Predictive Analytics Assessment Tool For Information Communication Technology (ICT) Courses Of Technical And Vocational Education And Training (TVET) School In The Philippines

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AUTHOR(S)

Joseph G. Gonzales, Ace C. Lagman, Roman M. De Angel

KEYWORDS

CHED, Decision Tree, NC Certifications, Predictive Analytics, TESDA, TVET, Research Methods.

ABSTRACT

The project is aims to develop a predictive analytics tool that will help TVETs Schools in the Philippines identify possible students who would fail in the NC certification so necessary interventions can be provided. The study aims to utilize the power of machine learning algorithms particular decision tree algorithm using J48 method to extract significant rules to embed in the proposed decision support system. This project will also adopt predictive analytics and will create a model to examine the students’ performance inside the classroom, and predict students’ performance, and help the institute to determine the possibility of students to pass the NC certification up to at least level II. The research methodologies used on this study were quantitative and qualitative methods. Qualitative research provides insights in the problems in developing hypotheses, while quantitative research has been used to measure the difficulty by having statistical data that provides figures. The study is mend to judge able to scrutinize summarize opinions from vast data gathered in the form of questionnaire and surveys.

REFERENCES

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