Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00036
Qing Chen, Zhen Li, T. Pong, Huamin Qu
The practical power of data visualization is currently attracting much attention in the e-learning domain. A growing number of studies have been conducted in recent years to help instructors better analyze learner behavior and reflect on their teaching. However, current elearning dashboards and visualization systems usually require a lot of time and effort into the exploration process. Moreover, the lack of communication power of existing systems constrains users from organizing the narrative of information pieces into a compelling data story. In this paper, we have proposed a narrative visualization approach with an interactive slideshow that helps instructors and education experts explore potential learning patterns and convey data stories. This approach contains three key components: guided-tour concept, drill-down path, and dig-in exploration dimension. The use cases further demonstrate the potential of employing this visual narrative approach in the e-learning context.
{"title":"Designing Narrative Slideshows for Learning Analytics","authors":"Qing Chen, Zhen Li, T. Pong, Huamin Qu","doi":"10.1109/PacificVis.2019.00036","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00036","url":null,"abstract":"The practical power of data visualization is currently attracting much attention in the e-learning domain. A growing number of studies have been conducted in recent years to help instructors better analyze learner behavior and reflect on their teaching. However, current elearning dashboards and visualization systems usually require a lot of time and effort into the exploration process. Moreover, the lack of communication power of existing systems constrains users from organizing the narrative of information pieces into a compelling data story. In this paper, we have proposed a narrative visualization approach with an interactive slideshow that helps instructors and education experts explore potential learning patterns and convey data stories. This approach contains three key components: guided-tour concept, drill-down path, and dig-in exploration dimension. The use cases further demonstrate the potential of employing this visual narrative approach in the e-learning context.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117064903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-01DOI: 10.1109/PacificVis.2019.00025
Yuyu Yan, Y. Tao, Sichen Jin, Jin Xu, Hai Lin
Text labeling for classification is a time-consuming and unintuitive process. Given an unannotated text collection, it is difficult for users to determine what label to create and how to label the initial training set for classification. Thus, we present an interactive visual analytics system for incremental text classification based on a semi-supervised topic modeling method, modified Gibbs sampling maximum entropy discrimination latent Dirichlet allocation (Gibbs MedLDA). Given a text collection, Gibbs MedLDA generates topics as a summary of the text collection. We design a scatter plot to display documents and topics simultaneously to show the topic information, and this helps users explore the text collection structurally and find labels for creating. After labeling documents, Gibbs MedLDA is applied to the text collection with labels again, and it generates both the topic and classification information. We also provide a scatter plot with the classifier boundary and a matrix view to present weights of classifiers. Users can iteratively label documents to refine each classifier. We evaluate our system via a user study with a benchmark corpus for text classification and case studies with two unannotated text collections.
{"title":"An Interactive Visual Analytics System for Incremental Classification Based on Semi-supervised Topic Modeling","authors":"Yuyu Yan, Y. Tao, Sichen Jin, Jin Xu, Hai Lin","doi":"10.1109/PacificVis.2019.00025","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00025","url":null,"abstract":"Text labeling for classification is a time-consuming and unintuitive process. Given an unannotated text collection, it is difficult for users to determine what label to create and how to label the initial training set for classification. Thus, we present an interactive visual analytics system for incremental text classification based on a semi-supervised topic modeling method, modified Gibbs sampling maximum entropy discrimination latent Dirichlet allocation (Gibbs MedLDA). Given a text collection, Gibbs MedLDA generates topics as a summary of the text collection. We design a scatter plot to display documents and topics simultaneously to show the topic information, and this helps users explore the text collection structurally and find labels for creating. After labeling documents, Gibbs MedLDA is applied to the text collection with labels again, and it generates both the topic and classification information. We also provide a scatter plot with the classifier boundary and a matrix view to present weights of classifiers. Users can iteratively label documents to refine each classifier. We evaluate our system via a user study with a benchmark corpus for text classification and case studies with two unannotated text collections.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129222236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-06DOI: 10.1109/PacificVis.2019.00014
Alex Bigelow, M. Monroe
This paper reports on a simple visual technique that boils extracting a subgraph down to two operations—pivots and filters—that is agnostic to both the data abstraction, and its visual complexity scales independent of the size of the graph. The system's design, as well as its qualitative evaluation with users, clarifies exactly when and how the user's intent in a series of pivots is ambiguous—and, more usefully, when it is not. Reflections on our results show how, in the event of an ambiguous case, this innately practical operation could be further extended into "smart pivots" that anticipate the user's intent beyond the current step. They also reveal ways that a series of graph pivots can expose the semantics of the data from the user's perspective, and how this information could be leveraged to create adaptive data abstractions that do not rely as heavily on a system designer to create a comprehensive abstraction that anticipates all the user's tasks.
{"title":"Jacob's Ladder: The User Implications of Leveraging Graph Pivots","authors":"Alex Bigelow, M. Monroe","doi":"10.1109/PacificVis.2019.00014","DOIUrl":"https://doi.org/10.1109/PacificVis.2019.00014","url":null,"abstract":"This paper reports on a simple visual technique that boils extracting a subgraph down to two operations—pivots and filters—that is agnostic to both the data abstraction, and its visual complexity scales independent of the size of the graph. The system's design, as well as its qualitative evaluation with users, clarifies exactly when and how the user's intent in a series of pivots is ambiguous—and, more usefully, when it is not. Reflections on our results show how, in the event of an ambiguous case, this innately practical operation could be further extended into \"smart pivots\" that anticipate the user's intent beyond the current step. They also reveal ways that a series of graph pivots can expose the semantics of the data from the user's perspective, and how this information could be leveraged to create adaptive data abstractions that do not rely as heavily on a system designer to create a comprehensive abstraction that anticipates all the user's tasks.","PeriodicalId":208856,"journal":{"name":"2019 IEEE Pacific Visualization Symposium (PacificVis)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127774643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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