【摘要】 【目的】针对学习者学习过程中出现的信息过载问题,构建一个基于学习情况的个性化学习推荐模型LS-PLRM,为学习者推荐个性化学习方案。【方法】在LS-PLRM中,提出一种应用三个学习情况因子改进相似度计算的PAD-CF协同过滤算法,结合知识地图与知识点度中心性实现知识点推荐度的计算与标注,最终生成个性化学习方案。【结果】对于F值,LS-PLRM比Pearson-CF、Edurank、CF-SPM等学习推荐模型分别提高6.24%、2.68%和1.98%。对于得分提升率,LS-PLRM比上述模型分别提高3.85%、2.39%和1.41%。【局限】未考虑多种复杂的学习情况影响因素,预测知识点得分的准确性有待提高。【结论】个性化学习推荐模型LSPLRM具有较高的实践应用意义。更多还原

【Abstract】 [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.更多还原