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CURRICULUM & TEACHING STUDIES

Academic trajectories analysis with a life-course approach: A case study in medical students

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Article: 2018118 | Received 21 Jul 2021, Accepted 23 Nov 2021, Published online: 05 Jan 2022

References

  • Abbott, A., & Forrest, J. (1986). Optimal matching methods for historical sequences. The Journal of Interdisciplinary History, 16(3), 471–20. https://doi.org/10.2307/204500
  • Abbott, A. (2001). Time matters: On theory and method. University of Chicago Press.
  • Akaki Blancas, J. L., & López Bárcena, J. L. (2018). Formación de médicos especialistas en México. Educación Médica, 19(S1), 36–42. https://doi.org/10.1016/j.edumed.2018.03.007
  • Albanese, M. (2000). Problem-based learning: Why curricula are likely to show little effect on knowledge and clinical skills. Medical Education, 34(9), 729–738. https://doi.org/10.1046/j.1365-2923.2000.00753.x
  • Alves, C. L., Belisário, S. A., Abreu, D. M. X., Lemos, J. M. C., D’Ávila, L. S., & Goulart, L. M. H. F. (2013). Curricular transformations in medical school: An exploration of the promotion of change in Brazil. Medical Education, 47(6), 617–626. https://doi.org/10.1111/medu.12154
  • Anderson, M. B. (2000). A snapshot of Medical Students’ education at the beginning of the 21st century: Reports from 130 Schools. Academic Medicine, 75(9), n9. https://journals.lww.com/academicmedicine/toc/2000/09001
  • Ball, R., & Halwachi, J. (1987). Performance indicators in higher education. Higher Education, 16(4), 393–405. https://doi.org/10.1007/BF00129112
  • Blanco, M. (2011). El enfoque del curso de vida: Orígenes y desarrollo. Revista Latinoamericana de Población, 5(8), 5–31. https://www.redalyc.org/pdf/3238/323827304003.pdf
  • Bland, C. J., Starnaman, S., Wersal, L., Moorhead-Rosenberg, L., Zonia, S., & Henry, R. (2000). Curricular change in medical schools: How to succeed. Academic Medicine, 75(6), 575–594. https://doi.org/10.1097/00001888-200006000-00006
  • Bruinsma, M., & Jansen, P. W. A. (2009). When will I succeed in my first‐year diploma? Survival analysis in Dutch higher education. Higher Education Research & Development, 28(1), 99–114. https://doi.org/10.1080/07294360802444396
  • Burillo, V., Arriaga, J., Carpeño, A., & Casaravilla, A. (2011). Estudio de la influencia de factores personales y de ingreso en la universidad en el abandono. Valorando el riesgo o probabilidad de abandono en la Universidad Politécnica de Madrid. Congresos CLABES.
  • Caballero, L. B., Castillo, Y. B., & Álvarez, C. B. (2011). Comparación de las tasas de aprobación, reprobación, abandono y costo estudiante de dos cohortes en carreras de Licenciatura en Ingeniería en la Universidad Tecnológica de Panamá. Congresos CLABES.
  • Campillo Labrandero, M., Martínez González, A., García Minjares, M., Guerrero Mora, L., & Sánchez Mendiola, M. (2021). Desempeño académico y egreso en 25 generaciones de estudiantes de la Facultad de Medicina de la UNAM. Educación Médica, 22(2), 67–72. https://doi.org/10.1016/j.edumed.2019.05.003
  • Castro, S. B. E., Castillo, M. A. S., Villegas, E. B., & Estrada, D. Y. R. (2016). Deserción o interrupción en las trayectorias estudiantiles. Congresos CLABES.
  • Chan, T., Sebok-Syer, S., Thoma, B., Wise, A., Sherbino, J., Pusic, M., & Promes, S. (2018). Learning analytics in medical education assessment: The past, the present, and the future. AEM Education and Training, 2(2), 178–187. https://doi.org/10.1002/aet2.10087
  • Christakis, N. (1995). The similarity and frequency of proposals to reform US medical education. JAMA, 274(9), 706–711. https://doi.org/10.1001/jama.1995.03530090038019
  • Cook, D. A. (2010). Twelve tips for evaluating educational programs. Medical Teacher, 32(4), 296–301. https://doi.org/10.3109/01421590903480121
  • De Angelis, C. D. (2000). The Johns Hopkins University School of Medicine curriculum for the twenty-first century. JHU Press.
