Predicción de riesgo académico usando notas, asistencia a clases y clics en el LMS

Gabriel Zúñiga Varela, Carles Lindín, Teresa Sancho Vinuesa

Resumen


Pocos estudios con Analítica del Aprendizaje han intentado predecir los resultados de conjunto de un año académico. Esta investigación desarrolló un modelo predictivo del riesgo de suspender el primer año en un Grado de Negocios (i.e., obtener menos créditos de los necesarios para aprobar), utilizando Regresión Logística con datos de dos cohortes de estudiantes (n=1046). El modelo utiliza la tasa de asistencia, calificaciones de evaluación continua de tres asignaturas y los clics en la LMS, del primer semestre del año. Se probó con 74 estudiantes de una cohorte diferente, obteniendo una precisión alta, pero una sensibilidad baja. Los resultados sugieren que la asistencia a clases, las habilidades de comunicación y la competencia numérica son transversales al éxito académico. El artículo revela una relación no lineal entre la actividad en la LMS y la media académica y propone un método para tratarlo.

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Referencias


Aguiar, E.; Ambrose, G. A.; Chawla, N. V.; Goodrich, V.; Brockman, J. (2014). Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Persistence. Journal of Learning Analytics, 1(3), 7-33. http://dx.doi.org/10.18608/jla.2014.13.3.

Ajjawi, R.; Dracup, M.; Zacharias, N.; Bennett, S.; Boud, D. (2020). Persisting students’ explanations of and emotional responses to academic failure. Higher Education Research & Development, 39(2), 185-199. https://doi.org/10.1080/07294360.2019.1664999.

Andrietti, V.; Velasco, C. (2015). Lecture Attendance, Study Time, and Academic Performance: A Panel Data Study. Journal of Economic Education, 46(3), 239-259. https://doi-org.sire.ub.edu/10.1080/00220485.2015.1040182.

Arnold, K. E.; Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge - LAK ’12, 267. https://doi.org/10.1145/2330601.2330666.

Atkinson, R. (2001). Standardized tests and access to American universities. The 2001 Robert H. Atwell Distinguished Lecture. The annual meeting of the American Council on Education. Washington D.C., Estados Unidos. http://dx.doi.org/10.1525/9780520933941-017.

Aulck, L.; Nambi, D.; Velagapudi, N.; Blumenstock, J.; West, J. (2019, julio). Mining University Registrar Records to Predict First-Year Undergraduate Attrition. In Proceedings of The 12th International Conference on Educational Data Mining (EDM 2019). The 12th International Conference on Educational Data Mining (EDM 2019). Montreal, Canada.

Balkis, M.; Duru, E. (2017). Gender Differences in the Relationship between Academic Procrastination, Satisfaction with Academic Life and Academic Performance. Electronic Journal of Research in Educational Psychology, 15(1), 105-125. https://doi.org/10.14204/ejrep.41.16042.

Bañeres, D.; Rodríguez, M. E.; Guerrero-Roldán, A. E.; Karadeniz, A. (2020). An Early Warning System to Detect At-Risk Students in Online Higher Education. Applied Sciences, 10(13), 4427. https://doi.org/10.3390/app10134427.

Berlanga-Silvente, V.; Baños, R. V. (2014). Cómo obtener un Modelo de Regresión Logística Binaria con SPSS. REIRE Revista d’Innovació i Recerca en Educació, 7(2), 105-118. https://doi.org/10.1344/reire2014.7.2727.

Bertolini, R.; Finch, S. J.; Nehm, R. H. (2021). Testing the Impact of Novel Assessment Sources and Machine Learning Methods on Predictive Outcome Modeling in Undergraduate Biology. Journal of Science Education and Technology, 30(2), 193-209. http://dx.doi.org/10.1007/s10956-020-09888-8.

Bevitt, D.; Baldwin, C.; Calvert, J. (2010). Intervening Early: Attendance and Performance Monitoring as a Trigger for First Year Support in the Biosciences. Bioscience Education, 15(1), 1-14. https://doi.org/10.3108/beej.15.4.

Brooks, C.; Thompson, C. (2017). Predictive modelling in teaching and learning. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), The Handbook of Learning Analytics (1st Edition, pp. 61-68). Alberta (Canada): Society for Learning Analytics Research (SoLAR). https://doi.org/10.18608/hla17.

