[Objective] This paper proposes a personalized model based on learning situation, which recommends schemes for learners and addresses the information overload issues.[Methods] First, we constructed a PAD-CF collaborative filtering algorithm based on three factors related to learning situation. Then, we introduced the knowledge map and degrees centrality of knowledge points to retrieve the recommended points.[Results] Compared to Pearson-CF, Edurank, and CF-SPM, the proposed model improved the F value by 6.24%, 2.68%, and 1.98%, respectively. The growth rates were 3.87%, 2.39%, and 1.43%.[Limitations] We need to add more complicated learning factors to improve the accuracy of predicted knowledge points.[Conclusions] The proposed model is highly practical for real world cases.
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