000			02704nam a22002777a 4500
003			KOHA
005			20230905091438.0
008			230905d2023 cy ||||| m||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 3070 _bO46 2023
100	1		_aOmolafe, Olamideji Michael
245	1	0	_aSTUDENT PERFORMANCE PREDICTION FROM ONLINE HANDWRITING BIOMETRICS / _cOLADIMEJI MICHEAL OMOLAFE; SUPERVISOR: ASST. PROF. DR. YASEMIN BAY
264			_c2023
300			_avii, 68 sheets; _c31 cm. _eIncludes CD
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Management Information Systems Department
504			_aIncludes bibliography (sheets 54-62)
520			_aABSTRACT 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
650			_aBiometry _vDissertations, Academic
650			_aPerformance _vDissertations, Academic
700	1		_aBay, Yasemim _esupervisor
942			_2ddc _cTS
999			_c290931 _d290931