Byrom, T.; Lightfoot, N. (2013). Interrupted trajectories: The impact of academic failure on the social mobility of working-class students. British Journal of Sociology of Education, 34(5-6), 812-828. https://doi.org/10.1080/01425692.2013.816042.

Cárdenas Moren, C.; Crawford Augant, K.; Crawford Labrin, B.; Soto de Giorgis, R.; de la Fuente-Mella, H.; Peña Fritz, Á.; Valenzuela Saavedra, M.; Hermosilla Monckton, P.; Álvarez Castelli, L. (2020). A quantitative analysis of the identification of personality traits in engineering students and their relation to academic performance. Studies in Higher Education, 45(7), 1323-1334. http://dx.doi.org/10.1080/03075079.2019.1572089.

Casey, K.; Azcona, D. (2017). Utilizing student activity patterns to predict performance. International Journal of Educational Technology in Higher Education, 14(1), 1-15. https://doi.org/10.1186/s41239-017-0044-3.

Cavanaugh, C.; Hargis, J.; Mayberry, J. (2016). Participation in the virtual environment of blended college courses: An activity study of student performance. The International Review of Research in Open and Distributed Learning, 17(3), 423-432. http://dx.doi.org/10.19173/irrodl.v17i3.1811.

Choi, S. P. M.; Lam, S. S.; Li, K. C.; Wong, B. T. (2018). Learning Analytics at Low Cost: At-risk Student Prediction with Clicker Data and Systematic Proactive Interventions. Journal of Educational Technology & Society, 21(2), 273-290.

Conijn, R.; Snijders, C.; Kleingeld, A.; Matzat, U. (2017). Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17-29. https://doi.org/10.1109/TLT.2016.2616312.

Credé, M.; Kuncel, N. R. (2008). Study Habits, Skills, and Attitudes: The Third Pillar Supporting Collegiate Academic Performance. Perspectives on Psychological Science, 3(6), 425-453. https://doi.org/10.1111/j.1745-6924.2008.00089.x.

Credé, M.; Tynan, M. C.; Harms, P. D. (2017). Much Ado About Grit: A Meta-Analytic Synthesis of the Grit Literature. Journal of Personality and Social Psychology, 113(3), 492-511. http://dx.doi.org/10.1037/pspp0000102.

Crocker, J.; Karpinski, A.; Quinn, D. M.; Chase, S. K. (2003). When Grades Determine Self-Worth: Consequences of Contingent Self-Worth for Male and Female Engineering and Psychology Majors. Journal of Personality and Social Psychology, 85(3), 507-516. https://doi.org/10.1037/0022-3514.85.3.507.

Fernández Mellizo-Soto, M. (2022). Estudio sobre el Abandono de los Estudios de Grado en el Sistema Universitario Español. Ministerio de Universidades, Gobierno de España. (https://www.universidades.gob.es/stfls/universidades/ministerio/ficheros/Informe_Ejecutivo_abandono_fin2_comenmtadoMinistro.pdf).

Fiallos Quinteros, J. C.; Jimenez Builes, J. A.; Branch Bedoya, J. W. (2022). Teaching and learning analytics applied to programming courses. Campus Virtuales, 11(1), 35-49. https://doi.org/10.54988/cv.2022.1.880.

Foster, E.; Siddle, R. (2020). The Effectiveness of Learning Analytics for Identifying At-Risk Students in Higher Education. Assessment & Evaluation in Higher Education, 45(6), 842-854. http://dx.doi.org/10.1080/02602938.2019.1682118.

García-Tinizaray, D.; Ordoñez-Briceño, K.; Torres-Diaz, J. C. (2015). Learning analytics para predecir la deserción de estudiantes a distancia. Campus virtuales, 3(1), 120-126.

Guzmán-Valenzuela, C.; Gómez-González, C.; Rojas-Murphy Tagle, A.; Lorca-Vyhmeister, A. (2021). Learning analytics in higher education: A preponderance of analytics but very little learning?. International Journal of Educational Technology in Higher Education, 18(1), 1-19. https://doi.org/10.1186/s41239-021-00258-x.

