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大规模在线课程研究

MOOC简介

近年来，大规模在线开放课程(Massive Open Online Course, MOOC)的发展与普及成为了教育全球化和信息化的重要进展。 MOOC是一种全新的网络教学模式，首先，学生可以自由地选择自己感兴趣的课程，不论学校和专业、不限年龄和性别、不分国家和地区；其次，学生可以选择在自己空闲的时间进行学习，而不是像传统课堂上由老师在统一的时间授课；最后，学生在学习过程中不用支付任何学费，这为贫困地区的学生提供了优质的自学资源。

相比于传统的远程教育，MOOCs不仅提供了视频、课件、习题等学校资源，而且老师和助教可以通过论坛、作业反馈等与学生进行交互。此外，由于MOOC的课程人数比一般实体课堂的人数多得多，学生和同伴之间的督促、交流、合作也会更多，使得学生之间的思维碰撞更密集，进而互相学习共同进步。自2012年“MOOC的启航之年”，Coursera、edX、Udacity等国外MOOC平台的建立掀起了新型网络教学的浪潮，并在世界范围内引起了广泛关注。国内的MOOC发展也紧跟时代潮流，华文慕课、清华学堂在线、网易爱课程、中国大学MOOC等在近几年纷纷涌现。

然而，MOOC本身也存在着很大的缺陷，例如：学生在学习过程中缺乏教师的交互和引导、学生提交的作业很多无法逐个批改、学生相互之间无法实时交流等。由于MOOC平台记录了学生在线学习的记录日志，使得研究者们能够通过学生行为分析，了解学生的学习方式和学习目标，进而设计智能化的学习平台，为学生提供个性化的帮助。    More Information >>

