My Personal Study
I’ve been studying through MCQs from CollegeBoard. I’ve also been reviewing the team teach blogs from my classmates on the Big Ideas 3 and 5 topics that I need to know for the AP Exam. I ensure that I’ve covered all of the skills on CollegeBoard so that I know what to do if I encounter any question.
Big Idea 5 Summary
Beneficial and Harmful Effects
We examined how advancements in computing can lead to both helpful and harmful outcomes. Through case studies such as the impact of social media on emotional well-being and how it shapes communication, we gained insight into its dual nature. This also led us to consider the ethical responsibility that creators and engineers have when developing new technologies.
Digital Divide
We explored the unequal distribution of digital resources and connectivity across different communities. This lack of access has far-reaching effects, particularly in areas like education and job opportunities, which can deepen existing social and economic gaps. These discussions highlighted the importance of striving for digital inclusivity.
Computing Bias
Our discussions revealed how unfairness can be embedded in algorithms due to skewed data or biased development practices. We analyzed cases involving facial recognition systems and recruitment software, and considered strategies to minimize bias, such as using varied data sources and conducting rigorous fairness checks.
Crowdsourcing
We looked into how the internet enables large groups of individuals to contribute knowledge, ideas, or data to collective projects. Websites like Wikipedia and collaborative software communities served as examples of how collective effort can drive innovation, though we also noted the potential challenges, such as quality control and misinformation.
Legal and Ethical Concerns
We delved into the legal landscape surrounding computing, focusing on issues such as intellectual property rights and digital privacy laws. Ethical questions surrounding artificial intelligence, data handling, and informed user consent were also a key part of our discussions, prompting us to think critically about how to navigate complex moral scenarios in tech.
Safe Computing
We learned practical ways to protect ourselves in the digital space, such as creating secure passwords and enabling two-factor authentication. Alongside this, we discussed threats like deceptive emails, malicious software, and manipulation tactics, emphasizing the need for smart, cautious behavior online and the role of digital citizenship.
Big Idea 3 Summary
Binary Search Algorithms
We studied how binary search provides an efficient way to locate elements in a sorted collection by repeatedly dividing the list in half until the desired value is found. This approach was contrasted with linear search, especially in terms of performance—highlighting binary search’s logarithmic time complexity. We also worked on building the algorithm from scratch using conditionals and loops.
Lists and Filtering Algorithms
We focused on manipulating lists and applying filters to extract meaningful data. These exercises reinforced our understanding of selection and iteration, as we designed algorithms that could search through and handle data effectively. This foundational skill is essential for solving real-world computational problems.
Big-O Notation
We explored Big-O notation as a way to describe the efficiency of algorithms. Through examples like constant time (O(1)), linear (O(n)), and exponential (O(n!)), we learned how to compare algorithms based on how they scale with input size. These comparisons highlighted the importance of optimizing code to improve performance.
Random Algorithms
Our lessons covered how randomness plays a critical role in computing, especially in simulations, gaming mechanics, and randomized algorithms. We discussed the balance between unpredictability and fairness, and how randomness can be strategically used to solve complex problems or simulate real-life variability.
Simulations
We learned how computers can mimic real-world systems through simulations, such as modeling climate change or simulating traffic flow. These models allowed us to test different scenarios, though we also noted how assumptions and constraints can influence a simulation’s reliability and accuracy.
Undecidable Problems
We explored the concept that not every computational problem has a definitive algorithmic solution. The Halting Problem served as a key example of this limitation, helping us recognize the boundaries of what algorithms can achieve and why some questions remain unsolvable within computational theory.
Graphs and Heuristics
We studied how graphs represent relationships through nodes and edges, commonly used in navigation systems, search engines, and network design. We compared representations like adjacency matrices and adjacency lists. Additionally, we looked at heuristics—strategies that offer practical solutions when exact methods are too resource-intensive. Examples included the Nearest Neighbor method for the Traveling Salesman Problem and heuristic search techniques like Manhattan distance.
MCQ Scores
2021
2020
2018
How I will Continue to Study
I will be practicing more MCs on my own time, and especially review topics that I missed on each MC. One of the biggest topics was safe computing and cybersecurity, so I need to work on those. I will also be reviewing CPT wording.