Modeling the Performance of Electronics and Communications Engineering Students in the Licensure Examination


Victor Hafalla Jr., MAAS, REE | Elisabeth Calub, MSIT


The study aimed to profile board passers and non-passers of the Electronics Engineering licensure examination using 64 respondents from the School of Engineering and Architecture who took their exams during the 2009 licensure examinations onto the different pre-determined variables and develop a discriminant function model using derived factor constructs from these variables. Results revealed that there are marked significant differences between passers and non-passers on their grade point average, number of failed subjects and the number of repeats of taking the board exam while the time interval between those takes, and the time interval between their graduation to their first take did not pose significant differences. Results also showed that both groups posed the lowest passing rate on the areas of general engineering education and communication of their licensure examination. Results of the factor extraction using orthogonal rotation revealed three underlying factor constructs, namely, 1. Student’s Academic Demographics, 2. Student’s Exam Demographics, 3. Interval Between Graduation and Exam which accounted for 81.65% of the total variability in the data. Results of the derivation of the discriminant model produced a discriminant function which was statistically significant to effect group discrimination. However, a low canonical correlation value of the function and a high Wilk’s lambda suggested that the group’s centroids were not satisfactorily separated onto the canonical discriminant axis. The classification matrix showed a marked significant classification hit ratio of 73.4% and was seen valid by the proportional chance criterion and Press’ Q statistics. The function gave a much higher classification ratio when classifying non-passers (91.7%) against passers (18.8%). The school should adopt a retention policy taking into consideration the number of failed subjects which affects the students’ probability of passing their licensure exam. The school should also look into the possibility that their curricular offerings for the subjects on the areas of general engineering education and communications might not be responsive to the type of questions posed in the licensure examination. Also, the school should strengthen its review classes by inviting outside reviewers and haul-in non-passers to their offered review classes to increase their chances of  passing their licensure exam. Since the discriminant function was derived using only a moderate number of predictor variables, it would be interesting to re-estimate the discriminant function by incorporating a much broader set of predictor variables.

Key Words: Licensure examination, Board performance, Modeling, Discriminant function, Factor analysis

Source: UB Research Journal, Vol. XXXV, No. 1, January – June 2011