Inquiry Learning: Instructor Perspectives
Bibliographic record
Abstract
The rise of the research intensive university has led some critics to argue that teaching has been discounted in favour of research, and thus the academy has “failed...their undergraduate populations” (Boyer Commission, 1998, p .5). This paper examines instructor perceptions of inquiry at an institution with a significant history of providing inquiry learning opportunities to undergraduate students. Inquiry learning is typically seen as a pedagogical approach, both a method and a process, delivered in small class settings. Qualitative data were collected through interviews (n=16) with instructors who used inquiry in their teaching to elicit instructor perceptions of inquiry teaching. Content analysis was performed on the interviews to extract themes. Results suggest that instructors approached their inquiry teaching differently - some adopting an intentional and some an unintentional approach. There was general consensus that teaching and learning academic skills is valuable, inquiry instructors are facilitators, technology has only a modest place in the inquiry classroom, and larger classes inhibit inquiry. Responses were mixed about the importance of stand-alone inquiry courses. Teaching such courses appears to change the instructor’s perspective about inquiry. L’augmentation des universités qui sont de plus en plus axées sur la recherche a soulevé quelques critiques à l’effet que l’enseignement ait été délaissé au profit de la recherche et que, conséquemment, les institutions d’enseignement ont « laissé tomber… leurs étudiants de premier cycle » (Boyer Commission 1998, p. 5). Le présent article porte sur la perception des enseignants d’un établissement qui préconise depuis longtemps des pratiques pédagogiques notamment la - méthode d’enquête- pour favoriser l’apprentissage de leurs étudiants de premier cycle. Cette méthode est généralement considérée comme une approche pédagogique qui se préoccupe tant de l’objet que du procesus d’apprentissage. Des entrevues réalisées auprès d’enseignants qui utilisent cette méthode ont permis de recueillir des données qualitatives (n=16) afin de savoir ce qu’ils en pensaient. Les chercheurs ont analysé le contenu des entrevues pour en extraire des thèmes. Les résultats indiquent que les enseignants abordent la méthode d’enquête de manière différente : certains adoptant une approche intentionnelle et d’autres une approche non intentionnelle. Les enseignants s’entendent sur les éléments suivants : l’enseignement et l’apprentissage des stratégies pédagogiques sont utiles, les enseignants qui utilisent la méthode d’enquête ont un rôle de facilitateur, la technologie n’occupe qu’une faible place dans la classe où cette méthode est appliquée et les classes nombreuses nuisent à cette méthode. Les avis sont partagées quant à l’utilisation de cette méthode dans les cours sans préalables. Le fait de donner ce type de cours semble modifier l’opinion de l’enseignant quant à cette méthode.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".