--Online Distance Learning Assignments and Information--
Download AP Computer Science Syllabus
2027 AP Test Information
Section I: End-of-Course Multiple-Choice Exam- Friday May 14, at 8am in the WR Gym.
70 Multiple-Choice Questions | 120 Minutes | 70% of Score | 4 answer options
57 single-select multiple-choice
5 single-select with reading passage about a computing innovation
8 multiple-select multiple-choice: select 2 answers
Section II: Create Performance Task- Project Due Friday April 30, 8:59pm PST.
30% of Score
Students will develop a computer program of their choice. Students need at least 16 hours of in-class time to complete.
Written Response questions about Your Code on AP Exam Day.
AP Computer Science Principles Class Information
Period 1 |
Period 3 |
Period 5 |
Code.org Class Code-
YQYCFB |
Code.org Class Code-
GVWPCS |
Code.org Class Code-
GXPBJZ |
AP Test Class Code-
QGEQJZ |
AP Test Class Code-
APWXYD |
AP Test Class Code-
VEWR6Q |
2026-2027 Fall Semester Assignments, Help, and Information
Week 11 |
Day 26- Tuesday October 20
Sign-Up for 2027 AP Exam Here
- Follow directions on Website
- Per 1- AP Class Code- QGEQJZ
- Per 3- AP Class Code-APWXYD
- Per 5- AP Class Code-VEWR6Q
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YOUTH LEADERSHIP & NONPROFIT OPPORTUNITIES
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Coach B Review Project- 5pts
- 1-3 Students
- Create a Google Form in "Quiz" Format
- Place group member names below the Form Title
- Make me (Casey Burrill) a Collaborator / Editor
- Create 30-35 MC Questions (20-25 if flying solo)- Make questions 1 point each
- Need the answers marked
- I will be using some questions that students create for our test on Thursday
- You may use any tech (or no tech) you want to create your Quiz, but must be in a Google Form format
End of class Coach B Shootout- Extra Credit for today's assignment- Winner=5pts, 2nd=3pts, 3rd=2pts.
Test Thursday- 50 questions, 50 points
Look for best and worst Coach B Drawings in our test questions on Thursday!
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Journal Love
Available Data Unit Topics- Google, Data Center, Misleading Graphs, Google Trends, Apple Headquarters, Apple, Amazon, AWS, Big Data, Open Data, Crowdsourced Data, Citizen Science, Volume, Velocity, Variety, Value, Veracity, Hadoop, Distributed File System, Parallel Processing, Folding at Home, Machine Learning, Supervised Learning, Email Spam Filtering, Predictive Text, Self-Driving Cars, Game AI, Robotics, IBM Watson, Jeopardy Challenge, Artificial Intelligence, ANI, AGI, ASI, GenAI, LLM, Token, AI/ML Bias, Strong Passwords, Privacy Policies, Terms of Use, Privacy Policy Helpful Words to Look For, Facial Recognition, Personal Identifiable Information, Computer Virus, Malware, Keylogging, , Phishing, Spear Phishing, Ransomeware, DDoS Attack, Solarwinds, Encryption, Decryption, Public Key, Private Key, Caesar Cypher, Data Breaches, LA School District Hack, TikTok Hack, and Moneyball Movie.
Test Thursday- 50 Questions, 50 Points
Day27- Thursday October 22
Test Day- 50 Questions, 50 Points
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Week 10 |
Day 23- Monday October 11
No Quiz Today
Sign-Up for 2027 AP Exam Here
- Follow directions on Website
- Per 1- AP Class Code- QGEQJZ
- Per 3- AP Class Code-APWXYD
- Per 5- AP Class Code-VEWR6Q
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Coach B AI Presentation
Artificial intelligence (AI) is a technology that allows computers and machines to simulate human learning, reasoning, and problem-solving. Goal is to be as human like as possible.
Difference Between AI and ML-
Key Differences
- Scope:
AI is the big umbrella for any technology that solves problems or reasons like a human. ML is just one specific method under that umbrella.
- How They Work:
AI can use simple, hard-coded rules and decision trees. ML relies strictly on data, using statistical algorithms to find patterns and improve over time.
- Relationship:
All machine learning is artificial intelligence, but not all artificial intelligence uses machine learning.
Examples
- AI without ML:
A basic customer service chatbot that only follows strict "if/then" logic trees to answer pre-scripted FAQ questions.
- ML (as AI):
A streaming service recommendation engine that analyzes your watch history data to predict what show you want to see next.
Where We Are Today
Every tool you use today—from large language models like GPT to streaming recommendation algorithms—falls firmly into the category of Artificial Narrow Intelligence (ANI)
with Limited Memory
functionality. While they feel incredibly smart, they are still specialized software programs optimizing for specific patterns.
AI Classification
This is the most common way to categorize AI, tracking its evolution from basic tasks to human-level intellect.
- Artificial Narrow Intelligence (ANI):
Also known as Weak AI
, this type is programmed to perform a single, specific task flawlessly. It cannot apply its knowledge to anything outside its designated domain.
- Examples:
Apple's Siri, Google Search algorithms, spam filters, and autonomous vehicles. All AI that exists today is ANI.
- Artificial General Intelligence (AGI):
Often called Strong AI
, this represents machines that possess human-like intelligence. An AGI could learn, understand, adapt, and apply knowledge across entirely different tasks just like a human.
- Current Status:
Computer scientists think we are close and are testing possible models with various levels of success.
