Factors Affecting Information Technology Studentsa Motivation; Case Study: Najran University, Saudi Arabia

The study of computer science and information systems requires commitment and dedication Students must learn above and beyond the standard requirements in order to compete in the job market Hence the motivation is the major driver to accomplish such requirements This paper investigates the possible factors affecting students motivation at Computer Science and Information System College in Najran University To find the best ways to improve student performance academic planning and improve college performance in general was behind the reason for the authors to study the motivation factors Fifty undergraduate students from a computer and information college participated in this research The students completed Academic Intrinsic Motivation Questionnaire MSLQ questionnaires In addition instructors answered interview questions related to factors affecting their students motivation Instructors who participated in seminars and interview believes the English language is the major barrier affecting motivation In addition instructors believes incentives and strict regulation may help improving students motivation Adding to incentives the sense of completion is missing beside no enough recognition from college and instructors The survey results shows students agreed that the Needs and Mastery factors motivate them Whilst the students wasn t able to decide on Power Fear Authority and Peers motivation factors The paper provides recommendations for college leaders and instructors on best ways to improve students motivation in order to reach better performanc

Feature selection for high dimensional data: An evolutionary filter approach.

Problem statement: Feature selection is a task of crucial importance for the application of machine learning in various domains. In addition, the recent increase of data dimensionality poses a severe challenge to many existing feature selection approaches with respect to efficiency and effectiveness. As an example, genetic algorithm is an effective search algorithm that lends itself directly to feature selection; however this direct application is hindered by the recent increase of data dimensionality. Therefore adapting genetic algorithm to cope with the high dimensionality of the data becomes increasingly appealing. Approach: In this study, we proposed an adapted version of genetic algorithm that can be applied for feature selection in high dimensional data. The proposed approach is based essentially on a variable length representation scheme and a set of modified and proposed genetic operators. To assess the effectiveness of the proposed approach, we applied it for cues phrase selection and compared its performance with a number of ranking approaches which are always applied for this task. Results and Conclusion: The results provide experimental evidences on the effectiveness of the proposed approach for feature selection in high dimensional data