ACADEMIC INTELLIGENCE: DATA, TECHNOLOGY, AND EDUCATIONAL TOURISM IN SYNERGY
Article Sidebar
Main Article Content
Abstract
The digital transformation of higher education institutions requires intelligent tools that support data-driven academic decision-making. The ADAPTE project applies statistical modelling and interactive dashboards to enhance institutional planning and promote academic success. Developed using anonymised data from a public higher education institution in the field of Tourism, the system provides accessible analytical resources for faculty members and management bodies. By identifying risk patterns associated with dropout and academic performance, ADAPTE enables more efficient management of study programmes, human resources, and strategic planning processes. Although implemented within a hospitality and tourism education context, the project demonstrates broader potential within the domain of educational tourism, by fostering stronger alignment between learning experiences and data related to students’ academic pathways. This contribution presents the current stage of ADAPTE’s implementation and illustrates how applied information technologies can support the development of a data-oriented educational culture, where academic trajectories meet intelligent analytical and predictive systems.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in e3 – Journal of Economics, Business and Entrepreneurship in the community of Portuguese-speaking countries are licensed under the Creative Commons Attribution (CC BY) license.
This license allows users to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, including commercial use, provided that appropriate credit is given to the original author(s) and the journal, a link to the license is provided, and any changes made are indicated.
References
Brito, M., Medeiros, F., & Bezerra, E. (2019, July). A report-type plugin to indicate dropout risk in the virtual learning environment Moodle. In 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT) (Vol. 2161, pp. 127–128). IEEE.
Bustamante, D., & Garcia-Bedoya, O. (2021). Predictive academic performance model to support, prevent and decrease the university dropout rate. In Applied informatics: Fourth international conference, ICAI 2021, Buenos Aires, Argentina, October 28–30, 2021, proceedings (pp. 222–236). Springer.
Cechinel, C., Dos Santos, M. D. F., Barrozo, C., Schardosim, J. E., de Vila, E., Ramos, V., ... Queiroga, E. M. (2021, October). A learning analytics dashboard for Moodle: Implementing machine learning techniques to early detect students at risk of failure. In 2021 XVI Latin American Conference on Learning Technologies (LACLO) (pp. 130–136). IEEE.
Chytas, K., Tsolakidis, A., Triperina, E., & Skourlas, C. (2023). Educational data mining in the academic setting: Employing the data produced by blended learning to ameliorate the learning process. Data Technologies and Applications, 57(3), 366–384.
Chytas, K., Tsolakidis, A., Triperina, E., Karanikolas, N. N., & Skourlas, C. (2023). Academic data derived from a university e-government analytic platform: An educational data mining approach. Data in Brief, 49, 109357.
Chytas, K., Tsolakidis, A., Triperina, E., Karanikolas, N. N., & Skourlas, C. (2023, September). An integrated platform for educational and research management using institutional digital resources. In Novel & intelligent digital systems conferences (pp. 266–276). Springer Nature Switzerland.
Dobudko, T. V., Gorbatov, S. V., Hashim, W., & Maseleno, A. (2019). Functional monitoring and control in electronic information and educational environment. All Open Access, Bronze.
Félix, I. M., Ambrósio, A. P., Neves, P. S., Siqueira, J., & Brancher, J. D. (2017, April). Moodle Predicta: A data mining tool for student follow-up. In International conference on computer supported education (Vol. 2, pp. 339–346). SCITEPRESS.
Garcia-Peñalvo, F. J., Casado-Lumbreras, C., Colomo-Palacios, R., & Yadav, A. (2020). Smart learning. Applied Sciences, 10(19), 6964.
Lainjo, B. (2023). Mitigating academic institution dropout rates with predictive analytics algorithms. International Journal of Education, Teaching, and Social Sciences, 3(1), 29–49.
Luna, J. M., Castro, C., & Romero, C. (2017). MDM tool: A data mining framework integrated into Moodle. Computer Applications in Engineering Education, 25(1), 90–102.
Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., & García-Osorio, C. I. (2020). Monitoring students at the university: Design and application of a Moodle plugin. Applied Sciences, 10(10), 3469.
Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., Rodríguez-Díez, J. J., Rodríguez-Arribas, S., Díez-Pastor, J. F., & Ji, Y. P. (2021). Improve teaching with modalities and collaborative groups in an LMS: An analysis of monitoring using visualisation techniques. Journal of Computing in Higher Education, 33(3), 747–778.
Sáiz-Manzanares, M. C., Rodríguez-Díez, J. J., Díez-Pastor, J. F., Rodríguez-Arribas, S., Marticorena-Sánchez, R., & Ji, Y. P. (2021). Monitoring of student learning in learning management systems: An application of educational data mining techniques. Applied Sciences, 11(6), 2677.
Saqr, M., Fors, U., & Tedre, M. (2017). How learning analytics can early predict under-achieving students in a blended medical education course. Medical Teacher, 39(7), 757–767.
Zacharis, N. Z. (2015). A multivariate approach to predicting student outcomes in web-enabled blended learning courses. The Internet and Higher Education, 27, 44–53.