Year: 2021
Type: article
Abstract: Data Mining plays an important role in the Business world and it helps to the marketing institution to predict and make decisions related to the business’ academic status. Predicting business’ performance becomes more challenging due to the large volume of data in marketing databases. Currently in Malaysia, the lack of existing system to analyse and monitor the performance of the business is not being addressed. There are two main reasons of why this is happening. First, the study on existing prediction methods is still insufficient to identify the most suitable methods for predicting the performance of the business in Malaysian’s institutions. Second, Due to the lack of investigations on the factors affecting student’s achievements in particular courses within Malaysian context. Therefore, a systematically literature review on predicting student performance by the proposed system is a web based which makes use of the mining techniques for the extraction of useful information. This work is dig insight into state and event-based approaches for predicting student performance. Comparative analysis is conducted to suggest regression-based algorithms of state-based framework lack accuracy and correlation-based algorithms under event driven approach outperforms classical regression algorithms. It is also concluded from pedagogical point of view, higher engagement with social media leads to higher final grades.
Authors: Kajal Devi, Harjinder Kaur
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4285360064