A Machine Learning Approach for Understanding the Educational Foci and Technical Solutions of AIED

Abstract

<p>This work-in-progress paper employs a machine learning method for the automated analysis of research interests in Artificial Intelligence in Education (AIED) at scale. We aim to analyze the essential techniques and critical educational problems studied by researchers in five AIED-related conferences and journals between 2010 and 2022. We trained and compared different machine learning models and feature extraction techniques to achieve the research objective. After comparing different models and hyperparameter combinations, our classifier achieves an accuracy of 0.87 and Cohen's kappa of 0.80. Based on the classification results, we identified the top 10 most frequent keywords within each category for every four year period over the past 12 years. Using the classifier, the 10,723 keywords from 2,684 articles were classified into three categories: educational foci, technical solutions, and AIED applications. We find that 'natural language processing' and 'machine learning' are the primary technical keywords in AIED research, and 'deep learning' and 'artificial intelligence' are the trending technical keywords since 2017. Meanwhile, 'massive open online courses', 'self-regulated learning', 'feedback', 'collaborative learning', and 'online learning' are the top educational foci in the field over the last 12 years. 'Intelligent tutoring systems', 'educational data mining', 'knowledge tracing', and 'learning analytics' continue to receive attention as AIED applications of sustained interest. This study helps to understand the educational foci and technical solutions of AIED research at scale and provides insights into the future of AIED research.</p>