This research proposed a methodology for identifying the
student's learning style and student’s performance prediction in the online
learning environment using Machine Learning (ML) techniques. Identification of
the student's learning style and performance prediction in the teaching and
learning environment is important in improving both teaching and learning
perspectives. The intention of the research was to investigate about applying
Machine Learning Techniques for the identification of the Learning style of the
students and the prediction of the student’s performance in an online learning
environment based on the Felder Silverman Learning Style (FSLSM) identification
model. The significance of this experiment is that the proposed methodology
considers the combination of access frequency (f) of course materials and total
time (T) students spent on each course activity to reduce the limitations that
occur due to accessing the course modules randomly without any preference in
the online learning environment in learning style identification. A reusable
Moodle time-tracking plugin was created for the data collection procedure.
Three-course modules that were created in accordance with the FSLSM model's
features were used to prepare a real-time dataset. Seven criteria were chosen,
and the features were verified using the Pearson Correlation Coefficient
approach. Each of these course modules had 150 enrolled students. Machine
learning is a widely used technology for the identification of the learning
style and analyzing the data for making predictions. Once the data set was
prepared, the data set was preprocessed and applied five Supervised
Classification Machine learning algorithms as Decision Tree, Logistic
Regression, Random Forest, Support Vector Machine and K-Nearest Neighbors
algorithm. The models were evaluated using Accuracy, Precision, Recall and F1
values. Of the five algorithms for Learning Style identification, the Decision
Tree classifier algorithm performed with the best average accuracy with 93.5% for
Input, 86% for Perception, 89.5 for Processing and 94% for Understanding
dimension. For the grade prediction process the Decision Tree algorithm
performed with a 96% accuracy level. The models were validated using the K-fold
Cross-validation and Standard Deviation values. Mean Squared Error, Bias and
Variance values were considered the evaluation of the underfitting or
overfitting context of the model. For parameter optimization, the Grid Search
Methodology was applied to find the best combination of criterion for the
model. Finally, an application was developed for Identifying the Learning Style
of the Students and performance prediction using the designed Machine learning
model. The Consistency of the ML Model based on the Decision Tree classifier
algorithm were evaluated using the results generated through the developed
application and the results suggested that consistency for taught machine
learning algorithms is often between 85% to 95%, which is an acceptable range.
For the grade prediction, the consistency of the models ranged nearly 89%. The
results generated by the application for identification of the learning style
suggested the combination of learning style for particular students sample as
Global-Mild, Visual- Strong, Sensing- Moderate and Reflective-Strong.
Identification of these combinations of learning styles assists teachers by
giving an insight into which components of the learning content should be
improved in the course designing process. One of the limitations is that though
how much we encourage the students, some of them do not like to engage in the
course works in the online learning environment. These behaviors may lead to
difficulties in conducting the data collection process in a precise manner.
Providing a mechanism to identify and analyze the factors that impact to
increase in the attractiveness of students when reading the course materials or
presentation will be one of the main future directions of this teaching and
learning research paradigm.
Author(s)details:-
Wanniarachchi, WAAM
Faculty of Computing, General Sir John Kotelawala Defence University, Sri
Lanka.
Premadasa, HKS
Centre for Computer Studies, Sabaragamuwa University of Sri Lanka, Sri
Lanka.
Please See the book
here :- https://doi.org/10.9734/bpi/rumcs/v6/8477E
