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Academic Analytics Applied in the Study of the Relationship Between the Initial Profile of Undergraduate Students and Early Drop-Out Rates. Defining the Variables of a Predictor Instrument

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Proceedings TEEM 2022: Tenth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM 2022)

Abstract

The field of university dropout research is of utmost importance especially in the current context arising from the Covid-19 pandemic. Students who started their degrees in the last two years completed their pre-university studies during various phases of confinement and by combining traditional and virtual training. In this scenario, students' motivation and the way they cope with the difficulties of their first year of university are very relevant and will depend on a multitude of personal and social variables in their immediate environment. Previous studies have shown that many university students drop out of their studies early, but what factors and to what extent they affect this dropout is still a field under study. This paper focuses on the identification, classification and evaluation of a set of indicators based on teacher and tutor perception in different fields of study by applying quantitative and qualitative techniques. The results of pilot studies developed support the approach adopted, as they show how teachers can identify students at risk of dropping out at the beginning of the course and take proactive measures to monitor and motivate them, thus reducing the possibility of dropout.

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References

  1. Alonso-Misol Gerlache, H., Moreno-Ger, P.,  de-la-Fuente Valentín, L.:  Towards the grade’s prediction. a study of different machine learning approaches to predict grades from student interaction data. Int. J. Interactive Multimedia  Artifi. Intell., 1–9 (2022). doi: http://doi.org/https://doi.org/10.9781/ijimai.2021.11.007

  2. Araque, F., Roldán, C., Salguero, A.: Factors influencing university drop out rates. Comput. Educ. 53, 563–574 (2009). https://doi.org/10.1016/j.compedu.2009.03.013

    Article  Google Scholar 

  3. Bustamante, D., Garcia-Bedoya, O.: Predictive academic performance model to support, prevent and decrease the university dropout rate. In: Florez, H., Pollo-, M.F. (eds.) ICAI 2021. CCIS, vol. 1455, pp. 222–236. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89654-6_16

  4. Dekker, G.W., Pechenizkiy, M., Vleeshouwers, J.M.:  Predicting students drop out: a case study. In: International Working Group on Educational Data Mining (2009)

    Google Scholar 

  5. Fanelli, A.G.de., Deane, C.A.d.:  Abandono de los estudios universitarios: dimensión, factores asociados y desafíos para la política pública. Revista Fuentes, 85–106 (2015)

    Google Scholar 

  6. Fernández-Mellizo, M.: Análisis del abandono de los estudiantes de grado en las universidades presenciales en España. Universidad Complutense de Madrid Ministerio de Universidades de España, 86 (2022) 

    Google Scholar 

  7. Fonseca, D., García-Peñalvo, F.J.: Interactive and collaborative technological ecosystems for improving academic motivation and engagement. Univ. Access Inf. Soc. 18(3), 423–430 (2019). https://doi.org/10.1007/s10209-019-00669-8

  8. Fonseca, D., Montero, J.A., Guenaga, M., Mentxaka, I.: Data analysis of coaching and advising in undergraduate students. An analytic approach (2017) 

    Google Scholar 

  9. Fonseca, D., Redondo, E., Villagrasa, S.: Mixed-methods research: a new approach to evaluating the motivation and satisfaction of university students using advanced visual technologies. Univ. Access Inf. Soc. 14(3), 311–332 (2014). https://doi.org/10.1007/s10209-014-0361-4

  10. Fonseca, D.:  Student motivation assessment using and learning virtual and gamified urban environments. In: ACM International Conference Proceeding Series. pp. 1–7 (2017)

    Google Scholar 

  11. Fuenmayor, J.G., Bolaños, C.M.: Estrategias de aprendizaje para mitigar la deserción estudiantil en el marco de la COVID-19. SUMMA Revista disciplinaria en ciencias económicas y sociales 2, 49–55 (2020). https://doi.org/10.47666/summa.2.esp.06

