The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Learning author-topic models from text corpora
ACM Transactions on Information Systems (TOIS)
Local space-time smoothing for version controlled documents
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Modeling the evolution of topics in source code histories
Proceedings of the 8th Working Conference on Mining Software Repositories
Question taxonomy and implications for automatic question generation
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Online writing data representation: a graph theory approach
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
The big five and visualisations of team work activity
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Assessing elementary students' science competency with text analytics
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Hi-index | 0.00 |
The use of cloud computing writing tools, such as Google Docs, by students to write collaboratively provides unprecedented data about the progress of writing. This data can be exploited to gain insights on how learners' collaborative activities, ideas and concepts are developed during the process of writing. Ultimately, it can also be used to provide support to improve the quality of the written documents and the writing skills of learners involved. In this paper, we propose three visualisation approaches and their underlying techniques for analysing writing processes used in a document written by a group of authors: (1) the revision map, which summarises the text edits made at the paragraph level, over the time of writing. (2) the topic evolution chart, which uses probabilistic topic models, especially Latent Dirichlet Allocation (LDA) and its extension, DiffLDA, to extract topics and follow their evolution during the writing process. (3) the topic-based collaboration network, which allows a deeper analysis of topics in relation to author contribution and collaboration, using our novel algorithm DiffATM in conjunction with a DiffLDA-related technique. These models are evaluated to examine whether these automatically discovered topics accurately describe the evolution of writing processes. We illustrate how these visualisations are used with real documents written by groups of graduate students.