St. Perpetua 2024–25 Computer Science Elective Trimester 2
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Classroom and Self-Directed Learning Resources
- Mr. Briccetti’s YouTube Channel with many programming lessons for you to explore on your own
- MakeCode
- Block-based Programming Environments
- MicroBlocks
- EduBlocks
- Blockly Games
- Snap!
- Run Snap!
- Snap! Reference Manual
- Snap! Crash Course
- “Why Do We Have to Learn This Baby Language?” from Brian Harvey, Teaching Professor Emeritus, University of California, Berkeley
- micro:bit Python editor
- Visualizing your Python program with Python Tutor Visualizer
- p5.js
- Tinkercad
- Beauty and Joy of Computing Curricula
- BJC Sparks for Middle School and Early High School
- BJC for High School (you are free to explore this if you run out of things to do in the middle school curriculum)
- code.org
- Zooniverse
- Teachable Machine
- Music
First Day, 2024-11-07
Welcome to Computer Science
Your Previous Computer Science Experience
Join Your Class in Google Classroom
Critical Thinking
- Anchoring bias: The tendency to rely too heavily on the first piece of information encountered when making decisions.
- Strawman fallacy: Misrepresenting someone’s argument to make it easier to attack.
micro:bits and Smart Cutebot Cars
Self-Directed Learning Activities
See Resources at the top of this page.
2024-11-12
Computing in the News
Chinese Air Fryers May Be Spying on Consumers
Mr. B. Works on a Foursquare line fault detector
micro:bits and Smart Cutebot Cars
Self-Directed Learning Activities
See Resources at the top of this page.
2024-11-14
Critical Thinking
micro:bits and Smart Cutebot Cars
Car Links
Self-Directed Learning Activities
See Resources at the top of this page.
2024-11-19
Computing in the News
Pong Creator Al Alcorn is a Computer History Museum Fellow
micro:bits and Smart Cutebot Cars
Controlling a servo motor
Self-Directed Learning
2024-11-21
Critical Thinking
Misleading Nature of “Earn as Much as $100 an Hour” Advertisements
Advertisements that use phrases like “earn as much as $100 an hour” can be misleading because they emphasize the maximum potential earnings without clarifying that this is not the typical or average income for most workers in that role. Here’s why this can be deceptive:
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Selective Highlighting: The ad focuses on the highest possible earning scenario, which may only apply under very specific or rare circumstances. For example, earning $100 an hour might require achieving difficult performance metrics, working during peak demand hours, or taking on undesirable tasks.
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Lack of Context: These ads often omit important details, such as how many people actually achieve the stated earnings, the average hourly pay, or the conditions required to earn that amount.
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False Expectations: By presenting the highest earning potential prominently, the ad creates an impression that this level of income is common or easily attainable, which may not be the case.
How Anchor Bias Relates
Yes, anchor bias can play a role in these advertisements. Anchor bias occurs when people’s decisions or perceptions are influenced by an initial piece of information—the “anchor.” In this case, the “$100 an hour” serves as the anchor. Here’s how it works:
- Initial Perception: When potential applicants see “$100 an hour,” they are anchored to that number and may overestimate their likely earnings.
- Contrast Effect: Even if the ad later discloses the average earnings or lower starting rates, people may still judge the opportunity favorably because the anchor ($100) skews their expectations.
- Cognitive Shortcut: People might not analyze the details carefully and instead focus on the anchor because it is easy to remember and appears appealing.
Summary
These advertisements exploit anchor bias to make an opportunity seem more attractive than it might actually be. The “$100 an hour” figure sticks in people’s minds, even if it doesn’t reflect the typical experience. To avoid being misled, it’s important to look for disclaimers, average pay rates, and the conditions required to reach the advertised maximum earnings.
Quiz on biases and fallacies
micro:bit CreateAI
Remember Google’s Teachable Machine? Now we can do something similar with the micro:bit.