Utilizing AI and Big Data in Education: The Current State and Future Directions

초록

This study discusses the current state of, future directions for, and challenges of research on AI and big data in education. To accomplish this goal, first, a review of the state of research on learning analytics, large-scale data analysis, text mining based big data analysis, multimodal data analysis, and game-based learning was conducted. Based on the review researchers suggested future directions as the following: 1) The use of learning analytics in elementary and secondary schools should be promoted by developing instructional models to analyze learning analytics data and establishing an integrated system for data collection at a national level, 2) Large-scale data from various sources should be merged together into educational big data sets so that researchers can extend the potential for exploring complex and critical issues in our education system, 3) More research on sentiment analysis via text mining is needed considering its potential for providing insight into identifying students’affective states and administering effective interventions, especially for elementary and secondary school students. Despite the promising uses of AI and big data analysis for solving educational problems, the challenges we face are, first, disbelief about the contributions that AI and big data can offer to the field of education, second, the lack of government initiative for organized data collection and analysis, third ethical concerns about biases and privacy matters, and finally, the shortage of educational AI and big data experts.

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