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Publications

  • Call on Student Dropout
  • Call on Competency-based Higher Education
  • Call on Leaders of Tomorrow
  • Living Lab Projects
  • IFE Data Hub Private Collections
  • IFE LL&DH Publications

More information about this call:
"Bringing New Solutions to the Challenges of Predicting and Countering Student Dropout in Higher Education"

Related publications:

  1. 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
     
  2. 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. https://www.researchgate.net/publication/369170217_An_Artificial_Neural_Network_Approach_to_Analyze_Students%27_Dropout_in_Higher_Education
     
  3. 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
     
  4. 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
     
  5. Gonzalez-Nucamendi, A., Noguez, J., Neri, L., Robledo-Rella, V., & Guadalupe García-Castelán, R. M. (2023). Analysis of Categorical and Numerical Variables for Dropout Intervention in Educational Settings. 2023 IEEE Frontiers in Education Conference (FIE), 1–8. https://doi.org/10.1109/FIE58773.2023.10343519
     
  6. 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), 376–381. https://doi.org/10.1109/UBMK59864.2023.10286747
     
  7. 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
     
  8. 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), 68–70. https://www.solaresearch.org/wp-content/uploads/2023/03/LAK23_CompanionProceedings.pdf
     
  9. 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 Nature Singapore. https://doi.org/10.1007/978-981-97-1814-6_77
     
  10. 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 Nature Singapore. https://doi.org/10.1007/978-981-97-1814-6_82

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

Related publications:

  1. 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
     
  2. 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
     
  3. 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
     
  4. 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

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

Related publications:

  1. 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. 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, 1–8. https://doi.org/10.1109/IEEECONF56852.2023.10104873

  2. Torres-Rodríguez, S.N., Morán-Mirabal, L.F., and Ruiz-Ramírez, J.A. (in press). Analysis of collaborative work through conversational patterns. In COMPENG2024: 2024 IEEE Workshop on Complexity in Engineering, p. to appear. https://doi.org/10.1109/COMPENG60905.2024.10741512

  3. Pérez-Gómez, L.J., Morán-Mirabal, L.F., and Ceballos, H. (in press). Assessing learners concentration using a low-cost portable EEG. In IFE Conference 2025: Empowering the Future of Education with Innovation and Technologies, in press, p. to appear.

  4. Hernandez, D., Flores-Vazquez, M., Hernandez-Mena, C., Olivares-Avalos, M., Coutinho, G., Reyes Avendaño, J., Dias, V., and Morán-Mirabal, L.F. (in press). Digital shadow as a didactic resource for control engineering. In IFE Conference 2025: Empowering the Future of Education with Innovation and Technologies, in press, p. to appear.

  5. Serrano-Heredia, A., García-Chitiva, M.P., Camacho-Zúñiga, C., Morán-Mirabal, L.F., and Vázquez-Villegas, P. (in press). Remediation of Mathematics Knowledge in Engineering Students through an AI-Based Self-Study Educational Intervention. In Proceedings of the 205 IEEE Global Engineering Education Conference, EDUCON 2025. IEEE Computer Society, p. to appear.

  6. Morán-Mirabal, L.F., Ruiz-Ramírez, J.A., González-Grez, A.A., Torres-Rodríguez, S.N. and Ceballos, H.G. (in press). Applying the Living Lab Methodology for Evidence-Based Educational Technologies. In Proceedings of the 205 IEEE Global Engineering Education Conference, EDUCON 2025. IEEE Computer Society, p. to appear.

  1. 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
     
  2. 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
     
  3. 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
     
  4. 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
  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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