What's in a Name? Exploring the Impact of Naming Assignments
Bibliographic record
Abstract
Past research has examined how various elements and style of a syllabus influence students’ perceptions of the class. Furthermore, students’ learning and grade orientations have been shown to impact academic performance and effort. We sought to add to this literature by exploring how an assignment’s name might impact estimates of time to be spent on and the importance of the assignment. We also explored the separate interaction effect of the attitudes and behaviors subscales of these orientations on students’ perceptions separately. In total, 159 undergraduate students completed a survey with a written assignment called “Quiz,” “Exam,” or “Journal.” Participants answered questions from the LOGO-II scale, and regarding their anticipated effort, time to be spent on, and the importance of the assignment. We found that the quiz and exam were perceived as more important than the journal even though participants reported spending the least amount of time on the quiz. Significant interactions between name and learning/grade orientation suggest that for students with high motivation to learn (attitudes and behaviors), all assignments are perceived as an opportunity to learn. However, for students focused on grades (grade orientation behaviors), all graded assignments are opportunities for grades and hence equally important. These results support analyzing attitudes and behaviors separately. Results are discussed in light of previous research and directions for future research.
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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 | 0.007 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".