Difference between revisions of "Computational Thinking"

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The CT constructs below were adapted from [[Computational Practices Adapted to Archival Science]] work at the University of Maryland iSchool and are aimed at delineating CT processes needed to understand the Link Data Research Group's work on project 1 above.
 
The CT constructs below were adapted from [[Computational Practices Adapted to Archival Science]] work at the University of Maryland iSchool and are aimed at delineating CT processes needed to understand the Link Data Research Group's work on project 1 above.
  
We have chosen to globalize our examination of linked data use for organizing resource collections using the following theme:<br>
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We have chosen to globalize our examination of linked data use for organizing resource collections using the following theme:
 
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<blockquote><i>Investigation semantic indexing methods over RDF graphs</i></blockquote>
<blockquote>Investigation semantic indexing methods over RDF graphs</blockquote>
 
 
 
 
Also, with:
 
Also, with:
 
+
<blockquote><i>Data-driven semantic indexing</i></blockquote>
<blockquote>Data-driven semantic indexing</blockquote>
 
 
 
  
 
in both c
 
in both c

Revision as of 19:28, 15 January 2021

We choose to deploy constructs from Computational Thinking (CT) in the context of linked data projects specifically with a mind toward teaching students how to think about the scaling problems when attempting to maximally organize sets of resources. We have two active projects in the Linked Data Research Group, one of which is well developed and will serve as our primary example to students and the second project is at a more nascent stage. They are:

  1. Organizing access to multimedia (still images and video clips) that document game action in college football games using data sources internal to a football game (e.g., statistical play-by-play datasets) and those data sources that are external (e.g., transcripts of television broadcasts).
  2. Organizing access to textual materials of the HathiTrust page scans by examining back-of-book indexes and ToCs as access mechanisms for internal book content. Also capturing features of interest to descriptive bibliographers.

The CT constructs below were adapted from Computational Practices Adapted to Archival Science work at the University of Maryland iSchool and are aimed at delineating CT processes needed to understand the Link Data Research Group's work on project 1 above.

We have chosen to globalize our examination of linked data use for organizing resource collections using the following theme:

Investigation semantic indexing methods over RDF graphs

Also, with:

Data-driven semantic indexing

in both c