国家自然科学基金“大规模在线课程中用户流失问题的研究”结题材料

项目负责人：张铭教授

科研论文（2019年1月20日检索的SCI/EI索引以及他引情况）

1. Ming Zhang; Jile Zhu; Zhuo Wang; Yunfan Chen,Providing personalized learning guidance in MOOCs by multi-source data analysis，World Wide Web 22(3): 1189-1219 (2019),EI：20181905177638
2. 张铭,计算机教育的科学研究和展望,计算机教育, 2017.12.10, (12)：5~10,
3. 张铭; 尹伊淳; 唐建,基于深度学习的网络表示研究进展,中国人工智能学会通讯, 2016.03.31, 6(3)：1~6,
4. 张铭,立足北大,放眼未来——“数据结构与算法”MOOC课程教学实践与思考工业和信息化教育, 2014.9.15,(09)：65~73, 他引：4
5. Zhang Ming; Zhang Long,Undergraduate IT education in ChinaACM Inroads, 2014.01.01, 5(3)：49~55,EI：20143800066880
6. 尹伊淳;张铭,一种基于数据重标注和富特征的神经网络机器理解模型中文信息学报, 2018.11,32(11)：125~129,
7. 王卓;张铭,基于贝叶斯知识跟踪模型的慕课学生评价,中国科技论文，2015.2.01，10(2)：30~36, 他引：2
8. 陈云帆;张铭,MOOC课程学生流失现象分析与预警,工业和信息化教育, 2014.9.15, (09)：30~36, 他引：5
9. Chenguang Wang; Yangqiu Song; Haoran Li;Zhang Ming,Unsupervised meta-path selection for text similarity measure based on heterogeneous information networks,Data Mining and Knowledge Discovery, 2018.11.1,32(6)：1735~1767,SCI入藏号: WOS:000444383000007 EI：20182905555129
10. 张昱; 陈娟; 肖胜刚; 张铭,由第48届ACM计算机科学教育大会看国内计算机教育科研,计算机教育, 2017.9.10,2017(9)：176~179,
11. Ming Zhang; Jile Zhu,A data-driven analysis of student efforts and improvements on a SPOC experiment,Proceedings of the ACM Turing 50th Celebration Conference - China, Shanghai, 2017.5.12-2017.5.14, EI: 20172603846688 他引：1
12. Ming Zhang; Jile Zhu; Yanzhen Zou; Hongfei Yan; Dan Hao; Chuxiong Liu,Educational evaluation in the PKU SPOC course SPOC Course “Data Structures and Algorithms”,2nd ACM Conference on Learning at Scale, L@S2015, Vancouver, BC, Canada, 2015.3.14-2015.3.18, EI: 20151700775625 他引：11
13. Luchen Liu; Jianhao Shen; Zhang Ming; Zichang Wang; Jian Tang ,Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction,The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, 2018.2.2-2018.2.7, 他引：1
14. Yiping Song; Rui Yan ; Yansong Feng; Yaoyuan Zhang; Dongyan Zhao; Zhang Ming,Towards a Neural Conversation Model with Diversity Net Using Determinantal Point Processes,The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 2018.2.2-2018.2.7, 他引：4
15. Yiping Song; Cheng-Te Li; Jian-Yun Nie; Zhang Ming; Dongyan Zhao; Rui Yan ,An ensemble of retrieval-based and generation-based human-computer conversation systems,IJCAI International Joint Conference on Artificial Intelligence, Stockholm, 2018.7.13-2018.7.19, EI：20184406016614 他引：4
16. Jile Zhu; Xiang Li; Zhuo Wang; Ming Zhang,An Effective Framework for Automatically Generating and Ranking Topics in MOOC Videos,Proceedings of the 10th International Conference on Educational Data Mining,Wuhan, 2017.6.25-2017.6.28 他引：1
17. Yichun Yin; Yangqiu Song; Zhang Ming,Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension,Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, 2017.9.7-2017.9.11, 他引：7
18. Yunfan Chen; Ming Zhang,MOOC student dropout: pattern and prevention,the ACM Turing 50th Celebration Conference,2017.5.12-2017.5.14, EI: 20172603846691 他引：4
19. Rui Yan; Cheng-Te Li; Xiaohua Hu; Zhang Ming,Chinese Couplet Generation with Neural Network Structures,Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin,2016.8.7-2016.8.12,EI：20170703342390 他引：8
20. Jian Tang; Jingzhou Liu; Zhang Ming; Qianzhu Mei,Visualizing Large-scale and High-dimensional Data,Proceedings of the 25th International Conference on World Wide Web, Montreal, 2016.04.11-2016.04.15, 他引：108, (网络信息领域顶会 Best paper nominee)
21. Zhuo Wang; Jile Zhu; Xiang Li; Zhiting Hu; Ming Zhang,Structured knowledge tracing models for student assessment on Coursera,3rd Annual ACM Conference on Learning at Scale, L@S 2016, Edinburgh,2016.4.25-2016.4.26, EI: 20162202443400 他引：13
22. Chenguang Wang; Yangqiu Song; Haoran Li; Zhang Ming; Jiawei Han,Text Classification with Heterogeneous Information Network Kernels,the 27th AAAI Conference on Artificial Intelligence, Washington,2016.07.14-2016.07.18,EI：20165203195760 他引：36
23. Yichun Yin ; Furu Wei; Li Dong; Kaimeng Xu; Zhang Ming; Ming Zhou,Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction,Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), New York, 2016.7.9-2016.7.15EI：20165103147093 他引：41
24. Xiang Li ; Lili Mou; Rui Yan; Zhang Ming,StalemateBreaker: A proactive Content-Introducing Approach to Automatic Human-Computer Conversation,International Joint Conference on Artificial Intelligence,New York, 2016.07.09-2016.07.15,EI： 20165103147176 他引：21
25. Chenguang Wang ; Yangqiu Song; Dan Roth; Chi Wang; Jiawei Han; Heng Ji;Ming Zhang,Constrained Information-Theoretic Tripartite Graph Clustering to Identify Semantically Similar Relations,IJCAI 2015, Buenos Aires, 2015.7.25-2015.7.31, EI：20155101693774 他引：14
26. Chenguang Wang ; Yangqiu Song; Ahmed El-Kishky; Dan Roth; Ming Zhang; Jiawei Han,Incorporating World Knowledge to Document Clustering via Heterogeneous Information Networks,SIGKDD 2015, 2015.8.10-2015.8.13,EI： 20160301811688 他引：32
27. Jian Tang ; Meng Qu; Mingzhe Wang; Ming Zhang; Jun Yan,LINE: Large-scale Information Network Embedding,WWW 2015, 2015.5.18-2015.5.22,EI：20162102409872 他引：934
28. Lijun Sun; Xin Ma; Ming Zhang; Tim Pan,Ada workshop: Study and practice on improving gender diversity in computer science industry, EI:20183405706118
29. Yao Guo; Junlin Lu; Yifeng Chen; Ming Zhang; Wenxin Li,Hybrid Small Class Teaching: Dividing and Conquering Large Computer Systems Classes,Proceedings of the ACM Turing 50th Celebration Conference - China,ACM TUR-C 2017, Shanghai, 2017.5.12-2017.5.14, EI: 20172603846692
30. He Jiang ; Yangqiu Song; Chenguang Wang; Ming Zhang; Yizhou Sun,Semi-supervised Learning over Heterogeneous Information Networks by Ensemble of Meta-graph Guided Random Walks,Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), Melbourne,2017.8.19-2017.8.25, EI：20174304308812 他引：7
31. Ziqian Zeng ; Yichun Yin; Yangqiu Song; Ming Zhang,Socialized Word Embeddings,the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17),2017.8.19-2017.8.25,EI：20174304308637 他引：3
32. Rui Yan ; Yiping Song; Cheng-Te Li; Ming Zhang; Xiaohui Hu,Opportunities or Risks to Reduce Labor in Crowdsourcing Translation? Characterizing Cost versus Quality via a PageRank-HITS Hybrid Model,24th International Joint Conference on Artificial Intelligence, IJCAI 2015, 2015.7.25-2015.7.31, EI：20155101693728 他引：7
33. Yangqiu Song ; Chenguang Wang; Ming Zhang; Hailong Sun; Qiang Yang,Spectral Label Refinement for Noisy and MissingText Labels,2015 AAAI Conference on Artificial Intelligence, AAAI 2015,Austin,2015.1.25-2015.1.30,EI：20161102080534 他引：6

