Newly qualified teachers’ perceptions and experiences about portfolio assessment in Wa Municipality, Ghana
Bibliographic record
Abstract
Portfolio assessment is a tool that enables professionals to reflect on their development. Despite the popularity of portfolio-based assessment among educators, little research has been conducted in Ghana to establish how newly trained teachers view portfolio assessment as a learning and assessment tool. A survey of newly qualified teachers was performed to learn about their experiences and perceptions of portfolio assessment as a tool for continuing professional development (CPD) and obtaining a full teaching license in Wa Municipality, Upper West Region of Ghana with a total population of 187. Out of the total population,58 newly qualified teachers (NQTs) were selected using a simple random sampling procedure. The researcher used a questionnaire as the predominant instrument to obtain data. Quantitative data were analyzed using descriptive statistics such as tables, charts, and bar graphs. Qualitative data were also analyzed using content analysis. This was based on analysis of meaning and implications emanating from the respondents’ information. The portfolio was deemed a good learning tool by the majority of the research participants, the newly qualified teachers. They did, however, believe that creating a proper portfolio is stressful and time-consuming. According to the study's findings, the system will not succeed unless students receive proper direction from academic professionals like National Teaching Council (NTC), in-service teachers, and headteachers, etc.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".