LANSE-MIRA: Módulo de Intervenções para Retenção Acadêmica
Resumo
Instituições de ensino superior enfrentam altas taxas de evasão que giram em torno de 66% no Brasil e 55% nos Estados Unidos da América. A Analítica de Aprendizagem pode ser usada para identificar alunos em risco, mas a lacuna entre previsão e ação ainda persiste. Este artigo apresenta o LANSE-MIRA, um módulo da plataforma LANSE que automatiza intervenções personalizadas via e-mail e WhatsApp. O sistema segmenta alunos por fatores de risco e gerencia campanhas baseadas em regras. Estudos anteriores demonstram que mensagens direcionadas aumentam a retenção de estudantes em até 22%. A arquitetura do LANSE-MIRA inclui testes A/B para otimização contínua, visando transformar LA em uma ferramenta proativa. Trabalhos futuros focam em intervenções adaptativas e IA Explicável (XAI) para maior personalização.
Referências
Bañeres, D., Guerrero-Roldán, A.-E., Rodríguez-González, M. E., and Karadeniz, A. (2021). A predictive analytics infrastructure to support a trustworthy early warning system. Applied Sciences, 11(13).
Bañeres, D., Rodríguez-González, M. E., Guerrero-Roldán, A.-E., and Cortadas, P. (2023). An early warning system to identify and intervene online dropout learners. International Journal of Educational Technology in Higher Education, 20(1):3.
Bañeres, D., Rodríguez-González, M. E., and Serra, M. (2019). An early feedback prediction system for learners at-risk within a first-year higher education course. IEEE Transactions on Learning Technologies, 12(2):249–263.
Bañeres, D., Rodríguez, M. E., Guerrero-Roldán, A. E., and Karadeniz, A. (2020). An early warning system to detect at-risk students in online higher education. Applied Sciences, 10(13).
Boath, E., Machin, R., Dixon, M., Thomas, N., O’CONNELL, P., and Taylor, L. (2016). Stop with the flo: using text messaging to improve retention rates in university students. Innovative Practice in Higher Education, 2(3):29–39.
Castleman, B. L. and Meyer, K. E. (2020). Can text message nudges improve academic outcomes in college? evidence from a west virginia initiative. The Review of Higher Education, 43(4):1125–1165.
Cechinel, C., Queiroga, E. M., Primo, T. T., Santos, H. L. d., Ramos, V. F. C., Munoz, R., Mello, R. F., and Machado, M. F. B. (2024). Lanse: a cloud-powered learning analytics platform for the automated identification of students at risk in learning management systems. In International Conference on Artificial Intelligence in Education, pages 127–138. Springer.
Deighton, K., Hudson, J., Manley, A. J., Kaiseler, M., Patterson, L. B., Rutherford, Z. H., and Swainson, M. (2019). Effects of emotional intelligence and supportive text messages on academic outcomes in first-year undergraduates. Journal of Further and Higher Education, 43(4):494–507.
Herodotou, C., Naydenova, G., Boroowa, A., Gilmour, A., and Rienties, B. (2020). How can predictive learning analytics and motivational interventions increase student retention and enhance administrative support in distance education? Journal of Learning Analytics, 7(2):72–83.
Irwin, V., Wang, K., Jung, J., Tezil, T., Alhassani, S., Filbey, A., Dilig, R., and Mann, F. B. (2024). Report on the condition of education 2024. nces 2024-144. National Center for Education Statistics.
O’Hara, R. E., Sparrow, B., and Joy, L. (2022). Values-based interventions increase reenrollment and equity among community college pre-allied health students. Journal of Postsecondary Student Success, 1(3):75–102.
Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A.-E., and Bañeres, D. (2022). Applying the utaut model to explain the students’ acceptance of an early warning system in higher education. Computers & Education, 182:104468.
Rebelo Marcolino, M., Reis Porto, T., Thompsen Primo, T., Targino, R., Ramos, V., Marques Queiroga, E., Munoz, R., and Cechinel, C. (2025). Student dropout prediction through machine learning optimization: insights from moodle log data. Scientific Reports, 15(1):1–16.
Rodríguez-González, M. E., Raffaghelli, J. E., Bañeres, D., Guerrero-Roldán, A.-E., and Crudele, F. (2024). Exploring higher education students’ experience with ai-powered educational tools: The case of an early warning system. Formazione & insegnamento, 22(1):74–84.
SEMESP (2025). Mapa do ensino superior no brasil. [link]. Acessado em: 23/07/2025.
Tippetts, M. M., Davis, B., Nalbone, S., and Zick, C. D. (2022). Thx 4 the msg: Assessing the impact of texting on student engagement and persistence. Research in higher education, 63(6):1073–1093.
