Methods of Overcoming Data Waste in Education: A Systematic Review

This research aimed to perform a systematic review of recent scientific studies that addressed the topic of educational data mining by highlighting recent research trends and reviewing the best practices of smart technology in this field. The current research was restricted to scientific studies and conferences in English that were published through the IEEE database between 2020 and October 2022. After applying the PRISMA form and reviewing and documenting the method, 25 papers were identified that matched the criteria. The outcomes of the current research concluded that the trend of prediction was one of the most common research trends in the domain of educational data mining. Moreover, this trend varied remarkably in its coverage of educational scientific aspects. On the other hand, the outcomes demonstrated that the trend of recommending the most appropriate specialised tracks for learners was one of the least common research trends. With regard to intelligent technical practices, the outcomes of the research revealed that the most used and mature intelligent technologies in practical frameworks and algorithms were intended for prediction purposes, and these were shown to greatly enhance researchers’ ability to reach accurate and logical results that are applicable in real educational contexts. KEYWORDS Artificial intelligence, machine learning, algorithms, neural networks, educational data, educational institutions