Feel Good Incorporated: Using Positively Framed Feedback in Library Instruction Course Evaluations Using a Survivorship-Bias Lens
Bibliographic record
Abstract
Objective – This research project makes use of a large dataset of directly solicited positively framed student feedback on virtual library instruction in order to 1. Identify potential improvements to instructional instrument, and 2. Create method for using positively framed student feedback for instructional improvement. Methods – Research team used content analysis to tag student responses using a rubric based on learning objectives and structure of instructional instrument. Tags were analyzed to identify patterns and categorize student-identified research skills. Results – An interpretive lens based on concepts from survivorship bias was used to highlight frequency differences between student identified skills and learning objectives. Gaps were identified between expected range of outcomes and actual range of outcomes, highlighting potential areas of instructional instrument that could be improved or given greater emphasis to ensure retention. Conclusion – A survivorship bias lens combined with a large dataset and a structured set of learning outcomes can make directly solicited positively framed feedback into a tool for instructional improvement.
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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 | 0.083 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".