Evaluation on the Functionality, Usability and Reliability of the Developed Application for Predicting Students' Performance in Cisco Certification Exam

Abstract

Predictive analytics is a technique that utilizes data mining and probability to forecast unknown future outcomes. Historical data can be used to extrapolate and predict future events. This study is focused on predicting students' performance in cisco certification exam. The data sets were analyzed using various classification algorithms. An application that could predict an exam taker's future outcome was developed using the predictive model discovered from the data sets. The application can be used to aid instructors and school administrators in identifying students that may require intervention at an early stage. The researcher adapted the KDD process and RAD model for software development. This study aims to determine the level of acceptability of the Cisco certification exam prediction system in terms of operational evaluation based on ISO/IEC 25010 metrics by end users such as functionality, usability, and reliability. The system was evaluated by cisco networking experts. The level of response for each criterion was based on Five-Point Likert Scale. Overall, the Cisco Certification Exam Prediction System was evaluated by experts as “Acceptable”.