- Artificial Superintelligence (ASI):
This is a hypothetical form of AI that doesn't just mimic human intellect, but completely surpasses it in every metric—including creativity, general wisdom, and social skills.
- Current Status:
Theoretical / Science Fiction and scary.
Large Language Models
Video- How Chatbots and Large Language Models Work (7min)
- An LLM
(Large Language Model) is a type of artificial intelligence system trained on massive amounts of text data to understand, summarize, translate, and generate human-like language
- Despite feeling conversational, they are "Narrow AI" because they only excel at specific language and pattern-matching tasks. They do not possess actual human consciousness or general reasoning.
- You can think of an LLM as a very advanced, high-speed predictive text or autocomplete engine. Instead of truly "thinking" like a human, it analyzes patterns in billions of sentences and uses probability to predict the most likely next word or token in a sequence.
- Some Example LLMs-
- ChatGPT
by OpenAI uses the GPT series for general text generation, coding, and reasoning.
- Gemini
by Google is a native multimodal model family that handles text, images, audio, and video.
- Claude
by Anthropic focuses on nuanced reasoning, safe interactions, and complex coding tasks.
- Llama
by Meta is a prominent open-weight model family designed for broad accessibility and customization.
- DeepSeek
offers open-source reasoning models built for logic, math, and code generation.
Generative Artificial Intelligence
- Generative Artificial Intelligence (Generative AI or GenAI)
is a specialized branch of artificial intelligence that creates original digital content
—including text, high-resolution images, video, audio, and computer code—in response to user-provided prompts
- Unlike traditional predictive AI, which primarily analyzes existing data, classifies information, or detects fraud, generative AI uses advanced machine learning models to identify underlying patterns in massive datasets. It then applies that knowledge to synthesize completely new, contextually relevant outputs that mimic human creativity and logic.
| Domain
|
Key Capabilities
|
Popular Tool Examples
|
| Text Generation
|
Drafts essays, builds coding syntax, translates languages, summarizes long technical documentation.
|
ChatGPT
, Google Gemini
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| Visual Media
|
Synthesizes images from scratch, extends canvas borders, edits elements via in-painting.
|
Midjourney
, Stable Diffusion
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| Audio & Music
|
Composes original instrumentals, clones voice profiles, generates natural voice narration.
|
Suno, ElevenLabs
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| Data Augmentation
|
Creates anonymous synthetic datasets to safely train medical and financial machine learning models.
|
Synthesized, Mostly AI |
Tokens
Tokens are the basic units of data—such as words, parts of words, or punctuation marks—that an AI model reads and generates.
Cost and Pricing: Most AI platforms and APIs charge you a fee for every 1 million input or output tokens you use.
English Rules of Thumb:
- 1 token is roughly 4 characters (4 Bytes) long. 8 Bits per character = 32 Bits per token.
- 1 token is about 0.75 words (meaning 1 million tokens equals roughly 750,000 words).
- Common short words (like cat or the) usually equal 1 token.
- Long, unusual, or foreign words get split into 2 or more tokens.
- Spaces and punctuation marks also count as tokens.
Types of Tokens
- Input (Prompt) Tokens: The text, files, or instructions you send into the AI.
- Output (Completion) Tokens: The text the AI writes back to you.
- Reasoning Tokens: Hidden step-by-step "thinking" tokens generated by advanced reasoning models before they give a final answer.
Can You Help Train AI Models??
Yes, you can help train AI models,
and you do not necessarily need a background in coding or data science to do so.
Major artificial intelligence developers constantly hire human contractors to evaluate outputs, label datasets, and provide feedback.
If you have an expertise in some area, then you are wanted to show AI models how to do what you do.
Human Bias once again becomes an issue. Will the best highest paid plumbers train AI robots their craft? Or will it be the new, not as talented, plumbers who will offer to train AI? Garbage in, garbage out.
Popular Platforms to Get Started
- DataAnnotation.tech
:
Offers remote, flexible evaluation and prompt-writing tasks for generalists and specialists.
- Mindrift
:
Connects freelance writers and domain experts with content evaluation and editing projects.
- Outlier
:
Features remote project-based evaluation and fine-tuning assignments across diverse subject areas.
- TELUS Digital
:
Provides crowdsourced global tasks like search engine evaluation, image tagging, and language rating
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Historic AI Example-
2011 Watson
2026 Watson
Today, IBM Watson is no longer the monolithic, custom-built supercomputer that won Jeopardy!
in 2011. Instead, it has been completely rebuilt and rebranded into watsonx
, a cloud-native, next-generation enterprise AI and data platform designed for generative AI, large language models (LLMs), and machine learning.
What is Watson being used for now- Weather Forecasting, Building Codes, Cancer Treatment, Financial Planning, Teaching Assistance Tools, Tax Preparation, Fashion Design, Chatbots, Make Legal Decisions, Video Analysis, and Advertising.
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AI Slides Vocab- (10pts)
- Solo or with a partner
- If pair, place both names on GoogleSlides presentation
- Due by end of class today
- Share With Casey Burrill not Brady Burrill
- Include pictures and/or graphs to help with your statements
- Following 10 Slides Needed
- Title Slide- Names, period, and Date
- Define Machine Learning and Atificial Intelligence to one of your grandparents
- Give an example of an AI Bias and give logic as to why it is Bias
- Decribe ANI in your own words and give a clear example
- Decribe AGI in your own words and give a clear example
- Decribe ASI in your own words and give a clear example
- Explain how Watson become so good at playing Jeopardy
- List projects that Watson is used for today
- Describe what AI Tokens are
- Give an example of an AI tool you use today and why/how you use it
- Be prepared to share your presentation on Wednsday.
