A Learning Analytics Framework Based on Human-Centered Artificial Intelligence for Identifying the Optimal Learning Strategy to Intervene in Learning Behavior

Big data in education promotes access to the analysis of learning behavior, yielding many valuable analysis results. However, with obscure and insufficient guidelines commonly followed when applying the analysis results, it is difficult to translate information knowledge into actionable strategies for educational practices. This study aimed to solve this problem by utilizing the learning analytics (LA) framework. We proposed a learning analytics framework based on human-centered Artificial Intelligence (AI) and emphasized its analysis result application step, highlighting the function of this step to transform the analysis results into the most suitable application strategy. To this end, we first integrated evidence-driven education for precise AI analytics and application, which is one of the core ideas of human-centered AI (HAI), into the framework design for its analysis result application step. In addition, a cognitive load test was included in the design. Second, to verify the effectiveness of the proposed framework and application strategy, two independent experiments were carried out, while machine learning and statistical data analysis tools were used to analyze the emerging data. Finally, the results of the first experiment revealed a learning strategy that best matched the analysis results through the application step in the framework. Further, we conclude that students who applied the learning strategy achieved better learning results in the second experiment. Specifically, the second experimental results also show that there was no burden on cognitive load for the students who applied the learning strategy, in comparison with those who did not.