UNVEILING LEARNER EMOTIONS: SENTIMENT ANALYSIS OF MOODLE-BASED ONLINE ASSESSMENTS USING MACHINE LEARNING

Aim/Purpose The study focused on learner sentiments and experiences after using the Moo-dle assessment module and trained a machine learning classifier for future senti-ment predictions. Background Learner assessment is one of the standard methods instructors use to measure students' performance and ascertain successful teaching objectives. In pedagogi-cal design, assessment planning is vital in lesson content planning to the extent that curriculum designers and instructors primarily think like assessors. Assess-ment aids students in redefining their understanding of a subject and serves as the basis for more profound research in that particular subject. Positive results from an evaluation also motivate learners and provide employment directions to the students. Assessment results guide not just the students but also the instruc-tor.Methodology A modified methodology was used for carrying out the study. The revised methodology is divided into two major parts: the text-processing phase and the classification model phase. The text-processing phase consists of stages includ-ing cleaning, tokenization, and stop words removal, while the classification model phase consists of dataset training using a sentiment analyser, a polarity classification model and a prediction validation model. The text-processing phase of the referenced methodology did not utilise tokenization and stop words. In addition, the classification model did not include a sentiment analyser.Contribution The reviewed literature reveals two major omissions: sentiment responses on using the Moodle for online assessment, particularly in developing countries with unstable internet connectivity, have not been investigated, and variations of the k-fold cross-validation technique in detecting overfitting and developing a reliable classifier have been largely neglected. In this study we built a Senti-ment Analyser for Learner Emotion Management using the Moodle for assess-ment with data collected from a Ghanaian tertiary institution and developed a classification model for future sentiment predictions by evaluating the 10-fold and the 5-fold techniques on prediction accuracy.Findings After training and testing, the RF algorithm emerged as the best classifier using the 5-fold cross-validation technique with an accuracy of 64.9%.Recommendations for PractitionersRecommendations for Researchers Instead of a closed-ended questionnaire for learner feedback assessment, the open-ended mechanism should be utilised since learners can freely express their emotions devoid of restrictions. Feature selection for sentiment analysis does not always improve the overall ac-curacy for the classification model. The traditional machine learning algorithms should always be compared to either the ensemble or the deep learning algo-rithmsImpact on Society Understanding learners' emotions without restriction is important in the educa-tional process. The pedagogical implementation of lessons and assessment should focus on machine learning integrationFuture Research To compare ensemble and deep learning algorithms