Herzog, S. (2006). Estimating student retention and degree-completion time: Decision trees and neural networks vis-à-vis regression. New directions for institutional research, 2006(131), 17-33. http://dx.doi.org/10.1002/ir.185

Hosmer, D. W.; Lemeshow, S.; Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). Hoboken, N.J. (USA): Wiley.

Huang, A. Y. Q.; Lu, O. H. T.; Huang, J. C. H.; Yin, C. J.; Yang, S. J. H. (2020). Predicting Students’ Academic Performance by Using Educational Big Data and Learning Analytics: Evaluation of Classification Methods and Learning Logs. Interactive Learning Environments, 28(2), 206-230.

Icekson, T.; Kaplan, O.; Slobodin, O. (2020). Does Optimism Predict Academic Performance? Exploring the Moderating Roles of Conscientiousness and Gender. Studies in Higher Education, 45(3), 635-647. http://dx.doi.org/10.1080/03075079.2018.1564257.

Ifenthaler, D.; Yau, J. Y.-K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68(4), 1961-1990. https://doi.org/10.1007/s11423-020-09788-z.

James, T. (2018). Empirical Results Using Learning Analytics in the Classroom. College Quarterly, 21(2), n.d.

Jayaprakash, S. M.; Moody, E. W.; Lauría, E. J. M.; Regan, J. R.; Baron, J. D. (2014). Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative. Journal of Learning Analytics, 1(1), 6-47. http://dx.doi.org/10.18608/jla.2014.11.3.

Karnik, A.; Kishore, P.; Meraj, M. (2020). Examining the linkage between class attendance at university and academic performance in an International Branch Campus setting. Research in Comparative and International Education, 15(4), 371-390. https://doi.org/10.1177/1745499920958855.

Knight, S., & Shum, S. B. (2017). Theory and learning analytics. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), The Handbook of Learning Analytics (1st Edition, pp. 17-22). Alberta (Canada): Society for Learning Analytics Research (SoLAR). https://doi.org/10.18608/hla17.

Kokoç, M.; Akçapınar, G.; Hasnine, M. N. (2021). Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics. Educational Technology & Society, 24(1), 223-235.

Larrabee Sønderlund, A.; Hughes, E.; Smith, J. (2019). The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology, 50(5), 2594-2618. https://doi.org/10.1111/bjet.12720.

Lassibille, G.; Navarro Gómez, L. (2008). Why do higher education students drop out? Evidence from Spain. Education Economics, 16(1), 89-105. https://doi.org/10.1080/09645290701523267.

Liesz, T. J.; Reyes, M. G. C. (1989). The Use of Piagetan Concepts to Enhance Student Performance in the Introductory Finance Course. Journal of Financial Education, 18, 8-14.

Lonn, S.; Aguilar, S. J.; Teasley, S. D. (2015). Investigating student motivation in the context of a learning analytics intervention during a summer bridge program. Computers in Human Behavior, 47, 90-97. https://doi.org/10.1016/j.chb.2014.07.013.

Lu, C.; Cutumisu, M. (2022). Online engagement and performance on formative assessments mediate the relationship between attendance and course performance. International Journal of Educational Technology in Higher Education, 19(1), 1-23. https://doi.org/10.1186/s41239-021-00307-5.

Lu, O. H. T.; Huang, A. Y. Q.; Huang, J. C. H.; Lin, A. J. Q.; Ogata, H.; Yang, S. J. H. (2018). Applying Learning Analytics for the Early Prediction of Students’ Academic Performance in Blended Learning. Educational Technology & Society, 21(2), 220-232.

Mansouri, T.; ZareRavasan, A.; Ashrafi, A. (2021). A Learning Fuzzy Cognitive Map (LFCM) Approach to Predict Student Performance. Journal of Information Technology Education: Research, 20, 221-243. https://doi.org/10.28945/4760.

Marcal, L. E.; Hennessey, J. E.; Curren, M. T.; Roberts, W. W. (2005). Do Business Communication Courses Improve Student Performance in Introductory Marketing?. Journal of Education for Business, 80(5), 289-294. https://doi.org/10.3200/JOEB.80.5.289-294.

Marín Sánchez, M.; Infante Rejano, E.; Troyano Rodríguez, Y. (2000). El fracaso académico en la universidad: Aspectos motivacionales e intereses profesionales. Revista Latinoamericana de Psicología, 32(3), 505-517.