毕业博士硕士

1. 王子琪，毕业博士，搜索引擎中查询转换的关键问题研究，导师张铭教授，2010.9-2015.6
2. 王晨光，毕业博士，融合知识图谱的文本异构信息网络研究，北京大学优秀博士论文，ACM China优秀博士论文提名奖，导师张铭教授，2011.9-2016.6
3. 李想, 毕业博士, 开放领域自动人机对话系统中主动表达的研究, 导师张铭教授, 2012.9-2018.6
4. 尹伊淳, 毕业博士, 基于深度学习的属性情感研究, 导师张铭教授,2013.9-2018.6
5. 荣小松，毕业硕士，社会媒体中的争议性事件检测与分析，导师张铭教授，2012.9-2015.6
6. 盛达魁，毕业硕士，异构信息网络中的元路径生成算法及应用，导师张铭教授，2013.9-2016.6
7. 王卓, 毕业硕士, 基于知识点的慕课学生评估算法及应用, 导师张铭教授, 2014.9-2017.6
8. 朱纪乐, 毕业硕士, 基于知识点的慕课课程 内容分析算法及应用, 导师张铭教授, 2015.9-2018.6

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经典论文

• Engagement Pattern: 研究学生的学习行为，如流失、提问、提交作业、学习成绩等

  • Ramesh A, Goldwasser D, Huang B, et al. Learning latent engagement patterns of students in online courses[C]//Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. AAAI Press, 2014: 1272-1278.
  • Anderson A, Huttenlocher D, Kleinberg J, et al. Engaging with massive online courses[C]//Proceedings of the 23rd international conference on World wide web. ACM, 2014: 687-698.
  • He J, Bailey J, Rubinstein B I P, et al. Identifying At-Risk Students in Massive Open Online Courses[C]//AAAI. 2015: 1749-1755.
  • Lakkaraju H, Aguiar E, Shan C, et al. A machine learning framework to identify students at risk of adverse academic outcomes[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015: 1909-1918.
  • Coleman C A, Seaton D T, Chuang I. Probabilistic use cases: Discovering behavioral patterns for predicting certification[C]//Proceedings of the Second (2015) ACM Conference on Learning@ Scale. ACM, 2015: 141-148.

