Supporting early undergraduate students: Using video to introduce critical reading skills in scaffolded information literacy instruction
Bibliographic record
Abstract
Students’ ability to develop transferable skills, such as those that correspond to information literacy (IL) and writing, is an expectation and trend that continues to gain momentum within higher education. To support this initiative, librarians at the University of Manitoba embraced a scaffolded instructional approach; a technique where outcomes are deconstructed and content is presented in “building complexity towards the final deliverable” (Lowe, Stone, Booth & Tagge, 2016, p. 127). IL scaffolding begins with a session on critical reading and addresses more traditional topics such as searching and citing later in the term. Although library instruction tends to emphasize “one-shot” teaching, the authors’ anecdotal evidence suggests that a multi-session approach is a better fit. Furthermore, they identified critical reading as a gap in instruction for first-year students. By using a YouTube video, librarians introduce critical reading in a familiar context, described in this chapter. The exercise can be used across various disciplines and class sizes, and has been delivered most often in Humanities and Social Science courses by the authors. Material under a CC BY-NC-ND license.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".