Summer Study Tours: Making the Most of a Preeminent Professional Development Opportunity
Bibliographic record
Abstract
ummer study tours present an unparalleled opportunity for teachers to continue learning about other people and places, to make new friends, to network, to internationalize curricula, and in the end, to rekindle enthusiasm for teaching.I have been privileged to be part of two Asia study tours.In 1997, I spent July in China with six colleagues, compliments of a Freeman Foundation grant.We traveled by bus, train, and plane in Eastern and Central China visiting cities, schools, and historic sites.In 1999, I spent two weeks in Japan with twenty other social studies educators from the United States, Canada, the United Kingdom, and Australia as part of the Keizai Koho Fellowship program.We spent one week in Tokyo visiting businesses and schools, then traveled by bus and bullet train to Hiroshima and Kyoto, where we visited more businesses and schools, completed a home stay, and sang some terrible karaoke.In this paper I turn what I've learned on these trips and related international experiences into ten suggestions intended to help educators preparing to participate in study tours make the most of their experiences.I also hope to inspire other readers to seek out and apply for travel opportunities on international summer study tours.One: Before departing, learn as much as possible about your destination.Read about its history, learn some "survival" phrases and sentences, and read travel guides and back issues of The Economist.Watch relevant documentary films, attend lectures by specialists on the country or region, and get a feel for the country's geography, economic strengths, and domestic and foreign issues.Learn the name of the president and prime minister and know the nation's current population.In short, become conversant about the country so that you'll ask better questions of your guides, earn the respect of your hosts, and create positive intellectual momentum.For example, before traveling to Japan, I read the "Japan" chapter in a wonderful book titled, Women in the Material World. 1 The chapter consisted of a photo essay and narrative about one typical Japanese female head of household.In reading it, I began to learn about women's issues in Japan.Then, when I completed a homestay and befriended the mother of the family, I better understood her situation because it closely paralleled that of the Japanese woman in the book chapter.Also, pre-travel orientations differ in their thoroughness.Be sure to ask about itinerary details, what to pack, what immunizations to get, how much spending money to take, and the best way to exchange money and make purchases.Two: Before departing, study exemplary curricula that engage students in thinking deeply about your travel destination and find ideas and inspiration for the curriculum writing you will do when you complete the study tour.For example, examine some of the excellent curriculum units produced by the
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".