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Predicting students’ performance with a reasonable degree of accuracy is beneficial in finding the students who perform poorly when the learning process begins. The main objective of any educational institution is to render the best chances for education and skills for the students. To reach this goal, it is essential to recognize that students need extra help and meaningful steps to improve their results.
Malaysia is experiencing a moderate increase in the unemployment rate each year. The projected unemployment rate for 2017 is 3.42221%. Future unemployment will rise slightly over the next ten years. Then the remaining four years began to grow at a rate of at least 3.8 per cent from 2023 to 2026 (Ramli et al., 2018). This is a highly alarming figure, given that the findings indicate that unemployed graduates from public universities have the highest unemployment rate. However, the education sector has also revealed that unemployment has increased. The first objective is to analyze the critical variables affecting Malaysia’s unemployment rate. According to the results, general factors such as inflation and population growth in Malaysia significantly impact the unemployment rate. In short, graduate students need to understand the situation and prepare for the uncertainty associated with this unemployment. Governments must also be accountable for taking appropriate action to tackle unemployment and not affect other social and economic conditions.
Today, due to the tremendous importance of this topic for the advancement of nations globally, the prediction of student performance is becoming increasingly significant. The educational process is entirely dependent on producing generations that are capable of leading this country and its steps toward progress in every area (scientific, economic, social and military, etc.). Consequently, one of the key criteria that motivate governments to ensure that academic institutions represent vast and scrupulous efforts to move the academic process towards continuous and improving advancement is advancing the academic process. Prediction can help in getting future knowledge. The more the volume of data is, like in massive databases, the more the forecasting is generated; this process is referred to as data mining which helps find the concealed information by examining various data sources associated with diverse domains, including social enterprises, healthcare, and academics (Chen et al., 2020; Miguéis et al., 2018). Relevant information is extracted for analyses of academic sources using EDM (Educational Data Mining), a new discipline for discovering important information using technology (Bakhshinategh et al., 2018).
The efficacy of learning environments improves as a result of statistical analysis and deep learning analysis. There has been a rapid increase in the significance of EDM currently due to the rise in the data gathered, based on the academic data obtained from various e-learning systems, along with the progress made in conventional academic systems. It is a kind of education in which the emphasis is on the instructor rather than the students, and it has drawbacks such as large class sizes, a lack of individual attention, and the use of traditional teaching tools such as black and whiteboards, desks, and pens. EDM's strengths rise from linking data from different domains. It is involved with feature extraction to help in the development of the academic process from the tremendous amount of data that the institute provides. Educational data mining refers to the processes of analyzing, investigating, forecasting, clustering, and classifying data found in educational institutions. (Chen et al., 2018).