Bibliographic record
Abstract
This workshop will focus on promoting student engagement through small changes within the traditional lecture-based model of an undergraduate mathematics class. The workshop focuses specifically on the language we use to introduce courses, to teach in the classroom, and to provide support to students. The workshop seeks to identify unhelpful trends in the language which is commonly used in the mathematics classroom and to offer alternatives which have been shown through research to cultivate a more positive learning environment (e.g., Slattery & Carlson, 2005; Mesa & Chang, 2010; Rattan, Good & Dweck, 2012).\nThe workshop begins by comparing the effect of using mathematical jargon versus accessible vocabulary when introducing a mathematical subject to new students. Next, the facilitator will define and identify monogloss versus heterogloss voice and discuss the impact each can have on classroom discourse. Finally, the workshop looks at the way in which we provide feedback to students. Even with the best intentions, the language used when providing feedback can actually result in demotivating students and lowering their expectation of success. Participants will consider examples of statements to avoid and construct a model for providing more effective feedback.\nMany undergraduate students see math courses as a necessary evil they must “suffer through” and as such are disengaged from the material. This workshop aims to show that we can improve student motivation and achievement by using accessible language in the course syllabus, by employing linguistic techniques which promote student participation in the classroom, and by offering strategy-oriented feedback throughout the course. This workshop will apply these techniques to address four main areas of improvement in an undergraduate mathematics course (see Figure 1). Working from the first to the last day of class, small changes in language can be used to address the important questions found at each stage.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".