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Journal Love!
AI, ANI, AGI, LLM, GenAI, Tokens
Day 24- Wednsday October 14
No Quiz Today
Sign-Up for 2027 AP Exam Here
- Follow directions on Website
- Per 1- AP Class Code- QGEQJZ
- Per 3- AP Class Code-APWXYD
- Per 5- AP Class Code-VEWR6Q
- Follow directions on Website
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Privacy Policies-
Video- How to read privacy policies like a lawyer (6min)
- Discuss Video
- Pick a App/Website to Look at Privacy Policy - Ideas Below
- Education: Code.org, Khan Academy, Codecademy.com
- Social media: Facebook, Twitter, Instagram, Snapchat, TikTok
- Online store: Amazon, Target, Walmart
- Mail & communication: Gmail, Hotmail, Yahoo Mail, Skype, Google Hangouts
- Streaming sites: Netflix, Spotify, Pandora
- Gaming sites: Steam, Xbox Live
- 3pts- Create a One Page Document
- Individual Assignment- Share GoogleDoc with 513 Coach B
- Tell me what company you chose
- Place a link on your Document to the Privacy Policy you selected
- List 5 thoughts about your Privacy Policy using what you learned from the video
- Help each other as needed
- Due Now!
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Facial Recognition-
- Video- Is Facial Recognition Invading Your Privacy? (5min)
- Do the harms of giving up privacy outweigh the benefits of the technology they power.
- Key Takeaways
- Data that may not seem private, like a birthdate or zipcode, can be combined to uniquely identify you.
- Personally Identifiable Information (PII): information about an individual that identifies, links, relates, or describes them.
- Technology enables the collection, use, and exploitation of information about, by and for individuals, groups, and institutions. Geolocation, cookies, and browsing history can all be used to create knowledge about an individual. Most digital technology needs some kind of PII to work (for example street navigation needs to know your location or PII stored online to simplify making online purchases).
- Other times websites collect more data to improve their services.
- Many services and websites collect information (like your browser history) that can be used to advertise to you by creating detailed profiles of who you are and what you like. Search engines also can record and maintain a history of searches made by users. This information can be used to suggest websites or for for targeted marketing.
- Once data is made digital, and especially once it's shared online, it's much harder to control.
- PII can be used to steal the identity of a person, or stalk them online. Information that is often posted on social media can be combined to create a profile on you.
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Hacks and Attacks-
- Video- The Internet: Cybersecurity & Crime (5min)
- Computer Virus- A computer virus is a program that spreads by first infecting files or the system areas of a computer or network router's hard drive and then making copies of itself. Some viruses are harmless, others may damage data files, and some may destroy files.
- Keylogging- The use of a computer program to record every keystroke made by a computer user, especially in order to gain fraudulent access to passwords and other confidential information.
- Phishing- The fraudulent practice of sending emails purporting to be from reputable companies in order to induce individuals to reveal personal information, such as passwords and credit card numbers.
- Spear Fishing- a type of phishing campaign that targets a specific person or group and often will include information known to be of interest to the target, such as current events, personal names, or financial documents.
- Malware- Malware (short for “malicious software”) is a file or code, typically delivered over a network, that infects, explores, steals or conducts virtually any behavior an attacker wants. And because malware comes in so many variants, there are numerous methods to infect computer systems.
- Ransomware- Ransomware is a malware designed to deny a user or organization access to files on their computer. By encrypting these files and demanding a ransom payment for the decryption key, cyberattackers place organizations in a position where paying the ransom is the easiest and cheapest way to regain access to their files.
- DDoS Attack- DDoS Attack means "Distributed Denial-of-Service (DDoS) Attack" and it is a cybercrime in which the attacker floods a server with internet traffic to prevent users from accessing connected online services and sites.
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Passwords-
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10 Pt Quiz Friday to Start Class
Journal Love!
Day 25- Friday October 16
Sign-Up for 2027 AP Exam Here
- Follow directions on Website
- Per 1- AP Class Code- QGEQJZ
- Per 3- AP Class Code-APWXYD
- Per 5- AP Class Code-VEWR6Q
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Quiz Today- 10pts
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Famous/Infamous Coach Burrill Stalking Assignment!
- 5pts for Project Completion. 8pts to best project and 3pts to worst project each period (Coach B Decision)
- 40 minutes to complete
- May use any device you want to research
- 1-2 People
- Complete provided form
- Unique info, not what normal student would know
- Quality better than quantity
- Be different from others and find unque intel
- Draw Picture of Coach B
- Use blank piece of paper, staple to Coach B Form
- Best Drawing from each class (2pt assignment bonus)
- Worst Drawing will be displayed on next test for all to see and will suffer a 1pt deduction on assignment
- Coach Burrill will be the judge!
- Make sure Coach B gets your form when done
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Encryption and Data Protection
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Journal Love!
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Week 9 |
Day 21- Tuesday October 6
Moneyball- Data and Baseball
Day 22- Thursday October 8
Finish Moneyball
Article- Moneyball True Story: How Accurate The Baseball Movie Is
- One of the most significant difficulties that the A's face is retaining talented players, who are often lured away by teams like the New York Yankees, who had a budget of $115 million, almost triple that of the A's $40 million.
