Examining the University-Profession Divide: An Inquiry into a Teacher Education Program’s Practices
Bibliographic record
Abstract
This paper focuses on the divide between the university as a site of teacher education and the profession of practicing teachers. We employed a theoretical inquiry methodology on a singular case study which included formulating questions about the phenomena of the university-profession divide (UPD), analysing constituents of the UPD, and developing a language system to represent our findings about the UPD. The questions guiding our examination were: How do we conceptualize this divide? How are these concepts represented in the literature? How can a Teacher Education Program (TEP) respond to the divide? The theoretical inquiry was conducted within a singular case study of a TEP in order to explore the chasm between these two settings in a limited and focused manner. Our inquiry led to the identification of three key concepts: competing cultures, competing expectations, and theory-practice dichotomy. In analyzing these concepts and responding to questions which drove this inquiry from the beginning, we assert that these concepts contribute to the divide and therefore, have implications for teacher education programming. We summarize findings about these three concepts, suggest causes for the chasm, and offer recommendations to address the divide. Finally, we argue that while it is important to address the divide to enhance teacher education, the divide itself is a potentially rich site of possibilities. We contend that a reconceptualization of the UPD in this way might mitigate its negative impact on teacher education curriculum and programming. Cet article se concentre sur l’écart qui existe entre l’université en tant que lieu de formation des enseignants et la profession d’enseignant en exercice. Nous avons employé une méthodologie d’enquête théorique basée sur une seule étude de cas qui comprenait des questions sur l’écart entre université et profession, l’analyse des composantes de cet écart et le développement d’un système de langue pour représenter nos résultats concernant l’écart entre université et profession. Les questions qui ont guidé notre examen étaient les suivantes : Comment conceptualisons-nous cet écart? Comment ces concepts sont-ils représentés dans les publications? Comment un programme de formation des enseignants peut-il répondre à cet écart? L’enquête théorique a été menée sur une seule étude de cas d’un programme de formation des enseignants afin d’explorer le gouffre qui existe entre ces deux domaines d’une manière limitée et ciblée. Notre enquête a permis d’identifier trois concepts clés : la concurrence entre les cultures, la concurrence entre les attentes et la dichotomie entre théorie et pratique. Suite à l’analyse de ces concepts et aux réponses obtenues aux questions qui étaient à l’origine de cette enquête dès le départ, nous pouvons affirmer que ces concepts contribuent à l’écart et, par conséquent, ont une incidence sur les programmes de formation des enseignants. Nous résumons nos résultats concernant ces concepts, suggérons des raisons à l’origine du gouffre et proposons des recommandations pour remédier à l’écart. Pour finir, nous suggérons que, bien qu’il soit important de remédier à l’écart afin d’améliorer la formation des enseignants, l’écart lui-même est un lieu potentiellement rempli de riches possibilités. Nous soutenons qu’une reconceptualisation de l’écart entre université et profession selon cette manière pourrait mitiger ses effets négatifs sur le curriculum et les programmes de formation des enseignants.
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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 | 0.023 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| 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".