Knowledge Tracing (KT), as a pivotal technology in intelligent education systems, analyzes students' learning data to infer their knowledge acquisition and predict their future performance. Recent advancements in KT recognize the importance of memory laws on knowledge acquisition but neglect modeling the inherent structure of memory, which leads to the inconsistency between explicit student learning and implicit memory transformation. Therefore, to enhance the consistency, we propose a novel memory flow-controlled knowledge tracing with three stages (MFCKT). According to information processing theory, we deconstruct learning into: sensory registration, short-term encoding, and long-term memory retrieval stages. Specifically, to extract sensory memory, MFCKT maximizes the similarity between positive augmentation views of learning sequence representations through contrastive pre-training. Then, to transform sensory memory into short-term memory, MFCKT fuses relational and temporal properties of sensory memory through a dual-channel structure composed of attention and recurrent neural networks. Furthermore, for obtaining long-term memory, MFCKT designs a monotonic gating mechanism to compute weights of hidden memory states, and then performs read-write operations on the memory matrix. Finally, MFCKT combines long-term and short-term memory vectors to retrieve latent knowledge states for future performance prediction. Extensive experimental results on five real-world datasets verify the superiority and interpretability of MFCKT.