Time-dependent performance prediction system for early insight in learning trends

• Carlos J. Villagrá-Arnedo ^[1] ; Francisco J. Gallego-Durán ^[1] ; Faraón Llorens-Largo ^[1] ; Rosana Satorre-Cuerda ^[1] ; Patricia Compañ-Rosique ^[1] ; Rafael Molina-Carmona ^[1]
  1. [1] Universitat d'Alacant

     Universitat d'Alacant

     Alicante, España

     
• Localización: IJIMAI, ISSN-e 1989-1660, Vol. 6, Nº. 2, 2020, págs. 112-124
• Idioma: inglés
• DOI: 10.9781/ijimai.2020.05.006
• Enlaces
  • Texto completo (pdf)
• Dialnet Métricas: 6 Citas
• Resumen

  • Performance prediction systems allow knowing the learning status of students during a term and produce estimations on future status, what is invaluable information for teachers. The majority of current systems statically classify students once in time and show results in simple visual modes. This paper presents an innovative system with progressive, time-dependent and probabilistic performance predictions. The system produces by-weekly probabilistic classifications of students in three groups: high, medium or low performance. The system is empirically tested and data is gathered, analysed and presented. Predictions are shown as point graphs over time, along with calculated learning trends. Summary blocks are with latest predictions and trends are also provided for teacher efficiency. Moreover, some methods for selecting best moments for teacher intervention are derived from predictions. Evidence gathered shows potential to give teachers insights on students' learning trends, early diagnose learning status and selecting best moment for intervention.

• Referencias bibliográficas
  • A. Hellas, P. Ihantola, A. Petersen, V. V. Ajanovski, M. Gutica, T. Hynninen, A. Knutas, J. Leinonen, C. Messom, and S. N. Liao, “Predicting...
  • W. Hämäläinen and M. Vinni, “Classifiers for educational data mining,” Handbook of Educational Data Mining, Chapman & Hall/CRC Data Mining...
  • S. B. Kotsiantis, “Use of machine learning techniques for educational proposes: A decision support system for forecasting students’ grades,”...
  • J.-F. Superby, J. Vandamme, and N. Meskens, “Determination of factors influencing the achievement of the first-year university students using...
  • N. T. Nghe, P. Janecek, and P. Haddawy, “A comparative analysis of techniques for predicting academic performance,” in Frontiers In Education...
  • G. Dekker, M. Pechenizkiy, and J. Vleeshouwers, “Predicting students drop out: a case study,” in Educational Data Mining 2009, 2009.
  • A. K. Hamoud, A. S. Hashim, and W. A. Awadh, “Predicting student performance in higher education institutions using decision tree analysis,”...
  • S. Huang and N. Fang, “Predicting student academic performance in an engineering dynamics course: A comparison of four types of predictive...
  • W. Hämäläinen, J. Suhonen, E. Sutinen, and H. Toivonen, “Data mining in personalizing distance education courses,” in Proceedings of the 21st...
  • B. Minaei-Bidgoli, D. Kashy, G. Kortemeyer, and W. Punch, “Predicting student performance: an application of data mining methods with an educational...
  • C. Romero, S. Ventura, P. G. Espejo, and C. Hervás, “Data mining algorithms to classify students,” in Educational Data Mining 2008, 2008.
  • Y. Freund, R. E. Schapire, et al., “Experiments with a new boosting algorithm,” in ICML, vol. 96, pp. 148–156, 1996.
  • A. Y. Wang and M. H. Newlin, “Predictors of performance in the virtual classroom: Identifying and helping at-risk cyber-students,” The Journal...
  • I. Lykourentzou, I. Giannoukos, G. Mpardis, V. Nikolopoulos, and V. Loumos, “Early and dynamic student achievement prediction in e-learning...
  • Y.-H. Hu, C.-L. Lo, and S.-P. Shih, “Developing early warning systems to predict students’ online learning performance,” Computers in Human...
  • G. Akçapınar, M. N. Hasnine, R. Majumdar, B. Flanagan, and H. Ogata, “Developing an early-warning system for spotting at-risk students by...
  • O. Petropoulou, K. Kasimatis, I. Dimopoulos, and S. Retalis, “Lae-r: A new learning analytics tool in moodle for assessing students’ performance,”...
  • T. Fawcett, “An introduction to {ROC} analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861 – 874, 2006. ROC Analysis in Pattern...
  • J. He, J. Bailey, B. I. Rubinstein, and R. Zhang, “Identifying at-risk students in massive open online courses.,” in AAAI, pp. 1749–1755,...
  • J. A. Méndez and E. J. González, “A control system proposal for engineering education,” Computers & Education, vol. 68, pp. 266 – 274,...
  • C. Villagrá-Arnedo, F. J. Gallego-Durán, R. Molina-Carmona, and F. Llorens-Largo, PLMan: Towards a Gamified Learning System, pp. 82–93. Cham:...
  • C. J. Villagrá-Arnedo, “Sistema predictivo progresivo de clasificación probabilıstica como guı ́ a para el aprendizaje,” 2016. ́
  • C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, pp. 273–297, Sep 1995.
  • C. J. Villagrá-Arnedo, F. J. Gallego-Durán, F. Llorens-Largo, P. CompañRosique, R. Satorre-Cuerda, and R. Molina-Carmona, “Improving the expressiveness...