Weblogs as Tools for Encouraging Self-Reflection and Peer Feedback among Student Teachers
Bibliographic record
Abstract
A weblog is one of the most effective tools among the latest inventions that enhance student teachers’ learning and practice. With technology becoming crucial for both personal and professional developments, this study focused on the effectiveness of using reflective weblogs in teacher education programs. In this regard, the research investigated the level to which weblogs successfully promote self-reflection and yield peer feedback among student teachers. Furthermore, it explored student teachers’ perceptions regarding the use of weblogs as tools for self-reflection and peer feedback. A case study of seven EFL student teachers taking a practicum course at Kuwait University was analyzed in this paper. The study was conducted in the English Curricula and Teaching Methods Department in the College of Education during the first semester of the 2013/2014 academic year. During the 4-week application period, participants were requested to reflect on their teaching practices and provide feedback on their peers’ posts. The data were collected through different qualitative methods such as semi-structured interviews and content analysis. The findings of the study suggest that the use of the practicum blog is considered to be effective in facilitating student teachers’ ability to reflect upon their teaching practices and provide comments on their peers during the practicum course. Most participants agree on the usefulness of using weblogs in teacher education programs. Overall, the study results show that student teachers find the weblog as an effective tool for writing reflections, sharing ideas, providing feedbacks, and increasing proficiency levels. The results of the study provide the rationale for using weblogs in student teacher education programs.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".