Authors :
P.E. Akinwole; O. K. Boyinbode; M.T. Kinga; P.K. Olotu
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/3zbwauv7
Scribd :
https://tinyurl.com/2udd5s53
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR535
Abstract :
A notable obstacle in the field of education is
the restricted flexibility of traditional teaching
approaches. These approaches frequently take a
consistent stance, disregarding the wide range of
learning preferences that pupils possess. This leads to a
decrease in student motivation and engagement, which
in turn produces below-average learning outcomes. This
research focuses on creating an adaptive learning system
that classifies learners using the Felder-Silverman model
in order to overcome these problems. After then, this
system creates customized recommendations based on
user choices in an effort to improve learning results. In
order to keep enhancing the system's efficacy, the study
have also included a feedback mechanism and
performance evaluation.
Keywords :
Personalized Learning, Mobile Learning, Recommendation Systems, user Preferences, Learning Experiences, Student Engagement, Adaptive Learning, Learning Styles, Educational Technology.
A notable obstacle in the field of education is
the restricted flexibility of traditional teaching
approaches. These approaches frequently take a
consistent stance, disregarding the wide range of
learning preferences that pupils possess. This leads to a
decrease in student motivation and engagement, which
in turn produces below-average learning outcomes. This
research focuses on creating an adaptive learning system
that classifies learners using the Felder-Silverman model
in order to overcome these problems. After then, this
system creates customized recommendations based on
user choices in an effort to improve learning results. In
order to keep enhancing the system's efficacy, the study
have also included a feedback mechanism and
performance evaluation.
Keywords :
Personalized Learning, Mobile Learning, Recommendation Systems, user Preferences, Learning Experiences, Student Engagement, Adaptive Learning, Learning Styles, Educational Technology.
