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.
Self-Directed Learning
2024-12-03
Computing in the News
Australia Bans Social Media Until Age 16
micro:bit CreateAI
Integrating with a MakeCode project
2024-12-05
Critical Thinking
Here are some examples from ChatGPT:
Belief Bias
Belief bias happens when someone thinks an argument is true or good just because they agree with the conclusion, not because the reasoning is logical.
Example:
- Belief: “Homework is bad for students.”
- Argument: “Homework is bad because it always makes kids stressed and doesn’t help them learn.”
- Why it’s a bias: You might agree that homework is bad, but the reasoning here isn’t strong—homework doesn’t always make students stressed, and it can help with learning. You believe the argument just because you like the conclusion.
Discussion Prompt: Can you think of an example when someone made a weak argument, but you believed it because you agreed with their conclusion?
Burden of Proof
Burden of proof means the responsibility to prove something is true. If someone makes a claim, it’s their job to provide evidence.
Example:
- Claim: “Our class will win the school competition because we’re the smartest.”
- Response: “That’s a big claim. What evidence do you have to prove we’re the smartest class?”
- Why it’s important: The person making the claim should provide facts or evidence, not just an opinion. Without proof, their claim might not be valid.
Advent of Code
From the web site: “Advent of Code is an Advent calendar of small programming puzzles for a variety of skill levels that can be solved in any programming language you like. People use them as interview prep, company training, university coursework, practice problems, a speed contest, or to challenge each other.”
Let’s solve day 1, part 1, using Python in the Python Tutor Visualizer.
One Possible Solution
nums1 = [3, 4, 2, 1, 3, 3]
nums2 = [4, 3, 5, 3, 9, 3]
nums1.sort()
nums2.sort()
zipped = list(zip(nums1, nums2))
total_dist = 0
for num1, num2 in zipped:
total_dist += abs(num1 - num2)
print(total_dist)
</a>2024-12-10
Computing in the News: Advent of Code
Solving Advent of Code Day 1, Part 1
Self-Directed Learning
</a>2024-12-12
Critical Thinking
We’ll try a practice quiz on biases and fallacies from Gemini Advanced 1.5 Pro with Deep Research.
Advent of Code
Let’s solve day 7, part 1, using Python in the Python Tutor Visualizer.
2024-12-17
Computing in the News
- MacBook Pro screen on Vision Pro: Like two 5K displays in one.
- Mr. B. makes progress in Advent of Code
You on a Tree
The p5.js sketch