Addressing gaps in knowledge while reading

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Addressing gaps in knowledge while reading
Authors: Christopher Jordan, Carolyn Watters [edit item]
Citation: Journal of the American Society for Information Science and Technology 60 (11): 2255-2268. 2009.
Publication type: Journal article
Peer-reviewed: Yes
Database(s):
DOI: 10.1002/asi.21168.
Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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Addressing gaps in knowledge while reading is a publication by Christopher Jordan, Carolyn Watters.


[edit] Abstract

Reading is a common everyday activity for most of us. In this article, we examine the potential for using Wikipedia to fill in the gaps in one's own knowledge that may be encountered while reading. If gaps are encountered frequently while reading, then this may detract from the reader's final understanding of the given document. Our goal is to increase access to explanatory text for readers by retrieving a single Wikipedia article that is related to a text passage that has been highlighted. This approach differs from traditional search methods where the users formulate search queries and review lists of possibly relevant results. This explicit search activity can be disruptive to reading. Our approach is to minimize the user interaction involved in finding related information by removing explicit query formulation and providing a single relevant result. To evaluate the feasibility of this approach, we first examined the effectiveness of three contextual algorithms for retrieval. To evaluate the effectiveness for readers, we then developed a functional prototype that uses the text of the abstract being read as context and retrieves a single relevant Wikipedia article in response to a passage the user has highlighted. We conducted a small user study where participants were allowed to use the prototype while reading abstracts. The results from this initial study indicate that users found the prototype easy to use and that using the prototype significantly improved their stated understanding and confidence in that understanding of the academic abstracts they read.

[edit] Research questions

"In this article, we examine the potential for using Wikipedia to fill in the gaps in one's own knowledge that may be encountered while reading. If gaps are encountered frequently while reading, then this may detract from the reader's final understanding of the given document. Our goal is to increase access to explanatory text for readers by retrieving a single Wikipedia article that is related to a text passage that has been highlighted."

Research details

Topics: Reading support [edit item]
Domains: Computer science [edit item]
Theory type: Design and action [edit item]
Wikipedia coverage: Sample data [edit item]
Theories: "Undetermined" [edit item]
Research design: Experiment [edit item]
Data source: Experiment responses, Wikipedia pages [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: Article [edit item]
Wikipedia data extraction: Dump [edit item]
Wikipedia page type: Article [edit item]
Wikipedia language: English [edit item]

[edit] Conclusion

"The prototype tool, LiteraryMark, developed for this study employs a less intrusive approach to retrieving related information. The user simply highlights text in the passage and a relevant Wikipedia article is displayed in a pop-up box. In this study, we examined six algorithms exploiting the language of the abstract, as well as links and categories of the Wikipedia articles, as the context to narrow the results to one relevant Wikipedia article. The most effective algorithm, using the terms of the abstract alone, was successful over 70% of the cases in a user study."

[edit] Comments


Further notes[edit]

Facts about "Addressing gaps in knowledge while reading"RDF feed
AbstractReading is a common everyday activity for Reading is a common everyday activity for most of us. In this article, we examine the potential for using Wikipedia to fill in the gaps in one's own knowledge that may be encountered while reading. If gaps are encountered frequently while reading, then this may detract from the reader's final understanding of the given document. Our goal is to increase access to explanatory text for readers by retrieving a single Wikipedia article that is related to a text passage that has been highlighted. This approach differs from traditional search methods where the users formulate search queries and review lists of possibly relevant results. This explicit search activity can be disruptive to reading. Our approach is to minimize the user interaction involved in finding related information by removing explicit query formulation and providing a single relevant result. To evaluate the feasibility of this approach, we first examined the effectiveness of three contextual algorithms for retrieval. To evaluate the effectiveness for readers, we then developed a functional prototype that uses the text of the abstract being read as context and retrieves a single relevant Wikipedia article in response to a passage the user has highlighted. We conducted a small user study where participants were allowed to use the prototype while reading abstracts. The results from this initial study indicate that users found the prototype easy to use and that using the prototype significantly improved their stated understanding and confidence in that understanding of the academic abstracts they read.nding of the academic abstracts they read.
Added by wikilit teamAdded on initial load +
Collected data time dimensionCross-sectional +
ConclusionThe prototype tool, LiteraryMark, developeThe prototype tool, LiteraryMark, developed for this study employs a less intrusive approach to retrieving related information. The user simply highlights text in the passage and a relevant Wikipedia article is displayed in a pop-up box. In this study, we examined six algorithms exploiting the language of the abstract, as well as links and categories of the Wikipedia articles, as the context to narrow the results to one relevant Wikipedia article. The most effective algorithm, using the terms of the abstract alone, was successful over 70% of the cases in a user study.ful over 70% of the cases in a user study.
Data sourceExperiment responses + and Wikipedia pages +
Doi10.1002/asi.21168 +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22Addressing%2Bgaps%2Bin%2Bknowledge%2Bwhile%2Breading%22 +
Has authorChristopher Jordan + and Carolyn Watters +
Has domainComputer science +
Has topicReading support +
Issue11 +
Pages2255-2268 +
Peer reviewedYes +
Publication typeJournal article +
Published inJournal of the American Society for Information Science and Technology +
Research designExperiment +
Research questionsIn this article, we examine the potential In this article, we examine the potential for using Wikipedia to fill in the gaps in one's own knowledge that may be encountered while reading. If gaps are encountered frequently while reading, then this may detract from the reader's final understanding of the given document. Our goal is to increase access to explanatory text for readers by retrieving a single Wikipedia article that is related to a text passage that has been highlighted. a text passage that has been highlighted.
Revid10,649 +
TheoriesUndetermined
Theory typeDesign and action +
TitleAddressing gaps in knowledge while reading
Unit of analysisArticle +
Urlhttp://dx.doi.org/10.1002/asi.21168 +
Volume60 +
Wikipedia coverageSample data +
Wikipedia data extractionDump +
Wikipedia languageEnglish +
Wikipedia page typeArticle +
Year2009 +