Critical Online Resource Evaluation in Secondary Schools: \nA Descriptive, Multiple-case Study of Teachers in Quebec
Bibliographic record
Abstract
Little is known about the circumstances surrounding how teachers guide their students to understand the credibility and relevance of information they research on the Internet. This two-part qualitative, multiple-case study examined how 11 secondary teachers in Quebec approached instructing their students in critical online resource evaluation (CORE). In Strand 1, an environmental scan of the Quebec landscape was done focusing on materials available to teachers for CORE. This included a systematic review through an extensive search of the website of every English school board, as well as informal discussions with subject matter experts (SMEs) such as a professor of journalism and a professor specializing on information literacy. In Strand 2, teachers shared their varied approaches to teaching CORE in semi-structured interviews. Participants from both public and private schools were selected based on their interest in the study, the grade level they taught, and the subject matter. Within-case and cross-case analysis revealed an overall understanding and acceptance of the importance of CORE in its basic definition. Further, several patterns were observed across cases such as common approaches to teaching CORE. Some obstacles hindering teachers were uncovered including gaps in government curriculum documents such as the Quebec Education Program (QEP) and a lack of time for teachers to adequately prepare and plan to teach materials that they are generally self-creating. A strength, weaknesses, opportunities, and threats (SWOT) analysis completes the discussion. Implications for the future include more targeted professional development options for teachers and clearer direction from government-mandated curriculum. More research is necessary to expand the scope of understanding of CORE past this limited sample.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".