• MOOC Forums: 研究学生学习过程中的困惑，如：分析论坛帖子等

  • Chaturvedi S, Goldwasser D, Daumé III H. Predicting Instructor's Intervention in MOOC forums[C]//ACL (1). 2014: 1501-1511.
  • Ramesh A, Kumar S H, Foulds J R, et al. Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums[C]//ACL. 2015: 74-83.
  • Yang D, Wen M, Howley I, et al. Exploring the effect of confusion in discussion forums of massive open online courses[C]//Proceedings of the Second (2015) ACM Conference on Learning@ Scale. ACM, 2015: 121-130.

• Skill Acquisition：研究学生学习过程中知识掌握情况

  • González-Brenes J P. Modeling Skill Acquisition Over Time with Sequence and Topic Modeling[C]//Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, pp. 296–305, 2015
  • Piech C, Bassen J, Huang J, et al. Deep knowledge tracing[C]//Advances in Neural Information Processing Systems. 2015: 505-513.
  • Khajah M, Lindsey R V, Mozer M C. How deep is knowledge tracing?[C]//In 9th International Conference on Educational Data Mining 2016: 94-101. Best Paper.

• Peer Grading: 研究自动打分、提供自动学习辅助等

  • Mi, F., & Yeung, D. Y. (2015, January). Probabilistic Graphical Models for Boosting Cardinal and Ordinal Peer Grading in MOOCs. In AAAI (pp. 454-460).
  • Shnayder, V., Frongillo, R., Parkes, D. C. (2016). Measuring performance of peer prediction mechanisms using replicator dynamics. IJCAI-16.
  • Raman, K., & Joachims, T. (2014, August). Methods for ordinal peer grading. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1037-1046). ACM.
  • SPiech, C., Huang, J., Chen, Z., Do, C., Ng, A., & Koller, D. (2013, July). Tuned Models of Peer Assessment in MOOCs. In Educational Data Mining 2013.
  • Geigle, C., Zhai, C., & Ferguson, D. C. (2016, April). An Exploration of Automated Grading of Complex Assignments. In Proceedings of the Third (2016) ACM Conference on Learning@ Scale (pp. 351-360). ACM.
  • Waters, A. E., Tinapple, D., & Baraniuk, R. G. (2015, March). BayesRank: A Bayesian approach to ranked peer grading. In Proceedings of the Second (2015) ACM Conference on Learning@ Scale (pp. 177-183). ACM.
  • Lu, Y., Warren, J., Jermaine, C., Chaudhuri, S., & Rixner, S. (2015, May). Grading the graders: Motivating peer graders in a MOOC. In Proceedings of the 24th International Conference on World Wide Web (pp. 680-690). ACM.

• Study Guidance: 提供自动学习辅助，如自动产生Hint等

  • Labarthe, H., Bouchet, F., Bachelet, R., & Yacef, K. (2016, June). Does a Peer Recommender Foster Students' Engagement in MOOCs?. In 9th International Conference on Educational Data Mining (pp. 418-423).
  • Dai, Y., Asano, Y., & Yoshikawa, M. (2016, June). Course Content Analysis: An Initiative Step toward Learning Object Recommendation Systems for MOOC Learners. In 9th International Conference on Educational Data Mining (pp. 347-352).
  • Sridhara, S., Hou, B., Lu, J., & DeNero, J. (2016, April). Fuzz testing projects in massive courses. In Proceedings of the Third (2016) ACM Conference on Learning@ Scale (pp. 361-367). ACM.
  • Piech, C., Sahami, M., Huang, J., & Guibas, L. (2015, March). Autonomously generating hints by inferring problem solving policies. In Proceedings of the Second (2015) ACM Conference on Learning@ Scale (pp. 195-204). ACM.
  • Kulkarni, C. E., Bernstein, M. S., & Klemmer, S. R. (2015, March). PeerStudio: rapid peer feedback emphasizes revision and improves performance. In Proceedings of the Second (2015) ACM Conference on Learning@ Scale (pp. 75-84). ACM.
  • Basu, S., Wu, A., Hou, B., & DeNero, J. (2015, March). Problems before solutions: Automated problem clarification at scale. In Proceedings of the Second (2015) ACM Conference on Learning@ Scale (pp. 205-213). ACM.