Bibliographic record
Abstract
Purpose The purpose of this paper is to call into question the most longstanding pedagogical practices in academia while analyzing their potential to foster student creativity and innovation in the classroom. While some suggestions advanced in this paper may not have the same importance in other fields and disciplines, they are highly relevant in the applied, interdisciplinary, and very fast moving field of Library and Information Science (LIS). Design/methodology/approach Positioning creativity as a teachable skill and relying on the learner-centered pedagogy of Carl Rogers, the paper presents a model that can serve as a litmus test for the creative potential of graduate-level assignments in LIS programs. The model is called “Walls,” “Doors,” and “Fences” (WalDorF); these terms refer to specific statements in graduate assignment descriptions that are necessary (“Walls”); conducive to creative expression (“Doors”); or unjustifiably restrictive (“Fences”). The paper uses a sample assignment from a “Foundations of LIS” course to illustrate the model; it also provides several examples of the WalDorF model application in other LIS courses. Findings Using the WalDorF model, the paper revisits and challenges some of the most common pedagogical practices in graduate LIS teaching, including the prevalence of written papers as course assignments; the implications of equating “research” with an overview of secondary literature; the need for professors’ approvals of research topics; the meaning of the “quality of writing;” the imperative of “academic” writing as opposed to other types of writing; the word/page limit; the use of standardized reference styles; the class participation requirement; and the late assignment policies, among others. Originality/value The real change in education is foundational and goes beyond cosmetic improvements. If we want to develop learning experiences that tap into students’ creative potential, the very core of our approaches needs to be scrutinized and questioned, even the centuries-old staples of academic teaching. At the end of the day, we may decide that changing things is not in the best interests of learning. However, a complete critical analytical work must be done to convince and reassure ourselves that tried-and-true methods are the best way to go. The proposed WalDorF model presents one possible frame for critical revision.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".