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AP Computer Science Principles - Study Blog

Beneficial and Harmful Effects of Computing

Beneficial Effects

  • Communication: Global connections via messaging, video calls, social media.
  • Education: Access to online learning tools like Khan Academy, Coursera.
  • Healthcare: Telemedicine, health tracking apps, AI-assisted diagnostics.
  • Productivity: Automating repetitive tasks (e.g., scheduling, calculations).

Harmful Effects

  • Job Displacement: Automation replacing manual or repetitive jobs.
  • Privacy Issues: Data collection without informed user consent.
  • Mental Health: Overuse of social media linked to anxiety and addiction.
  • Environmental Impact: E-waste and high energy consumption from servers.

✏️ Things to Remember

  • Know who benefits and who might be harmed.
  • Be ready to describe intended vs unintended effects.
  • Relate the impact to specific groups or global outcomes.

Digital Divide

Definition

The gap between those with access to technology/internet and those without.

Causes

  • Income level
  • Geographic location
  • Age and education
  • Infrastructure access

Consequences

  • Unequal access to education, healthcare, and jobs.
  • Some populations are excluded from digital innovation.

Solutions

  • Public Wi-Fi and tech donations
  • Affordable devices
  • Digital literacy programs

✏️ Things to Remember

  • Connect to equity and opportunity.
  • Know how innovations can both help and worsen the divide.

Computing Bias

Definition

Unfair or unequal outcomes from algorithms caused by biased data.

Examples

  • Hiring software that favors certain names
  • Facial recognition less accurate for darker skin tones
  • Predictive policing algorithms targeting certain neighborhoods

Causes

  • Biased training data
  • Lack of diverse representation in developers
  • Historical inequities in the data

Solutions

  • Diverse datasets
  • Fairness checks and audits
  • Ethics in design processes

✏️ Things to Remember

  • Understand “bias” as unintended discrimination.
  • Be able to explain how to reduce bias in a system.

Crowdsourcing

Definition

Getting input or solving problems using contributions from a large online group.

Examples

  • Wikipedia
  • Waze
  • Duolingo
  • Foldit

Pros

  • Quick, cost-effective
  • Taps into collective intelligence

Cons

  • Accuracy and quality vary
  • Easy to get misinformation

✏️ Things to Remember

  • Know when and why crowdsourcing is useful.
  • Evaluate reliability of user-generated data.
  • Copyright: Reusing protected works without permission.
  • Hacking: Unauthorized access to systems or data.
  • Data Protection: Laws like GDPR to protect personal information.

Ethical Topics

  • Informed Consent: Users must know what data is collected.
  • Transparency: Systems should behave as users expect.
  • Societal Impact: Innovations should not harm vulnerable populations.

✏️ Things to Remember

  • Distinguish legal vs. ethical.
  • Know common ethical concerns around AI and data.

Safe Computing Practices

Best Practices

  • Use strong, unique passwords.
  • Enable Two-Factor Authentication (2FA).
  • Keep antivirus software and operating systems updated.
  • Be aware of phishing scams.
  • Limit personal information shared online.

✏️ Things to Remember

  • Recognize secure vs. risky online behavior.
  • Understand how to protect your data and identity.

Binary Search Algorithm

Purpose

Efficiently finds an item in a sorted list.

Steps

  1. Look at the middle of the list.
  2. If target < middle, search left half.
  3. If target > middle, search right half.

Efficiency

  • Time complexity: O(log n)
  • Only works on sorted data

✏️ Things to Remember

  • Must be sorted!
  • Compare with linear search (O(n)).
  • Know the steps and trace an example.

Lists and Filtering Algorithms

Lists

  • Store multiple values
  • Can loop over them using for loops

Filtering

  • Create a new list with items that meet a condition

```python scores = [92, 85, 70] high_scores = [score for score in scores if score > 80]

Output: [92, 85]

Common Uses

  • Data sorting
  • Filtering by condition
  • Removing duplicates

✏️ Things to Remember

  • Filtering is often part of managing complexity in your code.
  • Know how to build and explain filtered lists.

Simulation / Games / Random Algorithms

Simulation

  • Model real-world systems (e.g., weather, population, disease spread).
  • Useful when testing situations that are dangerous, costly, or time-consuming.

Random Algorithms

  • Introduce unpredictability in simulations and games.
  • Examples: Dice rolls, card shuffling, randomized decisions.

Monte Carlo Simulation

  • Repeats random trials to estimate outcomes or probabilities.

✏️ Things to Remember

  • Know why simulations are used: safety, speed, and scale.
  • Understand how randomness affects the results.

Big O & Algorithm Efficiency

Big O Description Example
O(1) Constant time Accessing an array element
O(log n) Logarithmic time Binary search
O(n) Linear time Looping through a list
O(n²) Quadratic time Nested loops (bubble sort)

✏️ Things to Remember

  • Helps compare speed and efficiency of algorithms, especially with large inputs.
  • More efficient algorithms grow slower as data size increases.
  • You don’t have to calculate Big O—just recognize common patterns.
Study Resources Study Tips

AP Computer Science Principles Study & Portfolio Plan

✅ Overview

Goal: Be fully prepared for the AP Exam and submit a strong Create Performance Task portfolio
Timeline: ~4 weeks (adjust as needed)

🗓️ Week-by-Week Plan

Week 1: Foundation + Explore Portfolio Submission

  • Review: Key terms (abstraction, algorithm, binary, simulation, etc.)
  • Study Topics:
    • Digital divide, computing bias, crowdsourcing
    • Big O notation and basic algorithms
  • Tasks:
    • Finalize and review Create PT video + written responses
    • Check rubric and format (screenshots, video, PDF)
    • Submit portfolio by April 28

Week 2: Algorithms + Programming Practice

  • Focus:
    • Practice binary search, filtering, and sorting algorithms
    • Write and explain pseudocode
  • Study:
    • Review simulations and random algorithms
    • Do 1–2 practice FRQs from previous years
  • Practice:
    • Rebuild parts of your Create Task project from scratch to reinforce skills
    • Review mistakes on collegeboard MCS

Week 3: Ethics, Data, and Security

  • Study:
    • Beneficial/harmful effects of computing
    • Safe computing, phishing, data privacy
    • Legal/ethical issues (e.g., copyright, consent)
  • Review:
    • Practice multiple-choice sets online
  • Resources:
    • Use AP Classroom, College Board FRQ bank, and quizzes

Week 4: Full Review + Practice Exam

  • Take a full-length practice exam
    • Review results and focus on weak areas
  • Study with flashcards (key terms & definitions)
  • Final review of:
    • How to write good FRQ responses
    • List algorithms, abstraction, and modularity

📌 Daily Study Structure (1–2 hours/day)

  • 30 min: Review notes/flashcards
  • 30 min: Practice coding or pseudocode
  • 30 min: Practice MCQs or FRQs
  • Optional: Join a study group or watch video walkthroughs