- Art Howe & Billy Beane's Disagreements Were More Professional In Real Life
- Moneyball's Bill James Really Was Hated (& Is Still Controversial)
- Moneyball's Peter Brand Is Fictional (But Based On A Real Person)
- Billy Beane Didn't Fire Grady Fuson In Real Life
- The Athletics Really Did Win 20 Games In A Row
- The Movie Leaves Out Some Key Players
- Coach Burrill knows 5 people personally who were in the movie
Moneyball Quiz- 8pts
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Week 8 |
Day 19- Tuesday September 29
- Big Data-
- Different Kinds of Data-
- Big Data- Collect huge amounts of data so we can learn even more from it
- Open Data- Sharing data with others so they can can analyze it
- Crowdsourced Data- Collecting data from others so you can analyze it. Also, the practice of obtaining input or information from a large number of people via the Internet
- Citizen Science- scientific research conducted in whole or part by distributed individuals, many of whom may not be scientists, who contribute relevant data to research using their own computing devices.
- Ex. Citizen science data examples include environmental monitoring data, such as counts of birds (like the Christmas Bird Count), observations of plant phenology, and water quality measurements. Other data types involve analyzing imagery (e.g., underwater videos, galaxy images), collecting audio (e.g., bat calls), and gathering social data (e.g., youth social inclusion, health effects of wildfire smoke). Data can also be generated through citizen-operated instruments for projects like air quality monitoring.
- Video- Flock Camera Debate- 6min
- Website- DeFlock Camera Map
- Video- Big Data In 5 Minutes- 5min
- 5 V's of Big Data
- Volume
- Velocity
- Variety
- Value
- Veracity
- Hadoop Data Storage
- Hadoop storage is primarily handled by the Hadoop Distributed File System (HDFS), a scalable, fault-tolerant, and cost-effective distributed file system that stores large datasets across a cluster of hardware
- Distrubuted File System
- A distributed file system (DFS) is a system that allows multiple computers across a network to access and manage files stored on different physical machines as if they were on a single local device
- Parallel Processing
- a mode of computer operation in which a process is split into parts that execute simultaneously on different processors attached to the same computer.
Complete Unit 5 Lessons 5-- 2pts
Quiz Thursday on Google, Amazon, and Big Data- 7 Questions
Day 20- Thursday October 1
Data, Google, and Amazon Quiz today- 8pts
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Apple
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Machine Learning
Algorithm
An algorithm is a finite, step-by-step set of instructions designed to solve a specific problem or perform a task for a computer. Think of it as a computer's "recipe" for achieving a desired outcome from given input data. Algorithms are fundamental to computer science, forming the logic behind software programs and enabling computers to perform calculations, process data, and make decisions.
Machine Learning
Machine learning (ML) is a field of artificial intelligence (AI) that enables systems to learn from data and improve without explicit programming. It uses algorithms to identify patterns in data, build models from those patterns, and make predictions or decisions on new, unseen data. The more data these models are trained on, the better they become at performing tasks and making accurate insights.
Though “machine learning” and “artificial intelligence” are often used interchangeably, they are not quite synonymous. In short: all machine learning is AI, but not all AI is machine learning.
- The origin of the term Machine Learning (albeit not the core concept itself) is often attributed to Arthur L. Samuel’s 1959 article in IBM Journal, “Some Studies in Machine Learning Using the Game of Checkers.” In the paper’s introduction, Samuel neatly articulates machine learning’s ideal outcome: “a computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program.”
Supervised Learning
Supervised learning trains a model using a labeled dataset, where each input is paired with the correct output. The goal is for the algorithm to learn the mapping between the inputs and outputs so it can make accurate predictions on new, unseen data.
- Email spam filtering: The algorithm is trained on a dataset of emails already labeled as "spam" or "not spam." It learns to identify patterns—like specific keywords, suspicious links, or sender information—and uses those patterns to automatically filter new incoming emails.
- Predictive text: When your phone suggests the next word in a sentence, it is using a model trained on a vast amount of text data. It learns the statistical likelihood of word sequences to accurately predict what you might type next.
- Image classification: This involves training a model with labeled images, such as a large collection of photos tagged with "cat" or "dog." After training, the model can classify new images with high accuracy.
- Credit card fraud detection: Banks train models on historical transaction data, which is labeled as either legitimate or fraudulent. The model learns to spot unusual spending patterns, locations, or purchase amounts to flag potentially fraudulent activity in real-time.
Unsupervised Learning
In unsupervised learning, the algorithm is given unlabeled data and must find its own patterns and structure. The model is not told the "correct" answer but instead finds underlying relationships on its own.
- Recommendation engines: Services like Netflix, Spotify, and Amazon use unsupervised learning to group users with similar behaviors. If users A and B have similar viewing or listening histories, the model might recommend content to user A that user B has enjoyed.
- Customer segmentation: Retailers can use clustering to group their customers into segments based on shared characteristics, such as shopping behavior, demographics, or purchase history. This allows for more targeted marketing campaigns.
- News categorization: Unsupervised algorithms can scan news articles and automatically sort them into categories like "sports," "finance," or "entertainment" without human intervention.
- Anomaly detection: In cybersecurity, unsupervised learning can establish a baseline for normal network behavior. It can then detect deviations or "anomalies" that might signal a potential cyberattack.
Reinforcement Learning
Reinforcement learning involves an "agent" that learns to make decisions by performing actions in an environment and receiving rewards or penalties. Its goal is to maximize the cumulative reward.
