Development of a Model Using Data Mining Technique to Test, Predict and Obtain Knowledge from the Academics Results of Information Technology Students

Wisam Ibrahim, Sanjar Abdullaev, Hussein Alkattan, Oluwaseun A. Adelaja and Alhumaima Ali Subhi
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Wisam Ibrahim: Department of System Programming, South Ural State University, Chelyabinsk 454080, Russia
Sanjar Abdullaev: Department of System Programming, South Ural State University, Chelyabinsk 454080, Russia
Hussein Alkattan: Department of System Programming, South Ural State University, Chelyabinsk 454080, Russia
Oluwaseun A. Adelaja: Information Communication and Technology Department, Lagos State University, Lagos 102101, Nigeria
Alhumaima Ali Subhi: Department of System Programming, South Ural State University, Chelyabinsk 454080, Russia

Data, 2022, vol. 7, issue 5, 1-18

Abstract: Due to the huge amount of data obtained from students’ academic results in most tertiary institutions such as the colleges, polytechnics and universities, data mining has become one of the most effective tools for discovering vital knowledge from students’ dataset. The discovered knowledge can be productive in understanding numerous challenges in the scope of education and providing possible solutions to these challenges. The main objective of this research is to utilize the J48 decision algorithm model to test, classify and predict the students’ dataset by identifying some important attributes and instances. The analysis was conducted on the final year students’ academic results in C# programming amongst five universities which was imported in csv excel file dataset in WEKA environment. These training datasets contained the scores obtained in the examinations, grade remarks, grades, gender, and department. The knowledge extracted for the prediction model will help both the tutors and students to determine the success grade performance in the future. Flow lines, J48 decision trees, confusion matrices and a program flowchart were generated from the students’ dataset. The KAPPA value obtained from the prediction in this research ranges from 0.9070–0.9582 which perfectly agrees with the standard for an ideal analysis on datasets.

Keywords: data mining tools; WEKA; J48 algorithm; KAPPA value; predict; confusion matrix; csv (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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