The Latent Curriculum: Breaking Conceptual Barriers to Information Architecture
Bibliographic record
Abstract
In online instruction there is a physical and temporal distance between students and instructors that is not present in face-to-face instruction, which has implications for developing online curricula. This paper examines information literacy components of Introduction to Systematic Reviews, an online graduate-level course offered at the University of Saskatchewan. Course evaluation suggested that, although the screencast tutorials were well accepted by the students as a method of learning, there was need to enhance their content. Through grading of assignments, consultations with the students, and evaluation of the final search strategies, the authors identified common aspects of search strategy development with which the students struggled throughout the course. There was a need to unpack the curriculum to more clearly identify specific areas that needed to be expanded or improved. Bloom’s Revised Taxonomy was utilized as the construct to identify information literacy learning objectives at a relatively granular level. Comparison of learning objectives and the content of the screencast tutorials revealed disparities between desired outcomes and the curriculum (particularly for high-level thinking) – the latent curriculum. Analyzing curricula using a tool like Bloom’s Revised Taxonomy will help information literacy librarians recognize hidden or latent learning objectives.
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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 | 0.026 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".