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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.
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References
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Authors and Affiliations
Universidad Carlos III de Madrid, Avenida Universidad 30, 28911, Leganés, Madrid, Spain
José A. Ruipérez-Valiente, Pedro J. Muñoz-Merino & Carlos Delgado Kloos
IMDEA Networks Institute, Av. del Mar Mediterráneo 22, 28918, Leganés, Madrid, Spain
José A. Ruipérez-Valiente
- José A. Ruipérez-Valiente
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- Pedro J. Muñoz-Merino
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- Carlos Delgado Kloos
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Correspondence toJosé A. Ruipérez-Valiente.
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Editors and Affiliations
University of British Columbia, Vancouver, British Columbia, Canada
Cristina Conati
Computer Science Department, Worcester Polytechnic Institute, Worcester, Massachusetts, USA
Neil Heffernan
Department of Computer Science and Software Engineering, University of Canterbury, Christchurch, New Zealand
Antonija Mitrovic
E.T.S.I. Informática, Universidad National de Educacion a Distancia, Madrid, Spain
M. Felisa Verdejo
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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. https://doi.org/10.1007/978-3-319-19773-9_110
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