The use of artificial intelligence technology in training and education has been increasing over the past few years, with a focus on enhancing the personalized learning experience of students. In order to meet this goal, there is a need for innovative solutions that can adapt to the changing needs of the education sector. One such solution is the Experience API (xAPI), which is a technology standard for tracking and storing learning data.

OPM USALearning designed a project to use artificial intelligence in conjunction with significant amounts of xAPI data tracked to a learning record store (LRS). The goal of this research and development project is to explore the potential of artificial intelligence in improving learning outcomes through the use of xAPI data. The study aims to evaluate the feasibility of using machine learning algorithms to analyze xAPI data and provide personalized learning experiences for students.

This paper reports on the xAPI data collected from different learning environments and how the xAPI is analyzed to identify patterns and trends in student behavior. This information is used to predict learning outcomes of a student, identify at-risk students in need of intervention, and recommend learning content to learners. The study also explores the impact of the modified learning experiences on student engagement, motivation, and overall learning outcomes.

Finally, this paper presents the results of the analysis of a large xAPI dataset of approximately 200 Million data points (xAPI statements). The paper will describe the accuracy of the predictions, at-risk student identification, and describes the validity of recommended content. These features are critical in improving learning outcomes with the use of artificial intelligence in an xAPI-enabled environment.

John DeCore

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A Machine Learning Approach for Identifying At-Risk Students in Learning Record Stores: A Case Study Using USALearning Experience API (xAPI)