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Record W1842025785

Online Behavior and Cognitive Development

2007· article· en· W1842025785 on OpenAlexaffabout
Genevieve Johnson, Jillianne Code, Nicholas E. Zaparyniuk

Bibliographic record

VenueEdMedia: World Conference on Educational Media and Technology · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsSimon Fraser UniversityMacEwan University
Fundersnot available
KeywordsThe InternetPsychologyCognitionWorld Wide WebComputer science
DOInot available

Abstract

fetched live from OpenAlex

Parents of 128 children in a rural elementary school provided information on home Internet access and children’s online activities. Children were individually administered four measures of cognitive development (expressive language, metacognition, visual perception, and auditory memory) and were asked to define ten Internet terms (e.g., email, chat, website). Ability to define an Internet term was assumed indicative of experience with that application. Parent response to the open-ended item what does your child do when he/she uses the Internet at home was thematically organized into four types of Internet behavior: learn, play, browse, and communicate. Children’s ability to correctly define Internet terms as well as parent reported online learning and communicating (but not playing and browsing) were associated with increased cognitive scores. Focused and goal-directed online activities (e.g., learning and communicating) are recommended for children 6 to 12 years of age. When asked about their activities the previous day, 22% of American 8 to 10 year old children indicated that they had visited websites (Roberts, Foehr, & Rideout, 2005). Approximately 20% of Canadian 9 year-olds access the Internet through their own personal computer (Media Awareness Network, 2006). Forty per cent of Australian children aged 4 to 6 years have been online for at least two years (Nielsen//NetRatings Internet and Technology Report, 2005). All trends indicate that the number of children accessing the Internet and the amount of time they spend online are steadily increasing (DeBell & Chapman, 2006; Livingston & Bober, 2005; Statistics Canada, 2004; U.S. Census Bureau, 2005). Greenfield and Yan (2006) conceptualize “the Internet as a new object of cognition, neither a concrete artifact nor a visible social partner” (p. 393). From a developmental perspective, “the Internet is a cultural tool that influences cognitive processes and an environmental stimulus that contributes to the formation of specific cognitive architecture” (Johnson, 2006, p. 565). The Internet and Cognitive Development As children develop, their cognitive processes and abilities (e.g., language, metacognition, perception, and memory) mature in response to genetic and environmental forces (Garcia, Bearer, & Lerner, 2004). Environmental forces include parents, peers, schooling, and media (Gentile & Walsh, 2002). The Internet is not like other media “in the sense that it is used primarily for communication, information gathering, and games rather than for passively experiencing narrative stories” (Tarpley, 2001, p. 551). Further, different sites support (Dix, 2005), and different users require (Johnson, in press; LaRose & Eastin, 2004), variation in sensory stimulation and active involvement. In this regard, Internet use during the developmental years may have a greater cognitive impact than previous technological innovations (Johnson, 2006). While video games are not dependent on the Internet, the Internet provides access to many gaming experiences. Approximately one-third of the time that children are online, they report playing games (Roberts et al., 2004). DeBell and Chapman (2006) concluded that Internet use promotes cognitive development in children, “specifically in the area of visual intelligence, where certain computer activities -particularly games -may enhance the ability to monitor several visual stimuli at once, to read diagrams, recognize icons, and visualize spatial relationships” (p. 3). Greene and Bavelier (2003) noted that on a range of visual attention skills, video game players out-performed those not exposed to video games. They concluded that “although video-game playing may seem to be rather mindless, it is capable of radically altering visual attention processing” (p. 536). Reportedly, visual-spatial skills such as mental rotation of shapes are superior in those who play video games (Sims & Mayer, 2002). In a comprehensive review of the literature, Subrahmanyam, Kraut, Greenfield, and Gross (2001) concluded that cognitive processes improve by playing video games. According to early childhood educators, the Internet supports emergent literacy, builds problem-solving skills, and facilitates concept development (Lynch & Warner, 2004; Parette, Hourcade, & Heiple, 2000). Clements and Samara (2003) recommended Internet technology as a tool for improving children's learning through exploration, creative problem solving, and self-guided instruction. Fuchs and Wosmann (2005) claimed that the Internet helps children “exploit enormous information possibilities for schooling purposes and increase learning through communication” (p. 4). Jackson and colleagues (2006) provided low income children home-based Internet access and continuously recorded online behavior. “Findings indicated that children who used the Internet more had higher scores on standardized tests of reading achievement and higher grade point averages 6 months, 1 year, and 16 months later than did children who used the Internet less” (p. 429). Johnson (2006) cautioned that “current anxiety surrounding children’s Internet use should be for those whose cognitive processes are not influenced by the cultural tool” (p. 570).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.327
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2007
Admission routes2
Has abstractyes

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