Teaching Information Retrieval With Web-based Interactive Visualization
Bibliographic record
Abstract
Interactive visualization is a powerful educational tool, which has been used to enhance the teaching of various subjects from computer science to chemistry to engineering. This paper describes the use of interactive visualization tools in the context of a graduate course in information retrieval, to demonstrate two well-known retrieval models, the Boolean model and the vector space model. The results of five classroom studies with these tools are reported. The impact of the tools on student learning, as well as student attitudes toward the tools, were investigated. The results of the classroom studies indicate that use of interactive visualization in a homework context can result in significant growth of knowledge. The majority of the students recognize the value of interactive visualization and recommend its use in the context of information retrieval courses. The study also demonstrated that visualization focusing on less known and harder to understand topics causes a larger growth of knowledge and is perceived as more useful. This result suggests placing higher priority on the development of visualization tools for harder to understand topics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".