Özet:

This research focuses on developing a model prototype of predicting the possible student passers from a pool of board takers. The project focused on the performance of students who will take the board exam. Several attributes were included and identified as variables for prediction such as academic grades, age, gender and preboard scores. The research project identified who amongst the pool of board takers will pass or fail the board exam and the passing percentage of the institution if it qualifies the national passing rate. The model prototype served as a preparatory tool for board takers to prepare for the examination and also an aid to the institution to plan and train even more their students before taking the board exam. Prediction is incorporated in the prototype using linear regression analysis of data mining. The predicted value was validated using a machine learning tool to identify its accuracy.

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