MOOCCubeX is maintained by the the Knowledge Engineering Group of Tsinghua Univerisity, and supported by XuetangX, one of the largest MOOC websites in China. This repository consists of 4,216 courses, 230,263 videos, 358,265 exercises, 637,572 fine-grained concepts and over 296 million raw behavioral data of 3,330,294 students, for supporting the research topics on adaptive learning in MOOCs.
We summarize the contributions of MOOCCubeX as follows:
- High Coverage: MOOCCubeX obtains diverse MOOC resources and external educational resources, as well as the data records of the students' learning, exercising and discussion.
- Large-scale: Compared with other open-access educational data repository, the scale of MOOCCubeX is larger, thereby supporting the exploration of deep models with high data requirements.
- Concept-centric: Heterogeneous data is organized using fine-grained concepts, which makes resources more relevant and easier to represent, find and model.
- The prerequisite relations of mathematics, psychology and Computer science are refined !!
- Our paper is submitted to CIKM2021 resource track !!
- The MOOCCubeX Dataset Builder toolkit is updated !!
- Our paper is accepted by CIKM2021 resource track !!
The framework of MOOCCubeX is shown as bellow.
The data of MOOCCubeX are organized with a large fine-grained concept graph. The resources of MOOCCubeX are listed in the tables.
The course resources (more details are introduced in course.md).
| Course Resource Type | Description | Download | Size |
|---|---|---|---|
| Course Info | Course video and exercise organization. | entities/course.json | 43M |
| Video | Video name and captions. | entities/video.json | 580M |
| Exercise | A group of problems of the course | relations/exercise-problem.txt | 129M |
| Problem | Practice problems of a group of exercise. | entities/problem.json | 1.2G |
| School | School information. | entities/school.json | 613K |
| Teacher | Teacher information. | entities/teacher.json | 8.7M |
| Field/Discipline | The fields a course belongs to. Annotated by human. | relations/course-field.json | 62K |
The student behavioral data (more details are introduced in user.md).
| Student Behavior Type | Description | Download | Size |
|---|---|---|---|
| Student Profile | User id, school, course register order, etc. | entities/user.json | 770M |
| Video Watching | The speed and jumps of time of users watching video. | relations/user-video.json | 3.0G |
| Exercising | Users doing exercise problems. | relations/user-problem.json | 21G |
| Comment | Users' comment on a video or an exercise. | entities/comment.json | 2.1G |
| Reply | Users' replies on comments of other users. | entities/reply.json | 50M |
| Xiaomu | User interaction with Xiaomu (the QA bot of XuetangX). | relations/user-xiaomu.json | 9.7M |
The fine-grained concepts and their links with other types of MOOC resources, as well as the external resources are introduced in concept.md.
| Concept and Links | Description | Download | Size |
|---|---|---|---|
| Concept | Concepts extracted from video captions. | entities/concept.json | 156M |
| Concept-prerequisite | Prediction and human annotation of prerequisites of Psychology, Math and Computer Science. | prerequisites/psy.json prerequisites/math.json prerequisites/cs.json | 87M 59M 133M |
| Concept-course | Linked concepts of a course. | relations/concept-course.txt | 19M |
| Concept-video | Linked concepts of a video. | relations/concept-video.txt | 39M |
| Concept-problem | Linked concepts of a problem. | relations/concept-problem.txt | 1.3M |
| Concept-comment | Linked concepts of a comment. | relations/concept-comment.txt | 1.2M |
| Concept-others | Linked concepts of other resource. | relations/concept-other.txt | 19M |
We provide 2 toolkits for convenient usage. They are built for the refinement or DIY employment of our data.
-
MOOCCubeX Dataset Builder
Name Description Usage Example download_dataset.sh Download the full dataset. ./scripts/download_dataset.shcount.sh Count the number of courses/videos/... ./scripts/count.shuser_freq_histgram.py Plot usage frequency of videos/problems/... (Figure 4 in paper) python3 scripts/user_freq_histgram.pyconcept_course.py The script used to generate relations/concept-course.txt.python scripts/concept_course.pyconcept_finder.sh Find ccids related to the given concept../scripts/concept_finder.sh K_晶体三极管组态放大器_电子科学与技术course_info_finder.sh Find course information whose name contains the given string ./scripts/course_info_finder.sh 数据结构video_viewed_by_user_and_course.sh Get all resource_ids of videos viewed by a given user of a given course./scripts/video_viewed_by_user_and_course.sh U_94015 C_1824928problems_by_user.sh Get all problems attempted to solve by a given user. ./scripts/problems_by_user.sh U_10000835concepts_of_video.sh Get all concepts of given video ./scripts/concepts_of_video.sh V_479945who_replied.sh Get all other users who replied a given user's comment ./scripts/who_replied.sh U_10006544
Some tools above have dependency on jq or Python packages like matplotlib and tqdm.
-
MOOCube Concept Helper
- Concept Extraction Pipeline: https://github.com/yujifan0326/Concept-Acquisition-Pipeline
- Prerequisite Discovery: https://github.com/luogan1234/prerequisite-prediction-co-training
MOOCCubeX has some statistical features of its concepts and behavirol data.
- MOOCCubeX contains more fine-grained concept compared with the previous version MOOCCube.
- The video watching behaviors are long-tail, while the exercising is normal distribution.
@inproceedings{yu2021mooccubex,
title={{MOOCCubeX}: A Large Knowledge-centered Repository for Adaptive Learning in {MOOCs}},
author={Yu, Jifan and Wang, Yuquan and Zhong, Qingyang and Luo, Gan and Mao, Yiming and Sun, Kai and Feng, Wenzheng and Xu, Wei and Cao, Shulin and Zeng, Kaisheng and others},
booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
pages={4643--4652},
year={2021}
}