A Course Template for Integrating Chinese Dual-Degree Students into a Science Program and Enhancing Their Communication Skills
Bibliographic record
Abstract
In 2013 and 2014, Waterloo’s Earth and Environmental Sciences Department piloted EARTH 10 to provide social and academic support for Chinese dual-degree students during their first term in Canada (these are students who complete BSc degrees in Waterloo after two academic years in China). The 10-week non-credit course focuses on encouraging self-improvement in a safe, supportive environment. Students received content-based instruction from Earth Science faculty and language instructors in activities designed to improve English competency, help them adjust to academic and department specific requirements, and integrate them into the Waterloo community. The course emphasized active learning through hands-on activities (a geological field trip, a lab tour and demo, and group work for an oral presentation). Students completed 2-draft projects that allowed for formative assessment of their scientific and language competency.\nPeer mentors were key to the course’s success: they helped students with assignments, conversation practice, socializing, and orientation to the department. According to subjective and quantifiable metrics that included student self-assessments (interviews and questionnaires), in class progress on assignments, and comparison of performance (grades) in other courses, participants universally benefitted from the course in terms of increased confidence and measurably better academic and classroom performance than non-participant peers. The primary outcome of the course was developing the socio-cultural and interactional competence necessary to achieve academic success. Improvements to design and delivery over the 2-year pilot have made EARTH 10 a worthwhile extracurricular complement to academic programming, one which Waterloo’s Faculty of Science will be offering in four different departments next Fall.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.015 |
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".