STUDENT PERFORMANCE PREDICTION FROM ONLINE HANDWRITING BIOMETRICS /
OLADIMEJI MICHEAL OMOLAFE; SUPERVISOR: ASST. PROF. DR. YASEMIN BAY
- vii, 68 sheets; 31 cm. Includes CD
Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Management Information Systems Department
Includes bibliography (sheets 54-62)
ABSTRACT Traditionally, biometrics has been associated with fingerprint mapping, facial recognition, and retina scans. However, the recognition of individuals merely based on their handwriting has drawn the interest of researchers in the subject of handwriting biometrics. This new discipline has promising prospects for predicting student academic performance from their online handwriting biometrics. The available handwriting biometric database for this research is the CIU handwritten database, which includes student performance in addition to their identification labels. 804 handwriting samples collected from 134 students are contained in the database. This research investigates the potential of handwriting biometric features in predicting academic performance. Two datasets were extracted from the CIU handwritten database, with the first consisting of 134 participants and the second consisting of 97 participants. Success scores were classified as good, average, or poor in the first dataset, and as good or poor in the second dataset. NaiveBayes, J48 decision tree, RandomForest, and KNN models were used to classify the success scores based on handwriting biometric features. Results showed that the RandomForest model outperformed the other models in both datasets, with the highest accuracy achieved after feature selection. The results of the empirical analysis based on the online handwriting biometrics show that it is possible to predict student academic performance from their online handwriting biometrics. Keywords: Biometric Features, Handwriting Biometrics, Performance Prediction, Students Performance