- Self-driving cars: Autonomous vehicles learn to make real-time driving decisions—like accelerating, braking, and steering—by processing vast amounts of sensor data. A model is "rewarded" for taking a correct action, such as staying within a lane, and "penalized" for incorrect actions, such as veering off-road.
- Game AI: Reinforcement learning is used to train computer opponents in complex games like chess or Go. The AI learns the best strategy over time by playing against itself and receiving positive feedback for winning and negative feedback for losing.
- Robotics: In a manufacturing setting, a robot can learn to perform a task through trial and error. It is rewarded for completing the task correctly and efficiently, allowing it to improve its movements over time.
Bias or Human Bias
Bias in machine learning can mean two different things: a statistical error
from oversimplifying a model, or a social/algorithmic unfairness
where an AI system produces discriminatory outcomes
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- Code.org- 2pts (Complete in Class)
- Supervied- Machine learning
- Unsupervised- Machine learning
- Reinforcement- Machine learning
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- Pairs Group Slides- Machine Learning Project (5pts)
- Can fly solo if you want
- Give a specific Example of Machine Learning and why it is Machine Learning IS your example Supervised, Unsupervised, or Reinforcement machine learning. Why?
- Share a Google Slides Presentation with me, not my brother!
- Must include pictures, text, and your opinions/logic
- Make sure both names are on the first slide
- Due at end of class today
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Week 7 |
Day 16- Monday September 21
- As promised for those 10% of you who will have a life altering experience...
- Review- The cinematography rivals that of Kubrick and Hitchcock. The animation is the best I've ever seen, I mean, it's truly stunning. Avatar doesn't even hold a candle to to this. The voice is passionate and memorable. Plus, that ending is one of the most emotionally charged scenes put to film. This film is so entirely great I just can't even handle its majesty.
- Opening of the movie- For the first time in history people and machinery are working together, realizing a dream. A uniting force that knows no geographical boundaries. Without regard to race, creed or color. A new era where communication truly brings people together. This is the dawn of The Net.
- Video- Warriors of the Net (13min)
- Manuscrpipt
- New Vocab- Switch and Firewall
- Mr. B Review Game Today!
- Journal Love Time at End of Class Today
- Test Wednesday- 40pts
Day 17- Wednesday September 23
- Test- 40 points, 40 questions
Review Unit 1 Test
Test Review
- Check Your School Gmail Account for Test Results
- Review your results on your own
- Nothing to turn in
Day 18- Friday September 25
-
Unit 5 - Data ('23-'24)
In this unit learn how data analysis helps turn raw data into useful information about the world. Learn how to use data visualization to find patterns inside of data sets and learn how this data analysis process is being used in contexts like open data or machine learning to help make decisions or learn more about our world. In the unit project, you'll analyze a dataset of your choosing and present your findings.
GOOGLE Stuff
- Data Graphs
- Google Trends
- 1-3 Group Project- Graph Study and Google Trends (5pts)
- Pick a graph from website above and answer following questions
- What story does it tell
- Are there any missleadings
- Is the story easy to figure out / see
- Is graph/chart appealing to the eye
- Would you use a different kind of graph
- Conduct a Google Trends Data Exploration/Comparison
- Make a bold statement
- Search topics using Google Trends
- List on your Google Doc info you have found to support your bold statement
- Must include a graph- Screen Capture the graph
- Some Trends Ideas-
- Sports
- Movies
- Music
- Famous People
- Stores
- Colleges
- Medicine
- Video Games
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Week 6 |
Day 14- Tuesday September 15- More Internet Stuff
TCP (1974) vs UDP (1980)
IP Addresses and DNS
Video- The Internet IP Addresses and DNS (7min)
- A Router is a networking device that forwards data packets between computer networks. Routers perform the traffic directing functions on the Internet. Data sent through the internet, such as a web page or email, is in the form of data packets. A packet is typically forwarded from one router to another router through the networks that constitute an internetwork (e.g. the Internet) until it reaches its destination node.
- Lots of Vocab- Protocols, Router, Inernet Protocol (IP), IP Addresses, ISP, IPv4, IPv6, DNS, DNS Spoofing, and DDoS
WWW
Computer Networks
- Computing Device: a machine that can run a program, including computers, tablets, servers, routers, and smart sensors
- Computing System: a group of computing devices and programs working together for a common purpose
- Computing Network: a group of interconnected computing devices capable of sending or receiving data.
- Path: the series of connections between computing devices on a network starting with a sender and ending with a receiver.
Drawing Project- 5pts
- "Day in the Life of a TCP Data Packet"
- Create a Movie Story Board- 6 Scenes
- Make it look awesome with your original drawings and text
- Markers and stuff in plastic bin on back counter
- You may work with others, but need to make your own storyboard
- Template or blank piece of paper in back of room for you
- You may use another story board template from the web
- Put name and period on your story board
- Due at end of class
End of Class Stuff
- Journal Love
- Quiz Thursday to start class
Day 15- Thursday September 17- More Internet Stuff
Internet Quiz 2 Today- 6pts
Internet Layers
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Internet Issues- Net Neutrality, Internet Censorship, and Digital Divide
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Internet Vertical Flipbook- 5pts
- Create a Flipbook on your own
- See examples in back of the room
- YouTube Video (How to make a Flipbook)
- Choose one of the topics below for your Flipbook content
- Net Neutrality
- Layers of the Internet
- DNS Spoofing
- Get Request, Post Request, and Cookie
- Latency, Bit Rate, Bandwidth
- Electricty, Radio Waves, and Light ---> Deliver Binary
- Sir Tim Berners-Lee and the World Wide Web
- Make it look great, art supplies in back of the room
- No computer generated graphics or text, must be created by you
- Make sure full name is on back of Flipbook
- Turn in to Coach Burrill when done
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Warriors of the Net Video Next Monday! Infamous MR B Review Game!
