Bibliographic record
Abstract
Objective – This study examined information literacy tutorials in science. The goals of the research were to identify which of the information literacy standards for science, engineering and technology were addressed in the tutorials, and the extent that the tutorials incorporated good pedagogical elements. Methods – The researcher chose for review 31 of the tutorials selected by members of the ACRL Science & Technology Section (STS) Information Literacy Committee. She carefully analyzed the tutorials and developed a database with codes for the topic of each tutorial, the STS information literacy standard(s) addressed by the tutorial, and whether good pedagogical elements were incorporated. The entire analysis and coding procedure was repeated three times to ensure consistency. Results – The tutorials analyzed in this study covered various subjects and addressed all the (STS) information literacy standards. The tutorials presented information clearly and allowed users to select their own learning paths. The incorporation of good pedagogical elements was limited, especially in relation to active learning elements. Conclusions – Web tutorials have been accepted as effective information literacy instruction tools and have been used to teach all elements of the STS information literacy standards. Yet, ensuring they provide a real learning experience for students remains a challenge. More serious thought needs to be given to integrating good pedagogy into these instructional tools in order to attain deep learning.
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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".