Zick, C. D., Tippetts, M., and Davis, B. (2022). Money well spent? the cost-effectiveness of a texting intervention targeting college persistence. Journal of Research on Educational Effectiveness, 15(2):394–412.
Bañeres, D., Rodríguez-González, M. E., Guerrero-Roldán, A.-E., and Cortadas, P. (2023). An early warning system to identify and intervene online dropout learners. International Journal of Educational Technology in Higher Education, 20(1):3.
Bañeres, D., Rodríguez-González, M. E., and Serra, M. (2019). An early feedback prediction system for learners at-risk within a first-year higher education course. IEEE Transactions on Learning Technologies, 12(2):249–263.
Bañeres, D., Rodríguez, M. E., Guerrero-Roldán, A. E., and Karadeniz, A. (2020). An early warning system to detect at-risk students in online higher education. Applied Sciences, 10(13).
Boath, E., Machin, R., Dixon, M., Thomas, N., O’CONNELL, P., and Taylor, L. (2016). Stop with the flo: using text messaging to improve retention rates in university students. Innovative Practice in Higher Education, 2(3):29–39.
Castleman, B. L. and Meyer, K. E. (2020). Can text message nudges improve academic outcomes in college? evidence from a west virginia initiative. The Review of Higher Education, 43(4):1125–1165.
Cechinel, C., Queiroga, E. M., Primo, T. T., Santos, H. L. d., Ramos, V. F. C., Munoz, R., Mello, R. F., and Machado, M. F. B. (2024). Lanse: a cloud-powered learning analytics platform for the automated identification of students at risk in learning management systems. In International Conference on Artificial Intelligence in Education, pages 127–138. Springer.
Deighton, K., Hudson, J., Manley, A. J., Kaiseler, M., Patterson, L. B., Rutherford, Z. H., and Swainson, M. (2019). Effects of emotional intelligence and supportive text messages on academic outcomes in first-year undergraduates. Journal of Further and Higher Education, 43(4):494–507.
Herodotou, C., Naydenova, G., Boroowa, A., Gilmour, A., and Rienties, B. (2020). How can predictive learning analytics and motivational interventions increase student retention and enhance administrative support in distance education? Journal of Learning Analytics, 7(2):72–83.
Irwin, V., Wang, K., Jung, J., Tezil, T., Alhassani, S., Filbey, A., Dilig, R., and Mann, F. B. (2024). Report on the condition of education 2024. nces 2024-144. National Center for Education Statistics.
O’Hara, R. E., Sparrow, B., and Joy, L. (2022). Values-based interventions increase reenrollment and equity among community college pre-allied health students. Journal of Postsecondary Student Success, 1(3):75–102.
Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A.-E., and Bañeres, D. (2022). Applying the utaut model to explain the students’ acceptance of an early warning system in higher education. Computers & Education, 182:104468.
Rebelo Marcolino, M., Reis Porto, T., Thompsen Primo, T., Targino, R., Ramos, V., Marques Queiroga, E., Munoz, R., and Cechinel, C. (2025). Student dropout prediction through machine learning optimization: insights from moodle log data. Scientific Reports, 15(1):1–16.
Rodríguez-González, M. E., Raffaghelli, J. E., Bañeres, D., Guerrero-Roldán, A.-E., and Crudele, F. (2024). Exploring higher education students’ experience with ai-powered educational tools: The case of an early warning system. Formazione & insegnamento, 22(1):74–84.
SEMESP (2025). Mapa do ensino superior no brasil. [link]. Acessado em: 23/07/2025.
Tippetts, M. M., Davis, B., Nalbone, S., and Zick, C. D. (2022). Thx 4 the msg: Assessing the impact of texting on student engagement and persistence. Research in higher education, 63(6):1073–1093.
Zick, C. D., Tippetts, M., and Davis, B. (2022). Money well spent? the cost-effectiveness of a texting intervention targeting college persistence. Journal of Research on Educational Effectiveness, 15(2):394–412.
Publicado
25/11/2025
Como Citar
RAMOS, Vinicius; CECHINEL, Cristian; TARGINO, Rafael; PRIMO, Tiago; VILA, Eduardo de; RODRIGUES, Thomas Michels; RANDOJUNIOR, Edvaldo Luiz; QUEIROGA, Emanuel Marques.
LANSE-MIRA: Módulo de Intervenções para Retenção Acadêmica. In: WORKSHOP DE APLICAÇÕES PRÁTICAS DE LEARNING ANALYTICS E INTELIGÊNCIA ARTIFICIAL NO BRASIL (WAPLA), 3. , 2025, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 54-62.
DOI: https://doi.org/10.5753/wapla.2025.15927.