  • Dienstag, J. L. (2011). Evolution of the new pathway curriculum at Harvard Medical School: The new integrated curriculum. Perspectives in Biology and Medicine, 54(1), 36–54. https://doi.org/10.1353/pbm.2011.0003
  • Elder, G. H., Jr. (1999). Children of the great depression: Social change in life experience (25th Anniverary ed.). Westview Press.
  • Elder, G. H., John son, M. K., & Crosnoe, R. (2003). The emergence and development of life course theory. In Mortimer, and Shanahan (eds.), Handbook of the life course (pp. 3–19). Kluwer Academic/Plenum Publishers, New York.
  • Elder, J. Z. G. G. H. (1998). Methods of life course research: Qualitative and quantitative approaches. Sage. https://doi.org/10.4135/9781483348919
  • Ellaway, R. H., Topps, D., & Pusic, M. (2019). Data, big and small: Emerging challenges to medical education scholarship. Academic Medicine, 94(1), 31–36. https://doi.org/10.1097/ACM.0000000000002465
  • Gabadinho, A., Ritschard, G., Studer, M., & Müller, N. S. (2008). Mining sequence data in R with the TraMineR package: A user’s guide. Department of Econometrics and Laboratory of Demography, University of Geneva. http://mephisto.unige.ch/traminer
  • Gonzalez, M. V. (2017). Estudio del abandono empleando un modelo de riesgos proporcionales. Congresos CLABES.
  • Guthrie, G. (1986). Current research in developing countries: The impact of curriculum reform on teaching. Teaching and Teacher Education, 2(1), 81–89. https://doi.org/10.1016/0742-051X(86)90006-5
  • Haas, C., & Hadjar, A. (2020). Students’ trajectories through higher education: A review of quantitative research. Higher Education, 79(6), 1099–1118. https://doi.org/10.1007/s10734-019-00458-5
  • Hamui Sutton, A. (2016). Tensiones y reconfiguraciones de la práctica docente ante el cambio curricular en la Facultad de Medicina de la Universidad Nacional Autónoma de México. Investigación En Educación Médica, 5(20), 215–219. https://doi.org/10.1016/j.riem.2016.01.021
  • Han, Y., Liefbroer, A. C., & Elzinga, C. H. (2017). Comparing methods of classifying life courses: Sequence analysis and latent class analysis. Longitudinal and Life Course Studies, 8(4), 319–341. https://doi.org/10.14301/llcs.v8i4.409
  • Harper, S. R. (2007). Using qualitative methods to assess student trajectories and college impact. New Directions for Institutional Research, 2007(136), 55–68. https://doi.org/10.1002/ir.231
  • Hecker, K., & Violato, C. (2008). How much do differences in medical schools influence student performance? A longitudinal study employing hierarchical linear modeling. Teaching and Learning in Medicine, 20(2), 104–113. https://doi.org/10.1080/10401330801991915
  • Hecker, K., & Violato, C. (2009). Medical school curricula: Do curricular approaches affect competence in medicine. Family Medicine, 41(6), 420–426. https://pubmed.ncbi.nlm.nih.gov/19492189/
  • Hilliger, I., Miranda, C., Celis, S., & Pérez-sanagustín, M. (2019). Evaluating usage of an analytics tool to support continuous curriculum improvement. CEUR Workshop Proceedings, 2437, 1–14. European Conference on Technology Enhanced Learning.
