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。
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".