Teachers’ Practice and Perceptions of Self-Assessment and Peer Assessment of Presentation Skills
Bibliographic record
Abstract
Assessment has shifted from assessment of learning to assessment for learning. Self-assessment and peer assessment therefore appear to play more important roles as they encourage students to critically reflect on their own and their peers’ learning progress and performance. Although self-assessment and peer assessment of written language performance have been widely explored, assessment of spoken language, especially in presentation skills, is under-explored. Additionally, students’ peer assessments are found to be different from teachers’ assessments (De Grez, Valcke, & Roozen, 2012), with this possibly due to the lack of training. This study aimed to investigate whether in-service teacher participants, with experience in marking students’ performance, would be able to undertake self-assessment and peer assessment effectively in comparison to the teacher’s assessment. The study also intended to explore participants’ perceptions of self-assessment and peer assessment of English presentation skills. The participants were 14 in-service teachers teaching their native language at different levels, ranging from primary to tertiary, who were also studying English as a foreign language. The research instruments were scoring rubrics and an online questionnaire. The data were analysed by Pearson’s correlation coefficients, means and standard deviations. The results revealed that in-service teachers could perform better in peer assessment. The study’s discussion provides fruitful implications for language assessment.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".