Teachers’ conceptions and choices of assessment tasks in a Nigerian postgraduate teacher training
Bibliographic record
Abstract
Student assessment is a process that entails the collection of evidence of learning in diverse and systematic ways to make judgments on students’ learning. What then is the perception of this vital tool in the hands of the users (teachers)? This study investigates teachers’ conceptions of assessment and their choices of assessment tasks in postgraduate teacher training. Action research with one group pretest-posttest design was adopted for the study. The survey used to collect data for this study has three sections (A, B, & C). Section A elicits participants’ personal information; Section B contains 20 different assessment tasks. Section C includes 26 items that examined participants’ conceptions of assessment from four different sub-scales (school accountability, student accountability, improvement of teaching and learning, and irrelevance factors). The researchers further validated the survey, and the Alpha reliability coefficient of the whole scale was 0.85. Data collected from twenty-eight randomly selected teachers out of forty-five were analyzed using descriptive measures and paired sample t-test. Findings revealed that teachers enact both summative and formative assessment tasks but with preferences for summative tasks. A significant difference in teachers’ conception of assessment was recorded, but there was no significant difference in teachers’ assessment conception based on gender. Recommendations are presented to improve the research knowledgebase on assessment in the Nigerian education context.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".