Bibliographic record
Abstract
Objective – To explore children’s Internet searching at home in order to make recommendations to designers, researchers, educators, and parents on how to assist children in becoming search literate through understanding children’s search roles. 
 
 Design – Qualitative, exploratory study.
 
 Setting – Children’s homes in the urban areas of Maryland, Virginia, Delaware, and Pennsylvania.
 
 Subjects – 83 children (28 children were age 7, 29 were age 9, and 26 were age 11). 41 of the children were female and 42 were male. Parents of the children were also included in the study. 77% of the parent interviews were carried out with mothers, 15% were with fathers, and 8% were with both parents together. 
 
 Methods – The authors conducted qualitative interviews both with the parents and the children. Parents were interviewed first and the interviews were audiotaped and transcribed. The interviews covered computer rules, children’s experience in searching, searching habits, and areas of frustration. Interviews with the children covered questions about frequency of computer use and reasons for searching. These interviews were video recorded and transcribed. After the interview, the children were asked to complete five search tasks, which were video recorded, and were asked if they had successfully completed the task and why they clicked the link results. The researchers also took notes throughout the interviews and search tasks. The researchers were able to analyze 80 transcripts from the children and 75 transcripts from the parents. The interview transcripts were coded using inductive, qualitative coding starting with open coding to identify categories of children’s search roles. The transcripts from the children interviews were coded three times by one researcher and the coding was verified by another researcher. The transcripts then were coded again using the code book developed by the first researcher. The researchers completed axial and selective coding to refine their search role categories. The researchers also analyzed the data in order to identify behaviours that distinguished the categories from each other. The same coding process was used for the parent interview transcripts. The results from the analysis of the parent interviews were used to verify findings from the children transcripts. 
 
 Main Results – Children searching at home show seven different searching roles: developing, domain-specific, power, nonmotivated, distracted, rule-bound, and visual, with each search role being delineated by specific behaviors and/or abilities. Triggers for searching change as children age, with younger children searching based on personal interests while older children search for school-related information. Children rely on summaries shown on the results page, as well as familiarity with known websites, in deciding which links to click. Children are interested in both moving and still image results, with visual searchers, power, and distracted searchers frequently mentioning images in their interviews. Power searchers, those with the ability to use keywords and with an understanding of search engines, discussed less influence on their searches than others. Parents have more influence over younger children while school has more influence over older children. Parents helped and influenced their children’s searching in varied ways including demonstrating and offering advice for searching and setting rules for searching. Children often reported frustration with their searches, which was also reported by parents. Most of the children were unable to complete the complex search task as they were unable to separate the query into multiple parts. Few gender differences in searching were found, although researchers reported that games were a trigger for boys more often than girls, and boys declined to search more than girls. Girls were more influenced in their searching than boys and stopped searching due to boredom more often than boys. 
 
 Conclusion – The authors suggest that the findings can help search engine designers, researchers, educators, and parents to assist children in becoming search literate. Designers should enable scaffolded, assisted searching in order to help searchers, especially with separating out multiple parts of a complex question and with encouraging fact-checking. Educators and parents can coordinate their efforts to more effectively help children overcome searching frustrations and challenges. Researchers could replicate the study to validate the search roles discovered by the authors and also extend the study to focus on searching in regards to gender and use of other devices, such as smartphones and tablets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.182 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".