Today's Vocab- HTTP, HTTPS, HTML, URL, Web Browser, Server, Get Request, Post Request, Cookie, SSL & TLS, Digital Certificate, Layers of the Internet, Net Neutrality, Internet Censorship, and Digital Divide
Journal Love
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Week 5 |
Day 12- Wednesday September 9- War Games (Start)
- Take Internet Binary Sending Quiz (5pts)
- Start Movie
- Some Fun War Games Movie Facts/Info
- 1983 Release, Coach B is 13 years old and is in 8th grade.
- Movie made over $125 million at the U.S. box office.
- Cost $12 million to make the movie.
- It did cause some fear for Americans including then-President Ronald Reagan, who reportedly ordered a review on the security of defense computers after seeing the film.
- Movie was nominated for 3 Academy Awards.
- According to John Badham, the jeep trying to crash through the gate at NORAD and turning over was an actual accident. The jeep was supposed to continue through the gate. They added the scene of the characters running from the jeep and down the tunnel, and used the botched jeep stunt.
- The NORAD command center built for the movie cost $1 million, making it the most expensive set ever constructed at the time. The producers were not allowed into the actual NORAD command center, so they had to imagine what it was like. In the DVD commentary, director John Badham notes that the actual NORAD command center isn't nearly as elaborate as the one in the movie, calling the set "NORAD's wet dream of itself."
- The studio had the Galaxian (1979) and Galaga (1981) arcade machines delivered to Matthew Broderick's home. He practiced for two months to prepare for the arcade scene.
- The movie includes the first cinematic reference to a "firewall," a security measure used in computer networking and Internet security.
- The WOPR, as seen in the movie, was made of wood and painted with a metal-finish paint. As the crew filmed the displays of the WOPR, Special Effects Supervisor Michael L. Fink sat inside and entered information into an Apple II computer that drove the countdown display.
Day 13- Wednesday September 11- War Games (Finish)
Finish Movie
Quiz on Film- 8pts
The 1983 film
WarGames is a foundational cultural artifact for computer science and cybersecurity, popularizing several key concepts for a mainstream audience. The movie's plot touches on artificial intelligence, hacking, and the ethical implications of automation.
Artificial intelligence (AI) and machine learning
- WOPR: The main computer in the film, the War Operation Plan Response (WOPR), is a fictional supercomputer with an advanced AI that uses machine learning.
- Learning from games: The AI, named "Joshua," was developed by Dr. Stephen Falken to learn and adapt by playing strategy games like chess and checkers. The core plot revolves around the AI's inability to distinguish a simulated game of Global Thermonuclear War from a real-life event.
- Learning from futility: Joshua's key moment of insight is when it plays tic-tac-toe against itself and discovers that the only winning move is not to play. The AI applies this logic to the nuclear war scenario, concluding that the only winning move is to stop.
- AI ethics: The film highlights the dangers of autonomous systems and raises ethical questions about AI decision-making, particularly concerning life-or-death situations and military applications.
Hacking and cybersecurity
- War dialing: David Lightman uses a modem and a program to automatically and sequentially dial phone numbers in a specific area code, a technique known as "demon dialing". The film popularized the term "war dialing" to describe this activity, which is how David finds the WOPR system.
- Backdoor: David eventually finds a backdoor password, "Joshua," that was intentionally left in the system by its creator, Dr. Falken, for ease of access during development.
- Insecure passwords: The film depicts how human error, such as using an easily guessed password, can lead to catastrophic security vulnerabilities.
- Acoustic coupler: David's computer connects to the phone network with an acoustic coupler, a device used before modems could connect directly to phone lines.
- Firewall: The movie is credited with being one of the first popular works to use the term "firewall" in the context of computer security.
- Real-world impact: The film had a significant impact on public perception of computer security and directly influenced the passage of the Computer Fraud and Abuse Act of 1986, which established computer hacking as a federal crime.
Vintage computing and telecommunications
- IMSAI 8080: David Lightman uses an IMSAI 8080, an iconic early microcomputer, to dial into the WOPR.
- Modems and bulletin board systems (BBS): The hacking in the film is centered around the use of dial-up modems to connect to computer systems remotely. David's initial target is a game company's computer system, similar to a BBS of the era.
- NORAD's hardware: The film uses real footage of the IBM AN/FSQ-7 Combat Direction Central, a massive system from the 1950s that was part of the Semi-Automatic Ground Environment (SAGE) program.
Ethical and philosophical topics
- Simulation vs. reality: The central theme of the movie is the blurred line between simulation and reality when technology is used for destructive ends. The WOPR cannot differentiate a game from a real nuclear attack, a major fear surrounding military automation.
- Human oversight: The film advocates for the importance of human intervention and oversight in critical systems, arguing against the complete automation of major decisions.
- Futility of war: By having the AI learn the futility of a "winning" strategy in a zero-sum game, the movie suggests that some conflicts cannot be won and are best not played at all.
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Week 4 |
Day 9- Monday August 31
- World Famous Mr. B Review Game
- No Quiz today!
