Proposal models for personalization of e-learning based on flow theory and artificial intelligence

Anibal Flores, Luis Alfaro, José Herrera, Edward Hinojosa

Research output: Contribution to journalArticle (Contribution to Journal)peer-review

6 Scopus citations


This paper presents the comparison of the results of two models for the personalization of learning resources sequences in a Massive Online Open Course (MOOC). The compared models are very similar and differ just in the way how they recommend the learning resource sequences to each participant of the MOOC. In the first model, Case Based Reasoning (CBR) and Euclidean distance is used to recommend learning resource sequences that were successful in the past, while in the second model, the Q-Learning algorithm of Reinforcement Learning is used to recommend optimal learning resource sequences. The design of the learning resources is based on the flow theory considering dimensions as knowledge level of the student versus complexity level of the learning resource with the aim of avoiding the problems of anxiety or boredom during the learning process of the MOOC.

Original languageEnglish
Pages (from-to)380-390
Number of pages11
JournalInternational Journal of Advanced Computer Science and Applications
Issue number7
StatePublished - 2019
Externally publishedYes


  • Case based reasoning
  • E-learning
  • Flow-theory
  • Learning resource sequence
  • Massive Online Open Course
  • MOOC
  • Q-learning
  • Reinforcement learning


Dive into the research topics of 'Proposal models for personalization of e-learning based on flow theory and artificial intelligence'. Together they form a unique fingerprint.

Cite this