    Article  Google Scholar 

  12. García-Peñalvo, F.J.:  Digital Transformation in the universities: implications of the COVID-19 Pandemic. Transformación digital en las universidades: Implicaciones de la pandemia de la COVID-19 (2021)

    Google Scholar 

  13. Gilar-Corbi, R., Pozo-Rico, T., Castejón, J.-L., Sánchez, T., Sandoval-Palis, I., Vidal, J.: Academic achievement and failure in university studies: motivational and emotional factors. Sustainability 12, 9798 (2020). https://doi.org/10.3390/su12239798

  14. Jacobo-Galicia, G., Máynez-Guaderrama, A.I., Cavazos-Arroyo, J.: Miedo al Covid, agotamiento y cinismo: su efecto en la intención de abandono universitario. European Journal of Education and Psychology 14, 1–18 (2021). https://doi.org/10.32457/ejep.v14i1.1432

    Article  Google Scholar 

  15. Llauró. A., Fonseca, D., Villegas, E., Aláez, M., Romero. S.:  Educational data mining application for improving the academic tutorial sessions, and the reduction of early dropout in undergraduate students. In: Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM 2021). Association for Computing Machinery, New York pp. 212–218 (2021)

    Google Scholar 

  16. Merlino, A., Ayllón, S,, Escanés, G.: Variables que influyen en la deserción de estudiantes universitarios de primer año. Construcción de índices de riesgo de abandono / Variables that influence first year university students’ dropout rates. Construction of dropout risk indexes. Actualidades Investigativas en Educación 11 (2011). doi: https://doi.org/10.15517/aie.v11i2.10189

  17. Pérez, B,, Castellanos, C., Correal, D.:  Predicting student drop-out rates using data mining techniques: A case study. In: IEEE Colombian Conference on Applications in Computational Intelligence. Springer, pp 111–125 (2018)

    Google Scholar 

  18. Pérez García, J.A., Hernández Armenteros, J.: Conferencia de Rectores de las Universidades Españolas. La universidad española en cifras 2017/2018. CRUE, Madrid (2020)

    Google Scholar 

  19. Robertson, M., Line, M., Jones, S., Thomas, S.: International students, learning environments and perceptions: a case study using the delphi technique. High. Educ. Res. Dev. 19, 89–102 (2000). https://doi.org/10.1080/07294360050020499

    Article  Google Scholar 

  20. Waheed, H., Hassan, S.-U., Aljohani, N.R., Hardman, J., Alelyani, S., Nawaz, R.: Predicting academic performance of students from VLE big data using deep learning models. Comput. Hum. Behav. 104, 106189 (2020). https://doi.org/10.1016/j.chb.2019.106189

    Article  Google Scholar 

  21. Wolter, S.C., Diem, A., Messer, D.: Drop-outs from S wiss Universities: an empirical analysis of data on all students between 1975 and 2008. Eur. J. Educ. 49, 471–483 (2014)

    Article  Google Scholar 

  22. Factores relacionados con la intención de desertar en estudiantes de enfermería. | Revista Ciencia y Cuidado. https://revistas.ufps.edu.co/index.php/cienciaycuidado/article/view/1545. (Accessed 2 Mar 2022)

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Acknowledgements

This research was funded by the project “Academic Analytics applied in the study of the relationship between the initial profile of undergraduate students and early drop-out rates in order to improve tutorial support processes (ASPA4DOR)”, granted at the VIII Call of ACM (Aristos Campus Mundus) Research Projects—2022, with the grant number: ACM2022_04.

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Correspondence to Alba Llauró .

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Llauró, A. et al. (2023). Academic Analytics Applied in the Study of the Relationship Between the Initial Profile of Undergraduate Students and Early Drop-Out Rates. Defining the Variables of a Predictor Instrument. In: García-Peñalvo, F.J., García-Holgado, A. (eds) Proceedings TEEM 2022: Tenth International Conference on Technological Ecosystems for Enhancing Multiculturality. TEEM 2022. Lecture Notes in Educational Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-0942-1_103

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  • DOI: https://doi.org/10.1007/978-981-99-0942-1_103

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