MOOCC ube: A Large-scale Data Repository for NLP Applications in MOOC s (2020)

Abstract: The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applications, e.g., course concept extraction, prerequisite relation discovery, etc. However, the publicly available datasets of MOOC are limited in size with few types of data, which hinders advanced models and novel attempts in related topics. Therefore, we present MOOCCube, a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. Moreover, we conduct a prerequisite discovery task as an example application to show the potential of MOOCCube in facilitating relevant research. The data repository is now available at http://moocdata.cn/data/MOOCCube .

## introduction massive open online courses (moocs) boom swiftly in recent years and have provided convenient education for over 100 million users worldwide @xcite . as a multi-media, large-scale online interactive system, mooc is an excellent platform for advanced application research @xcite . since mooc is committed to helping students learn implicit knowledge concepts from diverse courses, many efforts from nlp and ai raise topics to build novel applications for assistance. from extracting course concepts and their prerequisite relations @xcite @xcite to analyzing student behaviors @xcite , mooc-related topics, tasks, and methods snowball in recent years. despite the plentiful research interests, the resource from real moocs is still impoverished. most of the publicly available datasets are designed for a specific task or method, e.g., @xcite build a mooc enrollment dataset for course recommendation and @xcite is only for course concept expansion, which merely contains a subset of mooc elements. consequently, they are not feasible enough to support ideas that demand more types of information. moreover, these datasets only contain a small size of specific entities or relation instances, e.g., prerequisite relation of tutorialbank @xcite only has 794 cases, making it insufficient for advanced models (such as graph neural networks). therefore, we present mooccube, a data repository that integrates courses, concepts, student behaviors, relationships, and external resources. compared with existing education-related datasets, mooccube maintains the following advantages: as shown in figure 1 , a data cell of mooc-cube is in terms of concepts, courses, and students, which represents a learning fact, i.e., a student s learns concept k in course c. through different queries, mooccube can provide various combinations of these data cells to support existing research. in this paper, we first introduce the data collection process and then give an insight into the characteristics of mooccube by analyzing its statistics in different aspects. we also conduct a typical nlp application task on mooccube and discuss the future directions on the usage of our datasets. our contribution is in two folds: a) an investigation of nlp and ai application research in online education, especially in moocs; b) a large-scale data repository of moocs, which organizes data in three dimensions: student behaviors, courses, and knowledge concepts. ## data analysis in this section, we analyze various aspects of mooccube to provide a deeper understanding of the dataset. comparison with similar datasets. ## application such a wealth of data enables mooccube to support multiple tasks such as course recommendation @xcite , concept mining @xcite , etc. in this section, we conduct an important and typical task, prerequisite relation discovery as an example application of mooccube by utilizing different types of data from it. as introduced in section 2.3, prerequisite relation indicates "what should a student learn at first". since existing efforts have attempted to discover such relationships among concepts from different types of information, we reproduce the following methods on mooccube and present some basic new models. dataset course video concept prerequisite taxonomy student enrollment video watching external resource kddcup2015 39 ----112,448 200,904 1,319,032 -hmr 1,302 ----82,535 458,454 --prl 20 1,356 573 3,504 ----corpus tutorialbank --200 794 200 ---corpus, paper lecturebank 60 208 921 1,221 ---corpus, paper, blog mooccube 706 38,181 106,056 17,686 3,152 199,199 682,753 4,874,298 corpus, paper course, video, concept, student are the sum of respective entities. prerequisite is the number of relation instances, taxonomy is the number of finest taxonomy categories, and enrollment and video watching are the records of behavior. the precision of this model (prereq-s improves the precision to 0.651). we argue that the diverse information provided by mooccube helps to discover such relationships. meanwhile, two simple dnn models perform competitive results in this task, which indicates that the existing methods are indeed limited by the amount of data (most advanced models cannot be trained on small datasets). ## related work in this section, we introduce the research of nlp in education, especially in moocs, as well as several publicly available related datasets. existing research in moocs uses courses and students as the main resource, which can be divided into two categories according to the research object: one focuses on the content of the courses, such as the course concept extraction @xcite , prerequisite relation discovery @xcite , and course concept expansion @xcite ; the other focuses on the learning behavior of students, such as the prediction of dropouts @xcite , course recommendations @xcite , etc. due to the different tasks, researchers have to repeat the work to build their datasets, which arouses the original motivation of mooccube. in addition, some researchers also try to obtain education information from other resources, e.g., acl anthology @xcite , tutorialbank @xcite , and lecturebank @xcite . they collected concepts and relationships from papers and lectures and also built diverse datasets. though they are also limited in data scale, these beneficial attempts guide the construction of mooccube. ## conclusion and future work we present mooccube, a multi-dimensional data repository containing courses, concepts, and student activities from real mooc websites. obtaining large-scale data in all dimensions, mooccube can support new models and diverse nlp applications in moocs. we also conduct prerequisite relation extraction as an example application, and experimental results show the potential of such a repository. promising future directions include: 1) utilize more types of data from mooccube to facilitate existing topics; 2) employ advanced models in existing tasks; 3) more innovative nlp application tasks in online education domain.