Do students perceive assessment differently? Exploring the diverse ways students conceive assessment and its impact on their assessment experiences and engagement
Bibliographic record
Abstract
This study is part of a larger dissertation that examined the assessment culture within Ghana’s education system. This paper specifically investigates the diverse conceptions of assessment held by students and how these perceptions shape their learning-related behaviours and assessment experiences. In total, 405 junior high school (Grades 7–9) and senior high school (Grades 10–12) students responded to the Students’ Conceptions of Assessment Inventory (SCoA-VI). Second-order confirmatory factor analysis (CFA) and latent profile analysis (LPA) were conducted to identify distinct patterns in students’ conceptions of assessment. To explore potential associations between students’ conceptions of assessment and their demographics, chi-square (crosstabulation) analyses were conducted. Three primary patterns emerged: mixed, improvement and negative conceptions of assessment. These three distinct conceptions, held by students within the same educational system, shaped their assessment experiences in different ways. Demographic factors, such as school division, class level and geographic location, significantly influenced these conceptions. Junior high students generally held more positive views than senior high students, who face high-stakes examination pressures. Urban students tended to have more positive conceptions of assessment than rural students. Implications for policy and practice are discussed.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".