Reference and Inspiration on Teaching of Chemistry Courses Based on “TBL + Two-Stage Exam” from UBC University in Canada
Bibliographic record
Abstract
摘要： 我国高等教育改革正在稳步进行并且逐步深化，教学、考试改革是高等教育教学改革的重要部分。文章介绍了加拿大英属哥伦比亚大学（UBC）理科大班教学中广泛使用的“TBL教学+两阶段考试（Two-stage Exam）模式”，并分析和汇总了教学-考核模式在课程中实际的使用效果和反馈。TBL教学提高了大班教学效率，两阶段考试将考试变成了一种学习方式。这种教育-考核模式打破了长期以来的单人考核模式，突出了同学间协作、交流的重要性，激发了学生主动学习的热情，同时也培养了学生社交沟通的能力。文章中同时也探讨了这种模式在实施中可能出现的不足和问题，希望能对我国高等理科大班课程教学改革提供一些可以借鉴的思路。
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".