Task‐based information retrieval: Structuring undergraduate history essays for better course evaluation using essay‐type visualizations
Bibliographic record
Abstract
Abstract When domain novices are in C.C. Kuhlthau's (1993) Stage 3, the exploration stage of researching an assignment, they often do not know their information need; this causes them to go back to Stage 2, the topic‐selection stage, when they are selecting keywords to formulate their query to an Information Retrieval (IR) system. Our hypothesis is that instead of going backward, they should be going forward toward a goal state—the performance of the task for which they are seeking the information. If they can somehow construct their goal state into a query, this forward‐looking query better operationalizes their information need than does a topic‐based query. For domain novice undergraduates seeking information for a course essay, we define their task as selecting a high‐impact essay structure which will put the students' learning on display for the course instructor who will evaluate the essay. We report a study of first‐year history undergraduate students which tested the use and effectiveness of “essay type” as a task‐focused query‐formulation device. We randomly assigned 78 history undergraduates to an intervention group and a control group. The dependent variable was essay quality, based on (a) an evaluation of the student's essay by a research team member, and (b) the marks given to the student's essay by the course instructor. We found that conscious or formal consideration of essay type is inconclusive as a basis of a task‐focused query‐formulation device for IR.
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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 | 0.005 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".