Pre-Service Physical Education Teachers’ Perceptions of Anticipated Challenges and Needs during Teacher Education Programs
Bibliographic record
Abstract
Given that teacher dropout is an issue for beginning teachers, it is important to be proactive in order to retain teachers within the profession. Physical Education pre-service teachers’ education programs represent a crucial part of their professional development in preparing them to face the challenges that often explain retirement, especially students’ motivation. Authors recognize the importance of considering pre-service teachers’ needs during teacher education programs and their concerns about the challenges to be faced once they start teaching (Richards et al., 2013). Using a qualitative approach, this study aims to: (1) identify pre-service Physical Education teachers’ perceptions of anticipated challenges in general, (2) identify the specific challenges they anticipate aboutsupporting students’ motivation and (3) describe how they can be prepared to support students’ motivation. Participants consisted of 18 pre-service Physical Education teachers (Mage = 25; SD = 3.61 years) from French-language universities in Quebec (Canada). Four focus groups were conducted, and data were analyzed consistent with the four steps suggested by Boutin (2007). Results indicate that the main challenges anticipated by pre-service Physical Education teachers are classroom management and students’ lack of motivation. In terms of supporting students’ motivation, five specific challenges were highlighted: (1) student heterogeneity, (2) proposal of learning activities to support motivation, (3) student engagement, (4) management of disengaged students, and (5) gender differences. As for their needs during teacher education program, participants wished to learn how to plan motivational strategies, be given more opportunities to practice, and discuss how to implement these strategies. Recommendations for teachereducation programs are discussed in the conclusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".