Reviewer For Licensure Examination For Agriculture in PHP

The research aims to develop a gamified application for the Licensure Examination for Teachers' (LET) reviewer. The study intends to increase the LET performance of the students by forecasting the possible LET result and give the area of improvement where the students could concentrate on their review. Six algorithms (Naïve Bayes, Linear Regression, SVM, Logistic Regression, Neural Network, kNN) were tested and evaluated to be used in forecasting LET result. Linear Regression was identified to be the algorithm that was integrated into the system. Descriptive analytics and the linear regression model were used in giving an area of improvement and forecast LET Result. The gamification elements present in the gamified mobile application were game mechanics, narrative design, musical score, content and skills, visual, aesthetic design, levels, leaderboard, feedback, and points. Based on the evaluation results from the students, the gamified application was able to engage, enjoy and motivate them while playing. It can also be concluded that this gamification application could help in improving the LET review and results. Overall, the program coordinators and IT Experts were satisfied with the system of the gamified application. It is recommended to improve the system's adaptability and compatibility and to further identify other factors or attributes to be used in forecasting the possible LET Result. Furthermore, to implement the system for 5 years to have more data to make the model more accurate. Also, check the possibility of the system being an Income Generating Project.