Adaptive E-Learning Design By Artificial Neural Network Techniques: A Case of Ethiopian Higher Learning Institution
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Date
2010-06
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Addis Ababa University
Abstract
An e-learning system is expected to recognize the different learner characteristics, the
complex learning process that can be influenced by learner characteristics such as previous
knowledge, learning styles, background, etc. The system is also expected to analyze students
need to use learning material and an order of presentation that depends upon their own
characteristics and needs. More adaptivity is expected from e-learning systems since students
need to be considered independent.
The purpose of this study is to investigate and demonstrate the possibility of developing
Adaptive e-learning model for Ethiopian higher learning institutions learners.
In this research, an experiment was made to build a learner model for an adaptive e-learning
model. Firstly, Information independent of the course like learner goals and background and
experiences as well as domain dependent data like prior knowledge about the course and
learner expectations from the course were assessed. Secondly, an experimental learning
styles predictive model is built through neural network data mining modeling technique.
From the analysis of the assessment in this study, Ethiopian higher learning institutions
learners’ students have varying learning goals, learner background and experiences,
preferences, learning styles as well as prior knowledge about the course and their
expectations from the course.
The learning style predictive model is experimented by neural network data mining technique
from student learning style questionnaire that received from 1296 respondents. Fourteen
variables were selected from the twenty variables for each model. From the total dataset,
80%, 10% and 10% of the data is used for training, validating and testing purpose
respectively.
Neural network model is built which can correctly classify 93.07%, 92.30%, 96.15% and
94.61% of the validation set for visual-auditory, sequential-global, activist-reflective,
sensitive-intuitive and learning styles respectively. Lastly, an adaptive e-learning model
prototype was designed.
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Keywords
Adaptive E-Learning Design