  • Hilliger, I., Ortiz-Rojas, M., Pesántez-Cabrera, P., Scheihing, E., Tsai, Y. S., Muñoz-Merino, P. J., Broos, T., Whitelock-Wainwright, A., Gašević, D., and Pérez-Sanagustín, M. (2020). Towards learning analytics adoption: A mixed methods study of data-related practices and policies in Latin American universities. British Journal of Educational Technology, 51(4), 915–937. https://doi.org/10.1111/bjet.12933
  • Jamelske, E. (2009). Measuring the impact of a university first-year experience program on student GPA and retention. Higher Education, 57(3), 373–391. https://doi.org/10.1007/s10734-008-9161-1
  • Jorm, C., & Roberts, C. (2018). Using complexity theory to guide medical school evaluations. Academic Medicine, 93(3), 399–405. https://doi.org/10.1097/ACM.0000000000001828
  • Komenda, M., Víta, M., Vaitsis, C., Schwarz, D., Pokorná, A., Zary, N., Dušek, L., & Vrana, K. E. (2015). Curriculum mapping with academic analytics in medical and healthcare education. PloS One, 10(12), e0143748. https://doi.org/10.1371/journal.pone.0143748
  • Konecki, M., LaPierre, C., & Jervis, K. (2018). “Accessible data visualization in higher education,” 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, pp. 0733–0737. https://doi.org/10.23919/MIPRO.2018.8400136
  • Labrandero, M. C., González, A. M., Minjares, M. G., Mora, L. G., & Mendiola, M. S. (2021). Desempeño académico y egreso en 25 generaciones de estudiantes de la Facultad de Medicina de la UNAM. Educación Médica, 22(2), 67–72. https://doi.org/10.1016/j.edumed.2019.05.003
  • Lockyer, J. M., Violato, C., Wright, B. J., & Fidler, H. M. (2009). An analysis of long-term outcomes of the impact of curriculum: A comparison of the three-and four-year medical school curricula. Academic Medicine, 84(10), 1342–1347. https://doi.org/10.1097/ACM.0b013e3181b6c08e
  • Mellado, F. R. M., Orellana, M. B. C., & Gabrie, A. J. B. (2017). Variables y factores asociados al fenómeno de la retención y abandono estudiantil universitario en investigaciones de Latinoamérica y el Caribe. Congresos CLABES.
  • Mennin, S. (2010). Self-organisation, integration and curriculum in the complex world of medical education. Medical Education, 44(1), 20–30. https://doi.org/10.1111/j.1365-2923.2009.03548.x
  • Milesi, C. (2010). Do all roads lead to Rome? Effect of educational trajectories on educational transitions. Research in Social Stratification and Mobility, 28(1), 23–44. https://doi.org/10.1016/j.rssm.2009.12.002
  • Musso, M. F., Hernández, C. F. R., & Cascallar, E. (2020). Predicting key educational outcomes in academic trajectories: A machine-learning approach. Higher Education, 80(5), 875–894. https://doi.org/10.1007/s10734-020-00520-7
  • National Research Council. (2012). Improving measurement of productivity in higher education. The National Academies Press. https://doi.org/10.17226/13417
  • Norman, G. (2003). RCT= results confounded and trivial: The perils of grand educational experiments. Medical Education, 37(7), 582–584. https://doi.org/10.1046/j.1365-2923.2003.01586.x
  • Oloriz, M. G., & Fernández, J. M. (2013). Relación entre las características del estudiante al momento de iniciar estudios superiores y el abandono en la Universidad Nacional de Luján durante el período 2000-2010. Congresos CLABES.
  • Opazo, P., & Villalobos, P. (2012). Probabilidad de desertar de estudiantes: 5 años de experiencia en la Universidad de Talca. Congresos CLABES. https://revistas.utp.ac.pa/index.php/clabes/article/view/1774
  • Ordorika, I. (2021). Student movements and politics in Latin America: A historical reconceptualization. Higher Education. https://doi.org/10.1007/s10734-020-00656-6
  • Popoola, S. I., Atayero, A. A., Badejo, J. A., John, T. M., Odukoya, J. A., & Omole, D. O. (2018). Learning analytics for smart campus: Data on academic performances of engineering undergraduates in Nigerian private university. Data in Brief, 17, 76–94. https://doi.org/10.1016/j.dib.2017.12.059
  • Putnam, C. E. (2006). Reform and innovation: A repeating pattern during a half century of medical education in the USA. Medical Education, 40(3), 227–234. https://doi.org/10.1111/j.1365-2929.2006.02402.x
  • Rodríguez Ayán, M. N., & Ruiz Díaz, M. Á. (2011). Indicadores de rendimiento de estudiantes universitarios: Calificaciones versus créditos acumulados. Revista de Educación, 355, 467–492. https://sede.educacion.gob.es/publiventa/indicadores-de-rendimiento-de-estudiantes-universitarios-calificaciones-versus-creditos-acumulados/investigacion-educativa/22890
  • Rodríguez, R. (1989). Metodología para el análisis demográfico de la eficiencia terminal, la deserción y el rezago escolar. InFelipe Martínez Rizo (Ed.), La Trayectoria Escolar En La Educación Superior (pp. 225–280). ANUIES & SEP (PROIDES).
