【摘要】 在线学习需求是影响在线学习者认知和学习行为的关键因素。精准分析与识别在线学习需求有助于提高学习完成度，促进在线课程的可持续发展。在在线学习需求概念界定与内涵分析的基础上，依据感性工学理论与方法，以感性意象词汇与在线课程资源要素的映射关系为核心，提出在线学习需求分析模型，使用BiLSTM深度神经网络方法，检验在线学习需求分析模型的有效性与泛化能力。结果表明，该模型能够有效提取在线学习者的学习需求，并从基本信息、自学资源、活动资源、学习产出、评价资源与课程支持者六个方面，提出面向在线学习需求的在线课程优化策略。更多还原

【Abstract】 Online learning needs are the key factors affecting online learners’ cognition and behavior.The online learning needs are accurately analyzed and identified, it contributes to enhance online learners’ learning completion and accelerate the sustainable development of online courses.The concept and connotation of online learning needs were analyzed.According to the theory of Kansei Engineering, the mapping relationship between perceptual image vocabulary and online course resource elements were focused, this paper put forward the model which was used to mine learning needs, using BiLSTM deep neural network method to analyze these data, the effectiveness and generalization ability of the model was tested. The results show that the model can effectively extract the learning needs of online learners, this paper puts forward the optimized strategy of the online course from six aspects: basic information, the resources for teaching oneself, the activity resources, learning output, the resources for evaluation and course supporters.更多还原