A Predictive Model of Learning Gains for a Video and Exercise Intensive Learning Environment

  • José A. Ruipérez-Valiente
  • Pedro J. Muñoz-Merino
  • Carlos Delgado Kloos
Conference paper

DOI: 10.1007/978-3-319-19773-9_110

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9112)
Cite this paper as:
Ruipérez-Valiente J.A., Muñoz-Merino P.J., Delgado Kloos C. (2015) A Predictive Model of Learning Gains for a Video and Exercise Intensive Learning Environment. In: Conati C., Heffernan N., Mitrovic A., Verdejo M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science, vol 9112. Springer, Cham

Abstract

This work approaches the prediction of learning gains in an environment with intensive use of exercises and videos, specifically using the Khan Academy platform. We propose a linear regression model which can explain 57.4% of the learning gains variability, with the use of four variables obtained from the low level data generated by the students. We found that two of these variables are related to exercises (the proficient exercises and the average number of attempts in exercises), and one is related to both videos and exercises (the total time spent in both) related to exercises, whereas only one is related to videos.

Keywords

Educational data mining Prediction Learning analytics 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • José A. Ruipérez-Valiente
    • 1
    • 2
  • Pedro J. Muñoz-Merino
    • 1
  • Carlos Delgado Kloos
    • 1
  1. 1.Universidad Carlos III de MadridLeganés, MadridSpain
  2. 2.IMDEA Networks InstituteLeganés, MadridSpain

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