Analytics 2.0 for Precision Education: An Integrative Theoretical Framework of the Human and Machine Symbiotic Learning

This methodological-theoretical synergy provides an integrative framework of learning analytics through the development of the human-and-machine symbiotic reinforcement learning. The framework intends to address the challenges of the current learning analytics model, including a lack of internal validity, generalizability, immediacy, transferability, and interpretability for precision education. The proposed framework consists of a master component (the brain) and its four subsuming components: social networking, the smart classroom, the intelligent agent, and the dashboard. The brain component takes in and analyzes multimodal streams of student data from the other components with the model-based reinforcement learning, which forms policies of adequate actions that maximize the long-term rewards for both the human and machine in the seamless learning environment. An example case plan in advanced statistics was demonstrated to illustrate the course description, data collected in each component, and how the components meet different features of the smart learning environment to deliver precision education. An empirical demonstration was provided using some selected mulitmodal data to inform the effectiveness of the proposed framework. The human-and-machine symbiotic reinforcement learning has theoretical and practical implications for the next-generation learning analytics models and research.