Using the user's mental model to guide the integration of information space into information need
Bibliographic record
Abstract
Abstract The study reported here tested the efficacy of an information retrieval system output summary and visualization scheme for undergraduates taking a Vietnam War history who were in Kuhlthau's Stage 3 of researching a history essay. The visualization scheme consisted of (a) the undergraduate's own visualization of his or her essay topic, drawn by the student on the bottom half of a sheet of paper, and (b) a visualization of the information space (determined by index term counting) on the top‐half of the same page. To test the visualization scheme, students enrolled in a Vietnam War history course were randomly assigned to either the visualization scheme group, who received a high recall search output, or the nonvisualization group, who received a high precision search output. The dependent variable was the mark awarded the essay by the course instructor. There was no significant difference between the mean marks for the two groups. We were pleasantly surprised with this result given the bad reputation of high recall as a practical search strategy. We hypothesize that a more pro‐active visualization system is needed that takes the student through the process of using the visualization scheme, including steps that induce student cognition about task–subject objectives.
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 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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".