Philippine E-Journals

PREDICTION AND CLASSIFICATION ANALYSIS OF STUDENT’S ACADEMIC PERFORMANCE BASED ON NATIONAL AND INSTITUTIONAL STANDARDS TOWARDS AN IMPROVEMENT OF INSTRUCTIONAL AND ASSESSMENT METHOD

Anna Liza A Ramos | Jazmin Tan | Emily Almendral | Arnold Aranaydo

 

Abstract:

The goal of the assessment is to measure the learning performance of the learners. This study investigates learners' academic performance in English, Math, and Science using Machine Learning to classify and predict performance, thus determining the weak areas needing more focus. The study utilized National Career Assessment Exam (NCAE) with 263 datasets, the Center for Educational Measurement (CEM) with 861 datasets, and Institutional Academic Performances with 1,777 datasets. Results showed that the prediction results highlighted English as the subject to have the highest impact across all NCAE assessment components with a p-value of 0.000; for Institutional Academic Performances, "Mathematics" was highlighted for having the highest percentage of competent students of 36.97%. CEM that most of the students who took the assessment belong to "Did Not Meet the Standard" across different subjects of 70.90%. The variations among these results are still considerable since there are significant differences present in the design of learning activities, type of assessments, and learners' preparedness level. This finding of the study will serve as input to educators to further assess the current learning materials to ensure better student performance.