Scaffolding Self-direction with the ACRL Framework: A Reflection-Based Approach
Bibliographic record
Abstract
This case study reports on the information literacy component of a pilot first-yearexperience course, U1X, at Concordia University. Based on the ACRL Framework for Information Literacy for Higher Education, the information literacy component of U1X was designed to encourage self-direction. Through exploring the university’s larger research mission, the module aimed to shift the emphasis from “how to do research correctly” to viewing research as a personal endeavour in which the researcher cultivates the skills necessary to make a meaningful contribution. It encouraged students to reflect on what they could contribute and the skills they would need to do so. The design of this module aligned with the U1X syllabus, which included as learning outcomes that students gain an understanding “of the University’s research mission at its highest level” and “of the relationship between research and citizenship.” The module took a similarly “big picture” approach, while also looking at students’ personal development through reflection. This paper will explore the challenges and opportunities of this approach.
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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 | 0.039 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".