  This study aims to determine a predictive model to learn students probability
to pass their courses taken at the earliest stage of the semester. To
successfully discover a good predictive model with high acceptability,
accurate, and precision rate which delivers a useful outcome for decision
making in education systems, in improving the processes of conveying knowledge
and uplifting students academic performance, the proponent applies and strictly
followed the CRISP-DM (Cross-Industry Standard Process for Data Mining)
methodology. This study employs classification for data mining techniques, and
decision tree for algorithm. With the utilization of the newly discovered
predictive model, the prediction of students probabilities to pass the current
courses they take gives 0.7619 accuracy, 0.8333 precision, 0.8823 recall, and
0.8571 f1 score, which shows that the model used in the prediction is reliable,
accurate, and recommendable. Considering the indicators and the results, it can
be noted that the prediction model used in this study is highly acceptable. The
data mining techniques provides effective and efficient innovative tools in
analyzing and predicting student performances. The model used in this study
will greatly affect the way educators understand and identify the weakness of
their students in the class, the way they improved the effectiveness of their
learning processes gearing to their students, bring down academic failure
rates, and help institution administrators modify their learning system
outcomes. Further study for the inclusion of some students demographic
information, vast amount of data within the dataset, automated and manual
process of predictive criteria indicators where the students can regulate to
which criteria, they must improve more for them to pass their courses taken at
the end of the semester as early as midterm period are highly needed.