  • Saavedra, Ó. C., & Espinoza, G. A. B. (2006). ¿Cómo estimar la eficiencia terminal en la educación superior? Notas sobre su estatuto teórico. Revista de La Educación Superior, 35(139), 1. http://publicaciones.anuies.mx/pdfs/revista/Revista139_S1A1ES.pdf
  • Sánchez-Mendiola, M., Durante-Montiel, I., Morales-López, S., Lozano-Sánchez, R., Martínez-González, A., & Graue-Wiechers, E. (2011). Plan de estudios 2010 de la Facultad de Medicina de la Universidad Nacional Autónoma de México. Gaceta medica de Mexico, 147(2), 152–158. https://www.medigraphic.com/pdfs/gaceta/gm-2011/gm112k.pdf
  • Sánchez-Mendiola, M., Martínez-Franco, A. I., Rosales-Vega, A., Villamar-Chulin, J., Gatica-Lara, F., García-Durán, R., & Martínez-González, A. (2013). Development and implementation of a biomedical informatics course for medical students: Challenges of a large-scale blended-learning program. Journal of the American Medical Informatics Association, 20(2), 381–387. https://doi.org/10.1136/amiajnl-2011-000796
  • Seifert, T. A., Pascarella, E. T., Erkel, S. I., & Goodman, K. M. (2010). The importance of longitudinal pretest-posttest designs in estimating college impact. New Directions for Institutional Research, 2010(S2), 5–16. https://doi.org/10.1002/ir.368
  • Shinkfield, A. J. (2007). Evaluation theory, models, and applications. Jossey-Bass.
  • Sneyers, E., & De Witte, K. (2017). The interaction between dropout, graduation rates and quality ratings in universities. Journal of the Operational Research Society Internet].;68(4):416–430. https://doi.org/10.1057/jors.2016.15
  • Trucchi, C., German, C., & Casini, R. B. (2017). Atraso medido en créditos alcanzados, y detección de factores mediante regresión logística en el marco de un nuevo plan de estudios en la Facultad de Ciencias Económicas. UNC. Congresos CLABES.
  • Universidad Nacional Autónoma de México (UNAM). (2018). Reglamento General de Estudios Técnicos y Profesionales. https://www.dgae-siae.unam.mx/acerca/normatividad.html#leg-2
  • Valle Gómez-Tagle, R., Argüelles, G. R., & Lozano, A. V. (2001). El análisis de las trayectorias escolares en la UNAM. Un método de análisis. Alejandra Romo y Magdalena Fresan, Deserción, Rezago y Eficiencia Terminal En Instituciones de Educación Superior, México. ANUIES.
  • van der Bles, A. M., van der Linden, S., Freeman, A. L. J., Mitchell, J., Galvao, A. B., Zaval, L., & Spiegelhalter, D. J. (2019). Communicating uncertainty about facts, numbers and science. Royal Society Open Science, 6(5), 181870. https://doi.org/10.1098/rsos.181870
  • Villalobos, M. O., & Gutiérrez, V. C. (2013). Relación entre los parámetros de rendimiento escolar en el bachillerato y las tasas de rezago y abandono en la licenciatura en la facultad de medicina veterinaria y zootecnia de la UNAM. Congresos CLABES.
  • Villegas, E. B., Urías, J. R. R., & de Los Uribe, M. A. M. (2017). Detección Temprana De Estudiantes En Situación De Riesgo Soportado En Un Sistema De Información. Congresos CLABES.
  • Wong, B. T., m., & Li, K. C. (2020). A review of learning analytics intervention in higher education (2011–2018). Journal of Computers in Education, 7(1), 7–28. https://doi.org/10.1007/s40692-019-00143-7