Making an impact through experiential learning
Bibliographic record
Abstract
ABSTRACT This panel focuses on experiential learning as a foundation of information science education. We critically examine the underlying philosophies, pedagogical attitudes, and specific teaching methods needed to foster a new generation of information science professionals. Spanning the pedagogical spectrum from theory to practice, we analyze how the integration of humanistic and progressive pedagogies, principles of student‐centered and facilitative learning, and problem‐based projects can contribute to the holistic education of creative leaders and lifelong learners whose skills and knowledge are congruent with the fluid and complex character of our field. Drawing on a combined framework from several theoretical studies in adult education, we examine the potential impact of experiential learning on the conception and perception of learning in higher education, on the information science curriculum, and on the nature of the student‐teacher relationship. In the spirit of the panel, we invite the session attendees to reflect on the introduced ideas in application to their own pedagogical practices, teaching styles, and courses through several interactive exercises and group discussions. These activities illustrate how experiential learning presents a basis for change in information science education.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".