Online Behavior and Cognitive Development
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".