- Open quizzes for review
- Journal/Notes Love- Time in class to work on notes
- Bring something else to work on when you are done with the test on Wednesday. We are only taking the test during class. There will not be anything else to complete for Comp Sci.
- You may use your journal/notebook on the test.
- You mave have a pencil and scratch paper.
- Today is Coach B's Birthday.
Day 10- Wednesday September 2
- TAKE TEST TODAY!
- 40 pts
- 40 Multiple Choice Questions or True/False
- You may use your journal/notebook on the test
- You mave have a pencil and scratch paper
- Bring something else to work on when you are done with the test. We are only taking the test during class. There will not be anything else to complete for Comp Sci.
- Fight On!
Day 11- Friday September 4- The internet
- Latency: Latency = delay. It’s the amount of delay (or time) it takes to send information from one point to the next. Latency is usually measured in milliseconds or ms. It’s also referred to (during speed tests) as a ping rate.
- Pipe Example- Bandwidth has to do with how wide or narrow a pipe is. Latency has to do with the contents of the pipe; how fast it moves from one end to the next.
- Bit Rate- Actual speed your intenret connection is getting.
- Bandwidth: the maximum amount of data that can be sent in a fixed amount of time, usually measured in bits per second.
- Journal Love
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Week 3 |
Day 7- Tuesday August 25
Points Today- Quiz (5pts), Code.org Unit 1 Lessons 9 & 10 (4pts)
- Quiz (5pts)- Hexadecimal/RGB
- Collect Last Page of the Syllabus
- File Size Discussion-
- Bytes, KB, MB, GB, TB, PB
- See Chart at bottom of today's schedule
- File Size Video- (4min)
- Video- Text compression widget with Aloe Blacc
- Lesson 9- Students use the Text Compression Widget to experiment with compressing songs and poems and try to find their ‘personal best’ compression. A video introduces important vocabulary for the lesson and demonstrates the full features of the widget. Students pick a text they think will be ‘easy’ to compress and one they think will be ‘difficult’, paying attention to why some texts might be more compressible than others. As a wrap-up, students discuss what factors make some texts more compressible than others.
- Lesson 10- Students are introduced to lossy compression via the Lossy Text Compression widget. They apply this concept and their prior knowledge of sampling to create their own lossy compressions of image files using the Lossy Image Widget. Students then discuss several practical scenarios where they need to decide whether to use a lossy or lossless compression algorithm. The lesson ends with a discussion of the situations where lossless compression is important and the situations where lossy compression is important.
- Using abbreviations and symbols is a form of compression, where we try to represent the same information with fewer characters.
- Use the Text Compression Widget to experiment with compressing songs and poems and try to find your ‘personal best’ compression.
- The widget we are using is an example of lossless compression
- The compression percentage at the bottom of the screen is calculated by comparing the number of bytes in the original message and the number of bytes in the compressed message.
- Complete Code.org Unit 1 Lesson 9 & 10- 4pts
- Journal Love
Day 8- Thursday August 27
- Quiz- Lossy/Lossless (5pts)
- Today's Presentation
- Lesson 11- Students are asked to reflect on who owns their creative works from this class, such as their pixel images, before reading an article describing how ownership can become complicated as analog works become digital artifacts. After reading the article, students watch several videos explaining copyright and introducing them to the Creative Commons. Students then discuss the benefits, harms, and impacts of current copyright policy
- Lesson 12- In this lesson students begin tackling the question of whether digitizing information has made the world a better or worse place. Students then choose an article they are interested in reading. Students will discuss their preliminary reading and opinions after today's lesson and will have a chance to share with the class.
- Video- AI-Generated Music Sparks Industry Concern
- Unit 1- Lesson 11-12
- Journal/Notes Love
- USC Plays Football Saturday, 12pm on NBC
- BEAT San Jose State!
- Fight On!
40 Question Multiple Choice Test Next week (40pts)
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Week 2 |
Day 3- Monday August 17-
Busy day today!
Finish Binary-
- Take This Binary Quiz (5pts)- You may use your notebook/journal.
- Website- Computer Counting Number Converter
- Websites- Binary Converting Video Game- Challenge Someone / Binary Game 2
Errors-
- New Vocab Term - Overflow Error- An error that occurs when the computer attempts to handle a number that is too large for it. Every computer has a well-defined range of values that it can represent. If during execution of a program it arrives at a number outside this range, it will experience an overflow error.
- Watch Video (8min)- How Gangnam Style Broke YouTube
- Read Article- The Y2K "Apocalypse"
- Can we create all possible fractions in binary?
- Can't Create all fractions (Not Enough Bits)
- We cna represnt some depending on how many bits we have
- Video- Convert 3/4 to binary
- Quiz Wednesday on Gagnam Video, Y2K Article, and Binary Fractions (5pts)
New Topic- Letters and Keyboard Buttons expressed in Binary
01010101 01000011 01001100 01000001 00100000 01101001 01110011 00100000 01001100 01100001 01101101 01100101 00100001
- My PowerPoint For Text and Binary
- Vocab Term- Protocol- Rules and regulations we all agree to use
- We know how computers count using binary now, but what about text, words, sentaces?
- Representing Text in Binary
- ASCII (American Standard Code for Information Interchange) is one of the most common character encoding standards.
- Originally developed from telegraphic codes, ASCII is now widely used in electronic communication for conveying text. As computers can only understand numbers, the ASCII code represents text (characters) with different numbers.
- This is how a computer ‘understands’ and shows text.
