Machine Learning in Educational Sciences

<p>Using machine learning in educational research.- Machine learning approaches to predict non-completion in AP statistics courses.- Predicting student attrition in university courses.- Machine learning based identification strategy of circumstances in the analysis of inequality of opportunity.- Machine learning applications for early and on-going warning systems in education.- Using neural networks for analyzing large-scale international assessment data.- Utilizing natural language processing and large language models in science education.- Machine based learning in psychological assessment.- Applying topic modeling to understand assessment practices of U.S. College instructors in response to the COVID-19 pandemic.- Penalized regression in educational large-scale assessments.- Applying machine learning to augment the design and assessment of immersive learning experience.- Automatic creation of concept maps to generate ‘Learning Coefficients’ in adaptive assessments.- Camelot: A council of machine learning strategies to enhance teaching.- Research on blended learning achievement improvement based on integrated machine learning methods.- Exploring non-cognitive factors affecting students’ academic performance based on PISA data: from econometrics to machine learning.- ChatGPTing the path to K12 educational reform: Examining Generative AI in the middle east from an industry perspective.- Exploring the integration of machine learning in mathematics classrooms: A literature review and recommendations for implementation.- Identification of students at risk of low performance or failure by combining enhanced machine learning, and knowledge graph techniques.</p>