No abstract

No abstract

No abstract

The introduction of computers and ICT related gadgets in education is presently yielding noticeable impacts and it’s been widely accepted to aid learning. Thus, creating an ever increasing desire and urge in school owners, teachers, parents and students to acquire them for learning. The aim of this research is to examine the effect of computer usage proficiency on both teachers’ pedagogical knowledge and students’ academic performance. A total of two hundred (200) respondents selected through a stratified random sampling technique comprising of forty (40) respondents (twenty-five (25) teachers and (15) students) each from five selected Junior Secondary Schools in Ogun state, Nigeria served as the research  population. Collated data were analyzed using frequency counts and Chi-Square at 0.05 alpha level of significance. Also, three null hypotheses were formulated to determine the significant relationship among computer usage proficiency, teachers’ pedagogical competence and students’ academic performance. Research findings revealed that computer assisted approach is the most preferred teacher’s pedagogical approach indicating that old and long-service teachers show poor approach towards employing computers and ICT gadgets to aid teaching and learning. The study further established that teachers’ pedagogical competence is not independent of computer knowledge which will resultantly influence students’ academic performance positively.

The accuracy and reliability of software are critical factors for consideration in the operation of any electronic or computing device.  Although, there exist several conventional methods of software bugs prediction which depend solely on static code metrics without syntactic structures or semantic information of programs which are more appropriate for developing accurate predictive models.  In this paper, software bugs are predicted using a Genetic Algorithm (GA)-based multi-objective optimization model implemented in MATLAB on the National Aeronautics and Space Administration (NASA) dataset comprising thirty-eight distinct factors reduced to six (6) major factors via the use of the Principal Component Analysis (PCA) algorithm with SPSS, after which a linear regression equation was derived. The developed GA- based multi-objective optimization model was well-tried and tested. The accuracy and sensitivity level were also analyzed for successful bug detection. The results for optimal values ranging from   95% to 97% were recorded at an average accuracy of 96.4% derived through MATLAB-implemented measures of critical similarities. The research findings reveal that the model hereto proposed will provide an effective solution to the problem of predicting buggy software in general circulation.