人工智能賦能個性化學習：E-learning推薦系統研究熱點與展望

E-Learning領域的推薦系統在滿足學習者個性化學習需求方面發揮著重要作用。近年來，國際上圍繞E-Learning推薦系統開展的研究迅速增多。採用文獻計量分析方法對該領域的研究進行系統分析，有助於為E-Learning推薦系統的高水平研究和高質量應用提供鏡鑒。綜括而言，當前國際E-Learning領域的推薦系統研究熱點及其演變趨勢集中體現在6個方面：一是融合多種技術優勢的混合推薦日益受到重視且逐漸成為主流。二是伴隨技術支持下群體學習的多元發展，個性化推薦由關注個體推薦逐步轉向關注群體推薦。三是隨著大規模開放在線課程的流行，個性化推薦逐步突破小規模而面向大規模學習者群體，重視通過對海量學習資源和過程數據的搜集和挖掘而提供個性化推薦。四是從心理學層面關注學習者情緒變化，並據此構建上下文推薦系統，通過優化調整推薦內容不斷促進學習者高效完成學習任務。五是在推薦功能上更加強調學習模型構建，重視提升學習者的深層次認知能力和促進有效學習。六是在先進技術的支持上，個性化推薦系統強調引入深度學習技術，不斷優化其表徵能力、融合效率和推薦效果。
Recommender systems in the field of E-learning are essential to meeting learners’ personalized learning needs. In recent years, research on E-learning recommender systems has grown rapidly worldwide. To systematically analyze relevant research in this field with the method of bibliometric analysis helps provide a reference for the high-level research and high-quality application concerning recommender systems in E-learning. The analysis results show the research hotspots and their evolution tendencies in this field from six aspects. First, the hybrid recommender system that takes advantage of multiple recommendation techniques has received increasing attention and has developed into the mainstream technique. Second, with the diversified development of group learning supported by innovative technologies, the focus of personalized recommendation has shifted from individual recommendation to group recommendation. Third, with the popularity of massive open online courses, there is a trend toward personalized recommendation on a large scale through collecting and mining large volume of data related to learning processes and materials. Fourth, attention has been paid to the emotional changes of learners from a psychological perspective, based on which context-aware recommender systems can be constructed to constantly promote learners to learn efficiently through adjustment and optimization of the recommended learning content. Fifth, increasing emphasis has been placed on learning model construction, with a focus on improving higher-order cognitive skills and promoting effective learning. Furthermore, supported by advanced deep learning technologies, the representation ability, information fusion efficiency, and recommendation effectiveness of personalized recommender systems can be continuously improved. Copyright © 2022 四川廣播電視大學.