths and limitations of artificial intelligence (pp. 155–176). Chapman & Hall/CRC. https://doi.org/10.1201/b23345
  1. Talamás-Carvajal, J. A., Ceballos, H. G., & Hilliger, I. (2025). The facts behind the prophecy: Validating a methodology for identifying behavioural differences in higher education student subpopulations under intervention. Journal of Learning Analytics, 12(2), 211-223. https://doi.org/10.18608/jla.2025.8553
  2. Talamás-Carvajal, J. A., & Ceballos-Cancino, H. G. (2024). Use of SHAP values for identifying differences in behaviors for subpopulations under intervention. Joint Proceedings of LAK 2024 Workshops, 11. https://ceur-ws.org/Vol-3667/DS-LAK24_paper_4.pdf
  3. Butt, S., Mejía-Almada, P., Alvarado-Uribe, J., Ceballos, H. G., Sidorov, G., & Gelbukh, A. (2023). MF-SET: A Multitask Learning Framework for Student Evaluation of Teaching. In K. Arai (Ed.), Proceedings of the Future Technologies Conference (FTC) 2023, Volume 1 (Vol. 813, pp. 254–270). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-47454-5_20
  4. Gallardo, K., Butt, S., & Ceballos, H. (2023). Improvement of Teaching Competencies Training in Higher Education Faculty Based on Student Evaluations of Teaching and AI Systems. In A. Mesquita, A. Abreu, J. V. Carvalho, C. Santana, & C. H. P. De Mello (Eds.), Perspectives and Trends in Education and Technology (Vol. 366, pp. 555–563). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-5414-8_51
  5. Ramirez-Montoya, M. S., Ceballos, H. G., Martínez-Pérez, S., & Romero-Rodríguez, L. M. (2023). Impact of Teaching Workload on Scientific Productivity: Multidimensional Analysis in the Complexity of a Mexican Private University. Publications, 11(2), 27. https://doi.org/10.3390/publications11020027

 

 

  1. Bautista Godínez, T., Castañeda Garza, G., Pérez Mora, R., Ceballos, H. G., Luna De La Luz, V., Moreno-Salinas, J. G., Zavala-Sierra, I. R., Santos-Solórzano, R., Moreno Arellano, C. I., & Sánchez-Mendiola, M. (2024). Perspectives and Opportunities for Learning Analytics Integration: A Qualitative Study in Mexican Universities. Journal of Learning Analytics, 11(1), 49–66. https://doi.org/10.18608/jla.2024.8125
     
  2. Morán-Mirabal, L. F., & Alvarado-Uribe, J. (2023). Using AI for Educational Research in Multimodal Learning Analytics. In M. Cebral-Loureda, E. G. Rincón-Flores, & G. Sanchez-Ante (Eds.), What AI Can Do: Strengths and Limitations of Artificial Intelligence (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/b23345
     
  3. Castañeda-Garza, G., Ceballos Cancino, H. G., & Mejía Almada, P. G. (2023). Artificial Intelligence for Mental Health: A Review of AI Solutions and their Future. In M. Cebral-Loureda, E. G. Rincón-Flores, & G. Sánchez-Ante (Eds.), What AI Can Do? Strengths and Limitations of Artificial Intelligence (First edition., pp. 373–400). Chapman & Hall/CRC (Taylor & Francis Group). https://doi.org/10.1201/b23345
     
  4. Ceballos, H. G., Castañeda-Garza, G., Alvarado-Uribe, J., & Mejía-Almada, P. (2024). The Data Hub of the Institute for the Future of Education. Joint Proceedings of LAK 2024 Workshops, 33–36. https://ceur-ws.org/Vol-3667/DS-LAK24_paper_4.pdf
     
  5. Gallardo, K., Díaz-Méndez, R. E., González, J. A., Mejía-Almada, P. G., & Ceballos, H. G. (2023). Tailor-Made Nutrition Education for University Students through Data Science. 2023 Future of Educational Innovation-Workshop Series Data in Action, 1–7. https://doi.org/10.1109/IEEECONF56852.2023.10104772
     
  6. Hilliger, I., G. Ceballos, H., Maldonado-Mahauad, J., & Ferreira, R. (2024). Applications of Learning Analytics in Latin America. Journal of Learning Analytics, 11(1), 1–5. https://doi.org/10.18608/jla.2024.8409
     
  7. Pineda-Romero, V. V., Orozco-Mora, C. E., & Ceballos, H. G. (2023). Factors to improve online education: A study on the impact of COVID-19 on Delhi students. 2023 Future of Educational Innovation-Workshop Series Data in Action, 1–8. https://doi.org/10.1109/IEEECONF56852.2023.10104773
     
  8. Tonja, A., Balouchzahi, F., Butt, S., Kolesnikova, O., Ceballos, H., Gelbukh, A., & Solorio, T. (2024). NLP Progress in Indigenous Latin American Languages. In K. Duh, H. Gomez, & S. Bethard (Eds.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 6972–6987). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.naacl-long.385

  9. García-Baena, D., Balouchzahi, F., Butt, S., García-Cumbreras, M., Tonja, A., García-Díaz, J., Bozkurt, S., Chakravarthi, B., Ceballos, H., Valencia-García, R., Sidorov, G., Ureña-López, L., Gelbukh, A., & Jiménez-Zafra, S. (2024). Overview of HOPE at IberLEF 2024: Approaching Hope Speech Detection in Social Media from Two Perspectives, for Equality, Diversity and Inclusion and as Expectations. Procesamiento Del Lenguaje Natural, 73, 407-419. Recuperado de http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6627/4019

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