The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality is to identify factors affecting academic performance and then trying to resolve weakness of these factors. The specific objective of the proposed research work is to find out if there are any patterns in the available data (student and courses records) that could be useful for predicting students' performance. The study involved a sample of 150 students collected from Najran University students in Saudi Arabia. The data was captured and arranged with the use of statistical package for social sciences (SPSS) and data mining tool (clementine). Developing an accurate student's performance prediction model is challenging task. Data mining based model were used to identify which of the known factors can give an early indicator of expected performance. This paper employs both feature reduction and classification technique to reduce error rate. The experimental results reveal significant relationships between including both practical work and assignments in course and its success rate. But, on the other hand the number of given assignment has a negative impact on course academic performance. In context of factors affect student academic performance, the most affecting factor is student attendance in class in addition to final exam and mid exam grades.

No abstract

No abstract

:Software metrics are quantitative estimates for software product attributes which guide us in taking managerial and technical decisions [1]. The current used software metrics depend on syntactical attributes of source code at the same time they ignore its semantic aspects. This fact motivates us to focus on semantic metrics instead of traditional used metrics. Moreover, semantic metric are more precise than syntactic ones, it is able to capture the semantic defects of the software products. In this paper, the researcher has been made an attempt to survey of existing research on software metrics along with potential research challenges and opportunities.