Text analytics in education has evolved to form a critical component of the future SMART campus architecture. Sentiment analysis and qualitative feedback from students is now a crucial application domain of text analytics relevant to institutions. The implementation of sentiment analysis helps understand learners’ appreciation of lessons, which they prefer to express in long texts with little or no restriction. Such expressions depict the learner’s emotions and mood during class engagements. This research deployed four classifiers, including Naïve Bayes (NB), Support Vector Machine (SVM), J48 Decision Tree (DT), and Random Forest (RF), on a qualitative feedback text after a semester-based course session at the University of Education, Winneba. After enough training and testing using the k-fold cross-validation technique, the SVM classification algorithm performed with a superior accuracy of 63.79%.
Counselling students remains a pre-eminence for most tertiary institutions in Ghana to the extent that institutions now have counselling units that extend to the departmental level. This study used the K-prototype machine learning algorithm to cluster students’ behaviour based on 28 relevant attributes and further proposed a classification model. The analysis of the experimental outcomes using the elbow method reveals the formation of three distinct clusters with decreasing intra-cluster similarities and increasing inter-cluster distances. The first cluster uniquely consists of active learners with three or more roommates, primarily in the first year. The second cluster with the highest membership consists mainly of second-year students who exhibit passive classroom conduct and reside in a two-occupancy hostel. The third cluster contains a mixture of third and final-year students who are highly passive in class and live in a tenancy occupancy of two. After clustering, the K-nearest neighbours, logistic regression, naïve Bayes (NB), and AdaBoost ensemble algorithms were implemented to create a model for future learner cluster prediction. Simulation results using the tenfold cross-validation technique show that AdaBoost (NB) has the highest accuracy of 99.88% with an F-measure score of 0.999 and receiver operating characteristic–area under the curve value of 1.00.
Currently, academic instructors in Ghana have some difficulty in grouping students for projects-based courses because of increasing student numbers. One of the recent challenges educational institutions and instructors are facing is the explosive growth of educational data and how to use this data to improve the quality of teaching. K-means clustering is an unsupervised Data Mining technique for grouping large datasets with insightful similarity patterns to expose hidden trends and behavior in each cluster. The purpose of this research is to apply K-means clustering algorithm to analyze students' clusters for centered project-based learning. This research uses K clusters of 20. The clustering gave a low within cluster Sum of Square Error (SSE) of 3.60889. Clusters 1 and 6 have the highest member set of 32 each whiles clusters 8 and 9 have the lowest member set of 2. The results show that the K-means clustering algorithm is effective in grouping learners based on similar characteristics that indicate their performance. Assessments can also be tailored to suit all categories of learners for efficient results in project-based courses.
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