Comparative Study of Teaching Content in Teacher Education Programmes in Canada, Denmark, Finland and Singapore
Bibliographic record
Abstract
Plank, Merle Varendi, and Anne Villems describe the process of quality assurance of e-courses in Estonia.They describe the development of the process and discuss the experiences gained over the past years.The rapid increase of e-learning initiatives has led to the need to identify and disseminate the best practices in elearning course design and instructional materials.Different aspects of teacher education and curricula were addressed in a number of papers.The first paper by Jens Rasmussen and Martin Bayer presents the results of a comparative study of the content in teacher education programmes for primary and lower secondary teachers in four countries, namely Denmark, Canada, Finland and Singapore.The latter three score highly in international comparisons, such as PISA and TIMMS.Notwithstanding differences in certain areas, the authors conclude that at times, greater differences can be found between the four individual countries.Describing the case of one institution, Anneli Kasesalu, Sirje Piht, Piret Lehiste, and Rea Raus report the findings of a study that investigated teacher education graduates' experiences concerning their readiness to enter the teaching profession.The authors view the novice teachers' experiences from social, professional and personal perspectives reminding us of the many-faceted nature of quality in teacher education.The paper by Sheila Henderson and Brian Hudson builds on the findings of a set of studies exploring the mathematical competence, confidence, attitudes and beliefs of primary student teachers.The authors point out the importance of acknowledging student teachers' beliefs about teaching and learning mathematics as well as about the nature of mathematics itself in order to encourage professional competence development and quality teaching.Iuliana Marchis investigates the self-regulated learning of teachers of mathematics in Romania, pointing out the important relationship between teacher competence and student learning.Liliana Ciascai and Lavinia Haiduc describe a study on pupils' attitudes towards natural sciences and opinions about the New Threats in Advanced Knowledge-based Economies to the Old Problem of Developing and Sustaining Quality Teacher 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".