Research on the Effectiveness of Technology-supported Personalized Learning Based on Deep Cognitive Diagnosis——A Case Study of the “Permutations and Combinations” Content in High School Mathematics

Technology enhanced learning is a core issue in educational technology research, yet its effectiveness has been questioned. Based on this, this paper adopted a quasi-experimental research method involving 607 second-year high school students from two public schools in Shanghai and used deep cognitive diagnosis to obtain the learners’ three-level cognitive starting points (representational stage, causal stage, and belief stage) aiming at the “Permutation and Combination” chapter in high school mathematics. Meanwhile, targeted learning interventions were accordingly designed and experimental teaching combined with technology were conducted, in order to examine the influence of technology-supported personalized learning based on deep cognitive diagnosis on academic performance improvement. The results showed that, compared with traditional teaching, technology-supported personalized learning based on deep cognitive diagnosis significantly improved learners’ mathematics scores, and interventions based on deeper cognitive starting points achieved even more significant learning effects. Through research, this paper was expected to provide empirical support and theoretical guidance for implementing efficient personalized learning in frontline teaching scenarios.