Towards the Grade’s Prediction. A Study of Different Machine Learning Approaches to Predict Grades from Student Interaction Data

  1. Héctor Alonso-Misol Gerlache 1
  2. Pablo Moreno-Ger 1
  3. Luis de-la-Fuente Valentín 1
  1. 1 Universidad Internacional de La Rioja
    info

    Universidad Internacional de La Rioja

    Logroño, España

    ROR https://ror.org/029gnnp81

Zeitschrift:
IJIMAI

ISSN: 1989-1660

Datum der Publikation: 2022

Ausgabe: 7

Nummer: 4

Seiten: 196-204

Art: Artikel

DOI: 10.9781/IJIMAI.2021.11.007 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Andere Publikationen in: IJIMAI

Ziele für nachhaltige Entwicklung

Zusammenfassung

There is currently an open problem within the field of Artificial Intelligence applied to the educational field, which is the prediction of students’ grades. This problem aims to predict early school failure and dropout, and to determine the well-founded analysis of student performance for the improvement of educational quality. This document deals the problem of predicting grades of UNIR university master’s degree students in the on-line mode, proposing a working model and comparing different technologies to determine which one fits best with the available data set. In order to make the predictions, the dataset was submitted to a cleaning and analysis phases, being prepared for the use of Machine Learning algorithms, such as Naive Bayes, Decision Tree, Random Forest and Neural Networks. A comparison is made that addresses a double prediction on a homogeneous set of input data, predicting the final grade per subject and the final master’s degree grade. The results were obtained demonstrate that the use of these techniques makes possible the grade predictions. The data gives some figures in which we can see how Artificial Intelligence is able to predict situations with an accuracy above 96%.

Bibliographische Referenzen

  • A. Elbadrawy, A. Polyzou, Z. Ren, M. Sweeney, G. Karypis, and H. Rangwala, “Predicting Student Performance Using Personalized Analytics”, Computer, vol. 49, no. 4, pp. 61–69, 2016.
  • L. Gerritsen, “Predicting Student Performance with Neural Networks,” Ph.D. dissertation, School of Humanities, Tilburg University, Tilburg, Netherlands, 2017.
  • Y. Jiang, R. S. Baker, L. Paquette, M. San Pedro, & N. T. Heffernan, “Learning, moment-by-moment and over the long term”, in International Conference on Artificial Intelligence in Education, Madrid, Spain, 2015, pp. 654–657, doi: https://doi.org/10.1007/978-3-319-19773-9_84
  • T. Mishra, D. Kumar, & S. Gupta, “Students’ employability prediction model through data mining”, International Journal of Applied Engineering Research, vol. 11, no. 4, pp. 2275–2282, 2016.
  • V. Singh & A. Thurman, “How Many Ways Can We Define Online Learning? A Systematic Literature Review of Definitions of Online Learning (1988-2018),” American Journal of Distance Education, vol. 33, no. 4, pp. 289–306, 2019.
  • R. Stillwell, & J. Sable, “Public School Graduates and Dropouts from the Common Core of Data: School Year 2009–10”, National Center for Education Statistics, US Department of Education, USA, 2013. Accessed: Feb. 15, 2019. [Online]. Available: https://nces.ed.gov/ pubs2013/2013309rev.pdf.
  • R. J. Sternberg. “Teaching College Students that Creativity Is a Decision”, Guidance & Counselling, vol. 19, no. 4, pp. 196-200, 2004.
  • J.M. Tomás & M. Gutiérrez, “Aportaciones de la teoría de la autodeterminación a la predicción de la satisfacción escolar en estudiantes universitarios”, Revista de Investigación Educativa, vol. 37, no. 2, pp. 471–485, 2019.
  • C. J. Villagrá-Arnedo, F. J. Gallego-Durán, F. Llorens-Largo, R. SatorreCuerda, P. Compañ-Rosique, & R. Molina-Carmona. “Time-Dependent Performance Prediction System for Early Insight in Learning Trends”, International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 2, pp. 112-124, 2020, doi: 10.9781/ijimai.2020.05.006.
  • V.M. Cojocariu, I. Lazar, V. Nedeff, & G. Lazar, “SWOT Analysis of E-learning Educational Services from the Perspective of their Beneficiaries”, Procedia-Social and Behavioral Sciences, vol. 116, pp. 1999–2003, 2014, doi: 10.1016/j.sbspro.2014.01.510.
  • P. Colás Bravo, “El abandono universitario”, Revista Fuentes, no. 16, pp. 9–14, 2015, doi: 10.12795/revistafuentes.2015.i16.
  • S. Regha R. & D. U. Rani, “An Efficient Clustering Based Feature Selection for Predicting Student Performance”, International Working Group on Educational Data Mining, vol. 9, no. 2, pp. 524–531, 2017, doi: 10.21817/ ijet/2017/v9i2/170902328.
  • G. W. Dekker, M. Pechenizkiy, & J. M. Vleeshouwers, “Predicting students drop out: A case study”, in International Working Group on Educational Data Mining 2009, Córdoba, Spain, 2009, pp. 41–50.
  • Q. Hu, A. Polyzou, G. Karypis, & H. Rangwala, “Enriching course-Specific regression models with content features for grade prediction”, in 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2017, pp. 504–513. doi: 10.1109/DSAA.2017.74.
  • I. Lykourentzou, I. Giannoukos, V. Nikolopoulos, G. Mpardis, & V. Loumos, “Dropout prediction in e-learning courses through the combination of machine learning techniques”, Computers & Education, vol. 53, no. 3, pp. 950–965, 2009, doi: 10.1016/j.compedu.2009.05.010.
  • J. Xu, K. H. Moon & M. van der Schaar, “A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs,” in IEEE Journal of Selected Topics in Signal Processing, vol. 11, no. 5, pp. 742-753, 2017, doi: 10.1109/JSTSP.2017.2692560.
  • R. Heredia, J. Jobany, H. Rodríguez, G. Aida & J.A. Vilalta, “Predicting performance in a subject using ordinal logistic regression”. Estudios pedagógicos (Valdivia), vol. 40. no 1, pp. 145–162, 2014. doi: 10.4067/ s0718-07052014000100009.
  • J. G. Cleary and L. E. Trigg, “K*: An Instance-based Learner Using an Entropic Distance Measure” in Machine Learning Proceedings, M. Kaufmann Publishers, 1995, pp. 108–114. doi:10.1016/b978-1-55860-377- 6.50022-0.
  • T. Miranda Lakshmi, A. Martin, and V. Prasanna Venkatesan, “An Analysis of Students Performance Using Genetic Algorithm”, Journal of Computer Sciences and Applications, vol. 1, no 4, pp. 75-79, 2013, doi: 10.12691/jcsa-1-4-3.
  • E. Osmanbegovic, M. Suljic, “Data Mining Approach for Predicting Student Performance” in Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 10, no. 1, pp. 3-12, 2012. [Online] Available: http://hdl.handle.net/10419/193806.
  • A. Hamoud, A. S. Hashim, & W. A. Awadh, “Predicting student performance in higher education institutions using decision tree analysis”, International Journal of Interactive Multimedia and Artificial Intelligence, vol. 5, no. 2, pp. 26-31, 2018, doi: 10.9781/ijimai.2018.02.004
Fuente de los datos: Dialnet