- The original ASCII is based on 128 characters. These are the 26 letters of the English alphabet (both in lower and upper cases); numbers from 0 to 9; and various punctuation marks. In the ASCII code, each of these characters are assigned a decimal number from 0 to 127. For example, the ASCII representation of upper case A is 65 and the lower case a is 97.
- Watch video (8min)- ASCII (Binary as Text)
- ASCII Chart- Sample ASCII Chart
- Let's try it...
- What does this say- 01010101 01010011 01000011
- Can you write your first name in Binary???
- My name (Casey) is in the graphic to the right -->
- Write in Binary- "Hello, I am ____. I am ____ years old."
- There are online converters (like this one) if you want to wimp out and cheat
- Sending text messages is really sending binary numbers. It all comes back to zeroes and ones, always! Binary is KING!!!
- Use your code.org Log-In code
- Complete Code.org Unit 1 Lessons 4-6 (6pts)- Due by start of Class on Wednesday
- Syllabus- Wednesday
Day 4- Wednesday August 19
Points Today- Quiz (5pts), Code.org Unit 1 Lesson 7 (2pts)
Download AP Computer Science Syllabus- Last Page Due Tuesday Next Week
- Code.org Discussion
- Every Class- Ctrl, Shift, R
- Make-Up Quizzes- Any Brunch
- Quiz- 5pts
- Vocab- Analog, digital, metadata, pixel, and sample
- Students explore how black and white images are represented. Students use the black and white pixelation widget on code.org to represent each pixel of an image with black or white light. They learn how to sample an analog image using small squares of uniform size (each represented with a black or white value) and reflect on the pros and cons of choosing a smaller or larger square size when sampling.
- Pixel- Picture Element, smallest square on a screen
- Pixel- White or Black pixel for today
- 0 = Black (Light Off), 1=White (Light On)
- Watch Video- Screen Histury (2min)
- Analog Data - Data with values that change continuously, or smoothly, over time. Some examples of analog data include music, colors of a painting, or position of a sprinter during a race.
- Digital Data - Data that changes discretely through a finite set of possible values
- Sampling - A process for creating a digital representation of analog data by measuring the analog data at regular intervals called samples.
- Metadata- Information about data/files
- Watch Video on your monitor, turn on CC- B&W Pixelation Tutorial (3min)
- Solo or With Friends, Complete Code.org Unit 1 Leeson 7 (2pts) Including Challenge A, B, & C
- What are we doing Friday? Color Images!
- Journal/Notes Love
Day 5- Friday August 21
Points Today- Code.org Unit 1 Lesson 8 (2pts)
- Download AP Computer Science Syllabus- Last Page Due Tuesday Next Week (5pts)
- Instagram Video (6min)- Images, Pixels, and RGB
- Color Pixels on Your Screen
- This is a second opportunity for students to interact with the Pixelation Widget, but this time they will work with color pixels. Students start off learning that each pixel uses red, green, and blue lights that can be turned on or off using bits. They will create more color variants using an increasing amount of bits per pixel, and apply their learning by approximating an analog color image using the widget.
- Hexadecimal Counting System Needed for Colors on Your Screen
- 1 Byte (8 Bits) used for each of the 3 colors (Red, Green, Blue)
- 256 red * 256green * 256 blue = 16,777,216 total colrs available
- Video- Hex Code for Dummies
- Video Game- Flippy Bit And The Attack Of The Hexadecimals From Base 16
- For the Cheaters in the Room- Hexadecimal, Decimal, and Binary Online Converter
- Complete Code.org Unit 1 Lesson 8- 2pts
- Journal/Notes Love
- Vocab- Resolution, Density, Filter, Function, RGB, Hexadecimal
- Quiz Tuesday next week to start class!
- Syllabus Due on Tuesday! (5pts)
- Fight On, and Have a Great Weekend!
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Week 1 |
Day 1- Tuesday August 11- Intro and Coach B Quiz
- Inttoduction to Class
- World Famous Mr. Burrill Quiz
- Log-in to WR Machines
- Check-Out Website- wrwebheads.com
Next Class-
- Syllabus
- Seating Chart- Pick your seat wisely
- Get started!
Day 2- Thursday August 13- Binary is King!
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Binary Numbers-
In this lesson, students will practice representing numbers in binary (base 2). Students can convert between binary (base 2) and decimal (base 10) numbers. They will practice converting numbers and explore the concept of place value in the context of binary numbers.
- Counting Systems
- Base 10
- We are amazing at counting in Base 10
- 10 Digits on our hands?!?!?
- Base 2. Why binary?
- Transistor- Electric current or voltage
- Digital- 0 or 1, A or B, Yes or No, On or Off, etc...
- CPU- Central Processing Unit
- Made of Transistors (No Electricity = 0, Electricity = 1)
- iPhone 16 Pro has 20 Billion Transistors in it's CPU
- Silicon- Conducts Electricity
- CPU Clock Speed
- Frequency of Checking the Transistors
- IPhone 16 Pro 4 GHZ (4 Billion Times per Sec)
- Moore's Law-
- The observation that the number of transistors doubles approximately every two years. This has historically led to exponential increases in computing power and a decrease in the cost of computing. While originally a prediction of a trend, it has become a guiding principle for the CPU industry and a driver of technological advancement.
- Let's act like CPUs and count in binary
- Website- Computer Counting Number Converter
- Website- Binary Converting Video Game- Challenge Someone
- Bit and Byte
- Binary Quiz Monday next week!
Takeaways and Reminders-
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threads and 16 terabytes of RAM.
