English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This study aims to determine a predictive model to learn students probabilityto pass their courses taken at the earliest stage of the semester. Tosuccessfully discover a good predictive model with high acceptability,accurate, and precision rate which delivers a useful outcome for decisionmaking in education systems, in improving the processes of conveying knowledgeand uplifting students academic performance, the proponent applies and strictlyfollowed the CRISP-DM (Cross-Industry Standard Process for Data Mining)methodology. This study employs classification for data mining techniques, anddecision tree for algorithm. With the utilization of the newly discoveredpredictive model, the prediction of students probabilities to pass the currentcourses they take gives 0.7619 accuracy, 0.8333 precision, 0.8823 recall, and0.8571 f1 score, which shows that the model used in the prediction is reliable,accurate, and recommendable. Considering the indicators and the results, it canbe noted that the prediction model used in this study is highly acceptable. Thedata mining techniques provides effective and efficient innovative tools inanalyzing and predicting student performances. The model used in this studywill greatly affect the way educators understand and identify the weakness oftheir students in the class, the way they improved the effectiveness of theirlearning processes gearing to their students, bring down academic failurerates, and help institution administrators modify their learning systemoutcomes. Further study for the inclusion of some students demographicinformation, vast amount of data within the dataset, automated and manualprocess of predictive criteria indicators where the students can regulate towhich criteria, they must improve more for them to pass their courses taken atthe end of the semester as early as midterm period are highly needed.

A Predictive Model using Machine Learning Algorithm in Identifying Students Probability on Passing Semestral Course