Abstract
This study aims to identify students who are vulnerable of not being able to pass the Cisco certification examination. The main goal is to develop a model that will determine the significant attributes that influence students’ success in Cisco certification examination. The significant attributes were determined using logistic regression. The researcher conducted preliminary interviews in selected Cisco academies to determine prevailing issues. The study used sets of classification algorithms to generate models that were used for prediction. The main function of the model is to predict the probability of the examinee to pass a Cisco certification examination. The researcher used data mining tools such as WEKA and SPSS to derive the required models. Various data mining classification algorithms were used to identify the most accurate technique best suited for the given data set. The result of the experiment showed that the Logistic Regression algorithm is the most accurate algorithm to be used in the development of the predictive model.
Keywords: data mining, classification algorithm, logistic regression, predictive model, students’ performance
Collaborative Peer Reviewer
This article is collaboratively reviewed by Dr. Masyitah Binti Md Nujid, Ts. Dr. Duratul Ain Binti Tholibon and Shadha Mohamed AL Amri
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