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Computer literacy: History, Technology & Science
"Computer literacy" is defined as the knowledge and ability to use computers and related technology efficiently, with skill levels ranging from elementary use to computer programming and advanced problem solving. Computer literacy can also refer to the comfort level someone has with using computer programs and applications. Another valuable component is…
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Computer literacy topic overview
The analysis highlights History, Technology and Science as prominent areas in the source structure around Computer literacy.
Related topics
48
Source areas
4
Connected nodes
52
Extracted relationships
73
Concept neighborhoods
21
Bridge connections
52
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
History · 41 topics
Background · 3 topics
Overview · 2 topics
Worldwide computer literacy rates · 2 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Computer literacy connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Computer literacy shows recurring relationship patterns in the source. For example, Computer literacy → April, AT, Catherine, Computerized Manufacturing Automation, Computers, Developing Computer Literacy, Education, Educators, Employment, February, Haigh, Higher Education, Human Behavior, Implications, JSTOR, Lifespan, March, National Technical Information Service, OTA CIT-235, PDF Another extracted example is Computer literacy → AFIPS, American Federation, Andrew Molnar, April, Arthur Luehrmann, At, BASIC, Computing Activities, Dartmouth College, Higher Education, In, Information Processing Societies, Journal, Kemeny, Kurtz, Luehrmann, National Science Foundation, Office, Shortly, Should. Use these groups to spot repeated connection types before inspecting the individual relationships.
Computer literacy
Top relations
related to Further reading· 30
Computer literacy → April, AT, Catherine, Computerized Manufacturing Automation, Computers, Developing Computer Literacy, Education, Educators, Employment, February, Haigh, Higher Education, Human Behavior, Implications, JSTOR, Lifespan, March, National Technical Information Service, OTA CIT-235, PDF
related to history· 22
Computer literacy → AFIPS, American Federation, Andrew Molnar, April, Arthur Luehrmann, At, BASIC, Computing Activities, Dartmouth College, Higher Education, In, Information Processing Societies, Journal, Kemeny, Kurtz, Luehrmann, National Science Foundation, Office, Shortly, Should
related to United Kingdom· 11
Computer literacy → BBC Micro, Bedroom, Britain, Europe, In, PCs, Project, The BBC Computer Literacy, The ZX Spectrum, This, United Kingdom
related to background· 2
Computer literacy → Comparatively, Computer
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 73 structured relationships around Computer literacy. Examples in this analysis include Per Scholas attempt to reduce the divide by offering free → instance of → Non-profit organizations and Computer literacy → related to background → Computer. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Per Scholas attempt to reduce the divide by offering free
instance of
Non-profit organizations
0.80
text
low-cost computers to children
instance of
Non-profit organizations
0.80
text
their families in underserved communities in South Bronx
instance of
Non-profit organizations
0.80
text
New York
instance of
Non-profit organizations
0.80
text
Miami
instance of
Non-profit organizations
0.80
text
FL
instance of
Non-profit organizations
0.80
text
and in Columbus
instance of
Non-profit organizations
0.80
text
OH
instance of
Non-profit organizations
0.80
text
Computer literacy
related to background
Computer
0.60
section
Computer literacy
related to background
Comparatively
0.60
section
Computer literacy
related to Further reading
Poynton
0.60
section
Computer literacy
related to Further reading
Timothy
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Computer literacy bring nearby vocabulary together. In this analysis, examples include Literacy, Programming and Use. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Computer literacy
Literacy
Programming
Use
Skills
Information
Basic
Science
Computers
Education
Kingdom
Young
Ability
computer literacy
Literacy
Programming
Use
Skills
Basic
Information
Science
Computers
Technology
Using
Education
Kingdom
computer programming
Literacy
National
Science
Skill
Programming
Use
Introduced
Skills
Young
Information
Basic
Computers
digital literacy
Skills
Use
Basic
Information
Programming
Technology
Using
Kingdom
Computers
United
Education
Rates
bbc computer literacy project
Literacy
Programming
Use
Skills
Basic
Information
Science
Computers
Technology
Using
Education
Kingdom
bbc computer literacy project 2012
Literacy
Programming
Use
Skills
Basic
Information
Science
Computers
Technology
Using
Education
Kingdom
computer science
Literacy
Teaching
Young
Programming
Use
Skills
Information
Basic
Science
Computers
Education
Technology
community computer center
Literacy
Programming
Use
Skills
Information
Basic
Science
Computers
Education
Young
Ability
Also
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Computer literacy, one of the stronger structural bridges in this analysis connects Computer literacy with History. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Computer literacy
Nodes53
Edges52
Triples73
Avg. degree1.96
Density0.037736
Components1
Source & methodology
TTTA analyzes the structure around Computer literacy to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Technology & Science, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
