Computer programming is now one of the most critical competencies taught in computer courses. [1]. Students require any assistance they can get when learning programming in order to acquire the necessary abilities to excel in the field of computing [2]. This paper aims to investigate the C compiler error logs of Computer Science freshmen students. A prototype was developed and pilot-tested to obtain C source code snippets focusing on assignment statements. The dataset consisting of 1013 logs were extracted from the initial prototype then followed the data science approach of [3] for pre-processing. A Person correlational analysis was conducted on eight features to investigate the relationship between all variables in the dataset. Results of the study show that there is a strong relationship between wrong expression and operator (0.806), wrong expression and numeric value (0.794), operator and numeric value (0.663). Implications of this study is also helpful to computing instructors to improvise the delivery of their teaching pedagogy.

In the Philippines, there is still a barrier language between the deaf and hard of hearing and abled communities. This could be attributed to how time and resource-consuming it is to learn FSL. This study aims to develop an FSL emergency sign detection in hopes to diminish the prejudice against dhh and the barrier that prevent them from being entirely accepted by non-dhh. To test the hypothesis of how fast hand detection works in emergencies using mobile, YOLO v4 tiny was used as the main algorithm for FSL detection for selected phrases often used in emergencies backed by an online survey for its usefulness. The results yielded an average accuracy of 85% in detecting the hand signs in different background environments with ample lightning and that using the mobile application to detect emergency hand signs is fast and accurate enough when in different backgrounds.