Black university students’ lived connections between classroom assessment and motivation
Bibliographic record
Abstract
• 20 Black college students describe the connection between assessment and motivation. • The reality of being Black permeated all experiences. • Students felt personal and social pressure to excel in assessment and persist. • Vague assessment criteria were perceived as having more space for racism and bias. • Students took assessment very seriously and were motivated to achieve. Most school systems involve an implicit connection between classroom assessment and student motivation. How visible minority students, particularly students who are Black, experience this connection given the historical and ongoing racism associated with assessment practices, is unknown. This study aimed to understand how Black students in North American post-secondary settings experience assessment and its association with motivation. Using an interpretive phenomenological analysis, we conducted focus groups with 20 Black students with various cultural backgrounds to discern connections between assessment and motivation in the North American context. We identified three overarching themes that described how participants connect assessment with motivation: the reality of being Black; a combined desire and need to excel; and nuances of the assessment and environment. The examples participants shared align with much of the existing literature documenting the tension between experiences of anti-black racism and individual and familial expectations for success, that create importance around assessment and sustain motivation. We discuss the results in light of the attitude-achievement paradox and provide recommendations for instructors regarding the assessment climate.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".