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Data Analytics for Volunteer-Based Educational Application | IEEE Conference Publication | IEEE Xplore

Data Analytics for Volunteer-Based Educational Application


Abstract:

In the world of technology, data have been available easily and in huge amounts. Because of the large amounts of data, Educational Data Mining (EDM) is increasingly gaini...Show More

Abstract:

In the world of technology, data have been available easily and in huge amounts. Because of the large amounts of data, Educational Data Mining (EDM) is increasingly gaining more importance. Educational data mining is trending as it is the analysis method for analyzing educational data. It involves checking the relationship between the characteristics of students and which features affect their final grades the most. It can also involve predictive modeling by predicting the final grades of students in the future to help educational institutes rescue failing students before they actually fail. Predictive analysis was the main focus in past years where most researchers targeted predicting grades of students. However, not all educational institutes are able to collect the amount of data suitable for machine learning models to achieve good accuracy. That is why the main target of the work presented in this paper is to develop an interactive interface that gives the ability to educational institutes to check by themselves the relation between different factors using correlation mining. The output of the tool is visual with message boxes to make it understandable by users. That is the best way to discover hidden patterns and trends without the need for a large amount of data in addition to removing the cost of deploying machine learning models. The second goal of the work is to give teachers the ability to check the quality of the content of their slides to help them provide a better learning process.
Published in: 2023 IEEE AFRICON
Date of Conference: 20-22 September 2023
Date Added to IEEE Xplore: 31 October 2023
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Conference Location: Nairobi, Kenya

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