| dc.description.abstract | E-Learning is becoming one of the most effective training approaches. In particular, blended
learning is considered a useful methodology for supporting and understanding students and
their learning issues. Thanks to e-Learning platforms and their collaborative tools, students
can interact with other students and share doubts on certain topics. However, teachers often
have less access of students’ problems, emotions and moods they have in classrooms
towards the current teaching due to their differences in cognitive capacities, methodology
the lecturer employs in among others. A solution for ensuring the privacy of communication
among students could be the adoption of a Sentiment Analysis methodology for the detection
of the classroom mood during the learning process. This study aimed at improving teaching
and learning on the e-learning platform by enhancing the platform with a sentiment analysis
system that automatically analyses students’ feedback in real-time and presents the analysis
results to the lecturer. The study extracted students’ opinion from teaching evaluation forms
of Uganda Martyrs University, preprocessed the data, trained and tested four classifiers
(Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), and Random
Forest (RF)). The test results showed that RF was the most accurate followed by NB and
SVM with the same accuracy and the least was DT. The respective accuracies were; 86%,
84%, and 77% respectively. RF was deployed into a web application to enhance the
elearning platform.
Keywords: E-learning, Sentiment analysis model, Support Vector Machine, Naïve Bayes,
Decision Tree, and Random Forest. | en_US |
