Mejorando la evaluación de juegos serios aplicando analíticas de aprendizaje y técnicas de minería de datos

  1. Cristina Alonso Fernández
Supervised by:
  1. Baltasar Fernández Manjón Director
  2. Manuel Freire Morán Director
  3. Iván Martínez Ortiz Director

Defence university: Universidad Complutense de Madrid

Year of defence: 2021

  1. Gonzalo Méndez Pozo Chair
  2. Guillermo Jiménez Díaz Secretary
  3. António José Mendes Committee member
  4. Ruth Cobos Pérez Committee member
  5. Yoon Jeon Kim Committee member
  1. Ingeniería del Software e Inteligencia Artificial

Type: Thesis


Serious games are highly motivational resources effective to teach, raise awareness, or change the perceptions of players. To foster their application in education, teachers and institutions require clear and formal evidences to assess students' learning while they are playing the games. However, traditional assessment techniques rely on external questionnaires, typically carried out before and after playing, that fail to measure players' learning while it is happening. The multiple interactions carried out by players in the games can provide more precise information about how players play, and even be used to assess them. In this regard, game learning analytics techiques propose the collection and analysis of such interactions for multiple purposes, including assessment. The potentially large game learning analytics data collected can be further analyzed with data mining techniques to discover unexpected patterns and to provide measures to evaluate the effect of fames on their players and assess their learning...