TY - JOUR
T1 - Intelligent Personalized Learning Management Model using the Case-Based Reasoning Techniques
AU - Maraza-Quispe, Benjamin
AU - Choquehuanca-Quispe, Walter
AU - Rosas-Iman, Victor Hugo
AU - Choquehuayta-Palomino, Simón Angel
AU - Caytuiro-Silva, Nicolás Esleyder
AU - Alcázar-Holguin, Manuel Alfredo
N1 - Publisher Copyright:
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2023
Y1 - 2023
N2 - The application of Information and Communication Technologies in education and the impact of the Internet have fostered online learning, breaking many limiting barriers of traditional education such as space, time, quantity, and cover-age. However, the new proposals affect the quality of educational services, such as linear access to content, standardized teaching structures and methods that are not flexible to the users' learning style. In this context, an Intelligent Model for Personalized Learning Management is implemented in a Virtual Simulation Environment based on Instances of Learning Objects, with the aim of identifying the best learning style of a student to provide them with the best learning object, using a similarity function through Weighted Multidimensional Euclidean Distance. The proposal is validated through a cross validation and experimentation on the MIGAP platform (Intelligent Model of Personalized Learning Management), for the development of courses on Newtonian Mechanics. The results show that the proposed model has a classification efficiency of 100%; above the following models: Simple Logistic with 99.50%, Naive Bayes with 97.98%, Tree J48 with 96.98%, and Neural Networks with 94.97% of success. The application of this model in other areas of knowledge will allow the identification of the best learning style, with the purpose of enabling educational resources, activities, and services to be flexible to the student's learning style, improving the quality of educational services.
AB - The application of Information and Communication Technologies in education and the impact of the Internet have fostered online learning, breaking many limiting barriers of traditional education such as space, time, quantity, and cover-age. However, the new proposals affect the quality of educational services, such as linear access to content, standardized teaching structures and methods that are not flexible to the users' learning style. In this context, an Intelligent Model for Personalized Learning Management is implemented in a Virtual Simulation Environment based on Instances of Learning Objects, with the aim of identifying the best learning style of a student to provide them with the best learning object, using a similarity function through Weighted Multidimensional Euclidean Distance. The proposal is validated through a cross validation and experimentation on the MIGAP platform (Intelligent Model of Personalized Learning Management), for the development of courses on Newtonian Mechanics. The results show that the proposed model has a classification efficiency of 100%; above the following models: Simple Logistic with 99.50%, Naive Bayes with 97.98%, Tree J48 with 96.98%, and Neural Networks with 94.97% of success. The application of this model in other areas of knowledge will allow the identification of the best learning style, with the purpose of enabling educational resources, activities, and services to be flexible to the student's learning style, improving the quality of educational services.
KW - Case-Based Reason
KW - Learning
KW - Learning Style
KW - Management
KW - Model
KW - System
UR - http://www.scopus.com/inward/record.url?scp=85194719089&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85194719089
SN - 1613-0073
VL - 3691
SP - 534
EP - 547
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2023 International Congress on Education and Technology in Sciences, CISETC 2023
Y2 - 4 December 2023 through 6 December 2023
ER -