Martínez-Carrascal, J. A.; Campuzano, J.; Sancho-Vinuesa, T.; Valderrama, E. (2019). Predicting student performance over time. A case study for a blended-learning engineering course. Proceedings of the Learning Analytics Summer Institute Spain 2019: Learning Analytics in Higher Education, 2415, 43-55. (http://ceur-ws.org/Vol-2415/paper05.pdf).

Martínez-Carrascal, J. A.; Márquez Cebrián, D.; Sancho-Vinuesa, T.; Valderrama, E. (2020). Impact of early activity on flipped classroom performance prediction: A case study for a first-year Engineering course. Computer Applications in Engineering Education, 28(3), 590-605. https://doi.org/10.1002/cae.22229.

Martínez-Carrascal, J. A.; Valderrama, E.; Sancho-Vinuesa, T. (2020). Combining clustering and sequential pattern mining to detect behavioral differences in log data: Conceptualization and case study. Proceedings of the Learning Analytics Summer Institute Spain 2020: Learning Analytics. Time for Adoption?, 2671, 25-38. (http://ceur-ws.org/Vol-2671/paper04.pdf).

Milliron, M. D.; Malcolm, L.; Kil, D. (2014). Insight and Action Analytics: Three Case Studies to Consider. Research & Practice in Assessment, 9, 70-89.

Pistilli, M. D.; Heileman, G. L. (2017). Guiding Early and Often: Using Curricular and Learning Analytics to Shape Teaching, Learning, and Student Success in Gateway Courses: Guiding Early and Often. New Directions for Higher Education, 2017(180), 21-30. https://doi.org/10.1002/he.20258.

Regorz, A. (s. f.). How to interpret a Collinearity Diagnostics table in SPSS. REGORZ STATISTIK / REGORZ STATISTICS. (http://www.regorz-statistik.de/en/collinearity_diagnostics_table_SPSS.html#references).

Richardson, M.; Abraham, C.; Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological bulletin, 138(2), 353-387. http://dx.doi.org/10.1037/a0026838.

Sackett, P. R.; Kuncel, N. R.; Arneson, J. J.; Cooper, S. R.; Waters, S. D. (2009). Does socioeconomic status explain the relationship between admissions tests and post-secondary academic performance?. Psychological bulletin, 135(1), 1-22. http://dx.doi.org/10.1037/a0013978.

Schmitt, N.; Keeney, J.; Oswald, F. L.; Pleskac, T. J.; Billington, A. Q.; Sinha, R.; Zorzie, M. (2009). Prediction of 4-Year College Student Performance Using Cognitive and Noncognitive Predictors and the Impact on Demographic Status of Admitted Students. Journal of Applied Psychology, 94(6), 1479-1497. http://dx.doi.org/10.1037/a0016810.

Scholes, V. (2016). The ethics of using learning analytics to categorize students on risk. Educational Technology, Research and Development, 64(5), 939-955. http://dx.doi.org/10.1007/s11423-016-9458-1.

Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851.

Stephens-Davidowitz, S. (2017). Everybody lies: Big data, new data, and what the internet can tell us about who we really are. New York (USA): HarperCollins New York.

Tempelaar, D. T.; Rienties, B.; Nguyen, Q. (2017). Towards Actionable Learning Analytics Using Dispositions. IEEE Transactions on Learning Technologies, 10(1), 6-16. https://doi.org/10.1109/TLT.2017.2662679.

Tempelaar, D. T.; Rienties, B.; Nguyen, Q. (2021). The contribution of dispositional learning analytics to precision education. Educational Technology & Society, 24(1), 109-122.

Waddington, R. J.; Nam, S.; Lonn, S.; Teasley, S. D. (2016). Improving Early Warning Systems with Categorized Course Resource Usage. Journal of Learning Analytics, 3(3), 263-290. https://doi.org/10.18608/jla.2016.33.13.

Westrick, P. A.; Le, H.; Robbins, S. B.; Radunzel, J. M. R.; Schmidt, F. L. (2015). College Performance and Retention: A Meta-Analysis of the Predictive Validities of ACT® Scores, High School Grades, and SES. Educational Assessment, 20(1), 23-45. https://doi.org/10.1080/10627197.2015.997614.




DOI: http://dx.doi.org/10.54988/cv.2025.1.1434

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