First Impressions Matter! An Experiment Comparing Autonomous and Controlling Language in Course Syllabi
Bibliographic record
Abstract
The course syllabus serves as an important first contact between professors and students in university courses and the language used in a syllabus can influence students’ first impressions of the professor and expectations for the course. Existing research in Self-Determination Theory has shown that autonomy-supportive language leads to increased positive outcomes for students compared to controlling language. The objective of the present studies was to compare an autonomy-supportive with a controlling syllabus to see how students felt when reading the syllabus (Study 1), and how the syllabus related to their impressions of the professor, reported motivation, and expectations for the course (Study 2). The results of Study 1 supported that the students reported more positive feelings when viewing the autonomy-supportive syllabus and perceived the autonomy-supportive syllabus was more autonomous and the controlling one was more controlling. In Study 2, the results showed that students who viewed the autonomy-supportive syllabus reported more positive impressions of the professor (more need-supportive, better quality), were more likely to have positive expectations about the course, and more likely to have a self-determined motivation towards attending class compared to students who viewed the controlling syllabus. Overall, the results from both studies supported that there are benefits to using autonomy-supportive language in a syllabus with few side effects. Professors could benefit by making a good first impression upon students by integrating autonomy-supportive language into their syllabus.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".