The digital transformation of education is greatly accelerating in various computer-supported applications. As a particularly prominent application of the human-machine interactive system, intelligent learning systems aim to capture users' current intentions and provide recommendations through real-time feedback. However, we have a limited understanding on what is the reality of user feedback for computer-supported adaptive quizzing. This present study proposes formalization for user feedback including engagement and interactions to understand and improve intelligent adaptive quizzing. This work starts by taking individual system's action transition patterns as a temporally evolving action graph and derives patterns in terms of informing future user engagement. Furthermore, considering the student knowledge preference for a certain moment is closely related to personal experience of past learning, a novel framework EICQ (Engagement and Interaction-based Computer-Supported Adaptive Quizzing) is proposed for learning and adapting to students' current preference based on engagement trends and interaction by collecting feedback data in real-time during a learning session. This established framework captures question characters, student dynamic interactions and engagement trends, which yield a better learning experience with adaptive practice question (PQ) recommendations. Finally, much more endeavors have been made to integrate the proposed EICQ into XXXU intelligent learning systems that are co-developed with the YYY Company.