Teaching Python: Adapting to Diversity
summary
Advocacy led to formal New Zealand digital-technology standards and an after-school Datacom program that taught Python in eight schools across deciles, with attendance, completion and credit rates tracked (completers earned credits at high rates, though selection bias inflates results). Lessons use structure diagrams mapped to Python indentation, simple integer boundary validation for merit, and try/except for unexpected types proved confusing so later preferred value-checking. Classes ranged from low-decile students who experimented eagerly to high-decile girls who avoided failure; short term weekly sessions improved confidence but limited “muscle” practice.
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chapters (11)
- 0:00Start
- 3:52Professional Development (PD) Opportunities
- 4:59Resource Sharing
- 5:14Teacher Background
- 7:21Motivation for My Work: Statistics from 2011
- 8:53The Project 2014 - 2016
- 11:01Python and the standards
- 13:12School Profiles
- 15:04School Statistics
- 22:01Level 1 Problem Example
- 22:50Level 1 Planning Solution
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