Doctoral studentsâ mental models of a web search engine : an exploratory study
Bibliographic record
Abstract
This exploratory research investigates the factors that might influence a specific group of users’ mental models of a Web search engine, Google, as measured in the dimension of completeness. A modified mental model completeness scale (MMCS) was developed based on Borgman’s, Dimitroff s, and Saxon’s models, encompassing the perception of (1) the nature of the Web search engine, (2) searching features of the Web search engine, and (3) the interaction between the searcher and the Web search engine. With this scale, a participant’s mental model completeness level was determined by how many components of the first two parts of the scale were described and which level of interaction between the participant and Google was revealed during the searches. The choice of the factors was based on the previous studies on individual differences among information seekers, including user’s search experience, cognitive style, learning style, technical aptitudes, training received, discipline, and gender. Sixteen Ph.D. students whose first language is English participated in the research. Individual semi-structured interviews were conducted to determine the students’ mental model completeness level (MMCL) as well as their search experience, training received, discipline and gender. Direct observation technique was employed to observe students’ actual interactions with Google. Standard tests were administered to determine the students’ cognitive styles, learning styles and technical aptitudes.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".