About this Research Topic
The current Research Topic comprises the application of ML to identify patterns and establish relationships to predict educational outcomes through data analysis. ML methods are usually classified into three categories, Supervised Learning, Unsupervised Learning, and Reinforcement Learning. ML enables machines to learn to make decisions from data with minimal or no human input and automatically govern the learning process. Researchers often employ ML methodologies to effectively interpret massively complex datasets. The application of ML in educational research is expected to provide unprecedented opportunities to enhance students’ learning outcomes, reduce intervention costs, and positively improve teaching quality. This topic involves applications of new approaches for discovering knowledge to support the decision-making process in an educational setting. It is related to developing innovative methods for analyzing data that come from an educational background and using those methods to better understand to stakeholders.
In this Research Topic, we will mainly focus on how ML can facilitate educational research in systematic reviews and primary and secondary data analysis utilizing models such as Random Forest, Bayesian Additive Regression Trees, Support Vector Machine, Neural Networks or Deep Learning, etc.
We want to invite you to submit Original research articles, Review or Mini-Review articles, and Perspective articles, related to themes that include (but are not limited to):
• Developing and integrating machine learning approaches for causal inference
• Data-Driven AI-based computational models for analyzing assessment data
• Educational data mining
• Educational evaluation with machine learning techniques
• Applications of machine learning algorithms
• Predicting students' progression with advanced statistical models
• Integrative approach in data analysis
• Modeling educational data
• Systematic review on machine learning applications
• Artificial intelligence for predicting educational outcomes
• Perspectives on the possibilities and challenges of applying ml in educational studies
• Reinforcement learning for education
Keywords: Computational Psychometrics, Applications of Machine Learning Algorithms, Ensemble Learning, Artificial Intelligence, Big Data, Systematic Review on Machine Learning Applications
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.