From Syllabus to Final Grades: A Wrap-around Workshop to Support Student Motivation
Bibliographic record
Abstract
Student motivation tends to be a somewhat elusive topic in teaching – either K-12 or post-secondary. And yet almost every instructor desires students who are enthusiastically engaged in the course content and related activities and assignments. Unfortunately, students often become more pragmatic and grade-focused during post-secondary education rendering enthusiasm sometimes in short supply. Achievement motivation can be used to distinguish between these two groups of students: the former is considered intrinsically motivated and the latter extrinsically motivation. Achievement motivation can also go beyond simply describing students to provide concrete suggestions on how instructors can design classroom environments that shift students away from extrinsic and towards intrinsic. If you are interested in helping students (re)embrace their love for learning – regardless of the content, class size, or grading distribution – this workshop is for you. Based on a two SSHRC-funded programs of research, the presenter will describe contemporary theorizing on student motivation and related classroom design principles. Next the presenter will take attendees through a series of activities designed to (a) clarify their own beliefs about student motivation, (b) tailor course components to maximize intrinsic motivation, and (c) offer alternatives to using grades to motivate students.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.013 |
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".