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Imagine evaluating the effectiveness of educational technologies or academic performance using pre-existing databases, which would allow access to detailed and comparative information for more accurate and informed results.
The IFE Data Hub, an initiative of the Institute for the Future of Education (IFE) provides access to institutional data collections for Tecnológico de Monterrey members and external collaborators, including those from national and international institutions. It also offers open data for researchers in educational innovation globally, enabling members to share their data through the institution’s Open Data Portal.
Through its calls for proposals, the IFE unit has provided datasets that facilitate various analyses and research. One of them launched a couple of years ago, included the “Higher Education Competency Dataset Based on the TEC21 Educational Model of Tecnologico de Monterrey,” which contains anonymized information on students who studied at least one semester under the TEC21 Educational Model between August 2019 and June 2022. The data includes sociodemographic, academic, subject, and disciplinary and transversal competency information.
The participating researchers were provided with a data dictionary that included a brief description of 45 variables comprising the dataset, along with their corresponding data types. The records were grouped into two types of competencies:
- Disciplinary competencies are based on specific knowledge in the student’s specialization areas of training, encompassing knowing, knowing how to do, knowing how to be, and knowing how to collaborate.
- Transversal skills are general skills that complement performance in the disciplines and are not exclusive to a particular specialization.
Supported by an academic council, a 2023 call for proposals focused on specific approaches to identifying activities and evidence that impact student learning. The objective was to provide university professors and department heads with tools for designing didactic strategies for the competencies to be developed in educational programs. This included an analysis by program of the transversal and disciplinary competencies of each subject, the progress in developing completed and uncompleted competencies, the training activities and their impact, and the relationship between skills and the labor market.
Joanna Alvarado Uribe and Paola Mejía, leader and coordinator of data operations at the Data Hub, respectively, explained that the purpose of the call was to analyze the development of student competencies under Tecnológico de Monterrey’s Tec21 Educational Model, applying statistical, artificial intelligence, and visualization techniques.
Likewise, this call promoted the expansion of research on the competencies required by Industry, thus offering a solid basis for advancing the analysis of competencies in higher education and understanding their relationship with professional development.
Under these criteria, several studies were prepared from access to the database, which resulted in five publications:
- Mejia-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. Educ. Sci. 2024, 14, 30. https://doi.org/10.3390/educsci14010030
- Talamás-Carvajal, J. A., Ceballos, H. G., & Ramirez-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
- 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), еp498. https://doi.org/10.30935/cedtech/14288
- 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 Tecnológico de Monterrey. Frontiers in Education, 9, 1415755. https://doi.org/10.3389/feduc.2024.1415755
- 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 Education, 9. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2024.1426183
High-quality academic data for educational research at your fingertips
The Data Hub has brought together managers, administrators, professors, researchers, and even students who have participated as training partners, posing challenges using this data and presenting the findings of the research derived from the call, such as the presentation by now-graduated Guillermo Tafoya. In collaboration with the Departments of Educational Technology and School Management at Tecnológico de Monterrey, this event presented various analyses of competencies achieved under the Tec21 Educational Model.
Some of the most significant contributions were made by researchers who observed a progressive development in sustainability competencies among the university’s STEM (science, technology, engineering, and mathematics) students, who had been continuously evaluated throughout their careers. At the end of the sixth semester, students were assessed on an average of 21 competencies related to sustainability, according to Danilo Valdés Ramírez. Likewise, Carolina Alcántar Niebla and Leonardo Glasserman Morales identified that gender and previous academic performance are determining factors in developing digital skills. They emphasized the need to conduct studies that consider other factors, such as access to technology and prior training in digital tools.
Disciplinary competencies are crucial in Biotechnology Engineering programs. Moreover, the modifications to the Tecnológico de Monterrey curriculum, based on the analysis of these skills, had a positive impact on other engineering careers, improving the development of practical skills, said Jorge Membrillo. José Martín Molina also revealed that academic performance, as measured by the final grade, is the strongest predictor of the acquisition of significant employability skills, such as reasoning for complexity and digital transformation.
Regarding critical thinking, Juan Andrés Talamás noted that this skill is essential for the development of other complex-thinking competencies, such as scientific and systemic thinking. Additionally, he emphasized that developing this sub-competency had a positive impact on the development of related skills. Meanwhile, Sabur Butt underscored the importance of language models and artificial intelligence to extract and classify skills efficiently, especially in the context of digital transformation. Machine learning models that improve skills classification facilitate the development of tools to generate automatic feedback for teachers, which enhances teaching based on students’ comments.
Finally, Patricia Caratozzolo Martelliti, assisted by her team, identified the competencies and skills that will be decisive in the future labor markets, with a focus on Industry 4.0. Creating an open-source platform to help academia and industry make informed decisions about the skills required for the workforce, they utilized natural language processing techniques to build KSA taxonomies (knowledge, skills, and attitudes) that enhance alignment between labor market demands and academic backgrounds.
These results provided relevant information for improving teaching and developing competency, meeting both academic and professional needs. They also significantly contribute to enriching academic training across various disciplines, ensuring better alignment with the competencies required by industry, sustainability, and digital transformation.
Thus, the IFE Data Hub provides substantial datasets for accurate and evidence-based analyses. These data enable the comparison of trends, evaluation of the impact of educational approaches, optimization of resources, and facilitation of collaboration among researchers. Additionally, they facilitate the identification of gaps between academic training and the labor market’s needs, helping to align education with the competencies required by Industry.
Do you want to conduct research that will drive significant improvements in teaching and skills development? Learn more here.
Translation by: Daniel Wetta
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This article from Observatory of the Institute for the Future of Education may be shared under the terms of the license CC BY-NC-SA 4.0 
