| Summary: | Deep learning (DL) is recognized as a breakthrough in the educational technology arena, more so in the sense that it can be applied for forecasting student performance and critical issues in academic systems. This systematic review is used to investigate advances in the DL-based system-to-predicting student performance and emphasizes its applicability, methodologies, and limitations. The paper analyses key technologies such as neural networks (NNs) and ensemble models used in educational data mining. The paper also points out limitations in previous studies, for example, data imbalance model interpretability, and issues of scalability. This review highlights the potential of DL to improve educational quality, provide personalized learning experiences, and mitigate learning hazards by synthesizing ideas from different studies. Future directions will comprise hybrid models, improvements in data preprocessing, and merging with real-time educational systems to optimize the performance of the prediction model in several academic environments. For this review, 58 papers were collected from the year 2017-2024 respectively based on DL in education, Risk in education, and student education performance analysis. Subsequently, the aim, technique used, dataset used, performance score attained, significance, and limitations of the existing studies were discussed in this review.
|
|---|