Model for Prediction of Student Grades using Data Mining Algorithms

Kdhaiya Sulaiman Al Shibli, Amal Sulaiman Sayed Al Abri, Linitha Sunny, Nandakishore Ishwar and Sherimon Puliprathu Cherian
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Kdhaiya Sulaiman Al Shibli: Arab Open University, Sultanate of Oman
Amal Sulaiman Sayed Al Abri: Arab Open University, Sultanate of Oman
Linitha Sunny: Saintgits College of Engineering, India
Nandakishore Ishwar: Saintgits College of Engineering, India
Sherimon Puliprathu Cherian: Arab Open University, Sultanate of Oman

European Journal of Information Technologies and Computer Science, 2022, vol. 2, issue 2, 1-6

Abstract: There has been a rapid growth in the educational domain since education has become an important need. Data is collected in this domain which can be put to meaningful use to derive a lot of benefits to the students. Predicting student performance can help students and their teachers keep track of student progress. Mining Educational data helps to uncover invisible patterns, relationships, or trends in the unstructured data and helps in delivering logical and meaningful recommendations. Several kinds of research are being conducted across the world to analyze the data regarding student learning to identify the factors affecting performance and to provide support to students to help them improve. It is the objective of the proposed research to conduct a detailed study in the Sultanate of Oman regarding the existing toolsets, systems, and mode of data collection that are used currently in the Education sector for the prediction of Student Grades. Taking this as the baseline, later a model that will feature different prediction algorithms which are more accurate in predicting the grades of a student will be developed. The objective of this research is to understand the various predictive methods used to predict student performance and to propose a machine learning model to predict student grades.

Keywords: Classification techniques; data mining; educational data mining; student grade prediction (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:epw:comput:v:2:y:2022:i:2:id:10047

DOI: 10.24018/compute.2022.2.2.47

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