TY - JOUR
T1 - Proposal models for personalization of e-learning based on flow theory and artificial intelligence
AU - Flores, Anibal
AU - Alfaro, Luis
AU - Herrera, José
AU - Hinojosa, Edward
N1 - Publisher Copyright:
© 2018 The Science and Information (SAI) Organization Limited.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Case based reasoning
KW - E-learning
KW - Flow-theory
KW - Learning resource sequence
KW - Massive Online Open Course
KW - MOOC
KW - Q-learning
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85070111957&partnerID=8YFLogxK
U2 - 10.14569/ijacsa.2019.0100752
DO - 10.14569/ijacsa.2019.0100752
M3 - Artículo (Contribución a Revista)
AN - SCOPUS:85070111957
SN - 2158-107X
VL - 10
SP - 380
EP - 390
JO - International Journal of Advanced Computer Science and Applications
JF - International Journal of Advanced Computer Science and Applications
IS - 7
ER -