Massive open online courses (MOOCs) are a technical trend in the field of education. As the number of available MOOCs continues to grow dramatically, the difficulty for learners to find courses that satisfy their personalized learning goals has also increased. Unstructured texts, such as course descriptions and course skills, contain rich course information and are useful for MOOC platforms in constructing personalized services. This paper proposes a novel search ranking method for MOOCs that integrates unstructured course information. We propose a latent Dirichlet allocation-based model to cluster courses into groups based on course descriptions. Courses in the same cluster are considered to share similar educational contents. We then propose the CourseRank algorithm based on the information of course skills to recommend and rank courses when students search for or click on a specific course. Our experiments on the dataset from Coursera indicate that our method is able to cluster courses effectively and produce satisfactory ranking results for courses in MOOC platforms.

中文翻译：

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非结构化课程信息的MOOC搜索排名新方法

大规模的在线公开课程（MOOC）是教育领域的技术趋势。随着可用MOOC数量的急剧增长，学习者寻找满足其个性化学习目标的课程的难度也增加了。非结构化的文本，例如课程说明和课程技能，包含丰富的课程信息，对于MOOC平台构建个性化服务很有用。本文提出了一种新颖的MOOC搜索排名方法，该方法集成了非结构化课程信息。我们提出了一种潜在的基于Dirichlet分配的模型，可以根据课程描述将课程分为几类。同一集群中的课程被认为共享相似的教育内容。然后，我们根据课程技能的信息提出CourseRank算法，以在学生搜索或单击特定课程时对课程进行推荐和排名。我们对Coursera数据集的实验表明，我们的方法能够有效地对课程进行聚类，并为MOOC平台中的课程产生令人满意的排名结果。