Blended Learning Experience of Students Participating Pedagogical Formation Program: Advantages and Limitation of Blended Education
Bibliographic record
Abstract
The aim of this study was to investigate the learning experience of students studying pedagogic formation in blended design with regard to attendance, self confidence, and attitudes toward both Pedagogic Formation Program (PFP) and the teaching profession. In order to achieve this aim, a qualitative case study approach was carried out. The participants of this study consisted of 154 graduated Faculty of Arts and Sciences students who were enrolled in the first blended PFP in Turkey. A qualitative case study was conducted. Data were obtained through an open-ended questionnaire (n=154) and focus group interviews (n=8). The qualitative data were analyzed by using content analysis techniques. Overall, the results indicated that blended PFP was highly promising regarding professional development, self-confidence, accessibility and eliminating some disadvantages of distance education. Specifically, inherent problems of online education continued to take place in blended design but a balanced blended approach could minimize these weaknesses. Participants indicated that face-to-face sections were more applied, authentic and effective than the online part. On the other hand, most of the participants preferred to attend the online lessons regularly. It was mainly because of availability concerns, travelling, and comfort of their home.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| 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".