Media & Information Literacy Grade 7: Social Media Algorithms How Your Feed Is Chosen

Grade 7 Media & Information Literacy Study Guide Topic: Social Media Algorithms: How Your Feed is Chosen

1. The Driving Question

"Why do I keep seeing videos of my favorite soccer player but never posts from my aunt who lives across the country? And how does TikTok know I’ll laugh at that one meme before I even see it?" Social media platforms don’t just show you posts in order—they choose what you see based on invisible rules. If you’ve ever wondered why your feed feels like it’s reading your mind (or ignoring you), the answer is in the algorithms. By the end of this guide, you’ll be able to explain how these systems work—and why they sometimes get it wrong.

2. The Core Idea — Built, Not Listed

Imagine you’re at a school cafeteria with 100 tables, each serving a different kind of food. A robot waiter (let’s call it "Algo") follows you around with a tray, deciding which dishes to offer you next. It doesn’t ask what you’re in the mood for—instead, it watches what you actually do: - Do you linger at the pizza table? Algo notes that and brings you more pizza tomorrow. - Do you take one bite of the salad and walk away? Algo marks that as a "no" and stops offering salads. - Do you share your dessert with a friend? Algo sees that interaction and might bring their favorite foods to your tray too.

Social media algorithms work the same way. They’re not mind readers—they’re pattern detectors. Every like, share, pause, or scroll is a clue about what you might want to see next. But here’s the catch: Algo doesn’t care why you like pizza. It just knows you do. That’s why your feed can feel repetitive (more pizza!) or miss things you’d actually love (like your aunt’s posts—unless you always like them).

Key Vocabulary: - Algorithm: A set of step-by-step rules a computer follows to solve a problem or make a decision. Example: The "For You" page on TikTok is built by an algorithm that predicts which videos you’ll watch all the way through. - Engagement: Any action you take on a post—liking, commenting, sharing, or even just pausing to watch. Example: If you watch a 10-second video for 9 seconds, the algorithm counts that as high engagement, even if you hated it. - Echo Chamber: A situation where you mostly see content that agrees with your existing beliefs or interests, making it harder to hear other perspectives. Example: If you only like posts about basketball, your feed might stop showing you news about art or science, even if you’d enjoy them. - Data Points: Tiny pieces of information about you that algorithms use to make decisions. Example: The time you spend reading a post, the device you’re using, and even the speed of your scroll are all data points.

(Note for high school/college: In advanced study, algorithms are analyzed for bias, ethics, and their role in misinformation. The "engagement" metric, for example, can prioritize outrage over accuracy because anger drives more clicks.)

3. Assessment Translation

How this appears on state assessments (Grade 7): - Multiple Choice: Questions about how algorithms work, often with scenarios like: "Javier likes a post about skateboarding. What is the algorithm MOST likely to do next?" - Distractors might include: - "Show Javier fewer skateboarding posts" (wrong—algorithms reward engagement). - "Delete all other posts about sports" (wrong—algorithms don’t delete; they prioritize). - "Show Javier ads for skateboards" (partially correct, but not the immediate effect). - Correct answer: "Show Javier more posts about skateboarding or similar topics."

• Short Constructed Response: Questions like: "Explain how a social media algorithm might create an echo chamber for a user who only likes posts about cats. Use the term ‘engagement’ in your answer."

• Proficient response (model): > "If a user only likes posts about cats, the algorithm notices this high engagement and starts showing them more cat content. Over time, the user’s feed fills up with cat videos, memes, and ads, while posts about other topics (like dogs or science) get pushed down. This creates an echo chamber because the user only sees one type of content, making it harder to discover new interests."

• Evidence-Based Writing: Prompts like: "Some people argue that social media algorithms are helpful because they show us content we like. Others say they’re harmful because they trap us in echo chambers. Which side do you agree with? Use evidence from the text and your own experience to support your answer."

• What teachers look for:
  • Clear claim (e.g., "Algorithms are harmful because...").
  • At least two pieces of evidence (e.g., "The text says algorithms prioritize engagement, which can lead to echo chambers. For example, if I only like funny memes, I stop seeing news posts, even if I’d want to know about them.").
  • Counterargument (e.g., "Some might say algorithms are helpful because they save time, but...").

4. Mistake Taxonomy

Mistake 1: Misunderstanding "Engagement" - Prompt: "Why might a social media algorithm show you more videos about cooking, even if you don’t like cooking?" - Common wrong answer: "Because the algorithm knows I like cooking." - Why it loses credit: The student assumes the algorithm knows preferences, not that it infers them from actions. They miss that engagement isn’t just likes—it’s any interaction. - Correct approach:

"The algorithm might show cooking videos if I paused on one, even if I didn’t like it. Engagement includes things like watch time, so if I watched 5 seconds of a cooking video, the algorithm might think I’m interested and show me more."

Mistake 2: Overgeneralizing Algorithm Behavior - Prompt: "True or False: If you unlike a post, the algorithm will stop showing you similar content immediately." - Common wrong answer: "True." - Why it loses credit: Students assume algorithms update instantly or perfectly. In reality, algorithms learn slowly and sometimes "overcorrect" (e.g., if you unlike a post, you might still see similar ones for a while). - Correct approach:

"False. Algorithms take time to adjust. If you unlike a post, the algorithm might still show you similar content for a few days while it ‘learns’ your new preference. It’s not instant."

Mistake 3: Ignoring the "Why" Behind Echo Chambers - Prompt: "Explain how an echo chamber forms on social media. Use the term ‘data points’ in your answer." - Common wrong answer: "An echo chamber is when you only see one type of post." - Why it loses credit: The student describes the result but not the process. They miss how data points (like likes, shares, and watch time) cause the echo chamber. - Correct approach:

"An echo chamber forms when an algorithm notices you engage with certain posts (like data points such as likes or watch time) and starts showing you more of the same. For example, if you always like posts about basketball, the algorithm will prioritize basketball content, making it harder to see other topics like art or news."

5. Connection Layer

1. Within Media Literacy-Misinformation: Algorithms prioritize engagement, and outrage drives engagement. This is why false or sensational posts often spread faster than accurate ones—understanding algorithms helps you spot why misinformation goes viral.
2. Across Subjects-Math (Statistics): Algorithms use probability to predict what you’ll like. For example, if 80% of people who like soccer also like pizza, the algorithm might show you pizza ads. This is the same logic used in math to predict trends from data.
3. Outside School-Shopping Recommendations: Ever notice how Amazon suggests products "based on your browsing history"? That’s an algorithm at work, just like social media. Now you’ll recognize the pattern everywhere—from Netflix to YouTube to online ads.

6. The Stretch Question

"If social media algorithms are designed to keep you engaged, why do some people get ‘bored’ of their feeds after a few months? Shouldn’t the algorithm just keep showing them the same things they like?"

Pointer toward the answer: Algorithms do keep showing you what you’ve liked in the past—but humans get bored of repetition. Platforms solve this by introducing variations (e.g., if you like soccer, they might show you soccer and sports memes). But this can backfire: if the algorithm misjudges your "boredom threshold," you might start ignoring posts entirely. Some platforms also use explore pages to test new content on you, which is why your feed sometimes feels random. The real question is: Can an algorithm ever truly predict what you’ll want to see next, or is human curiosity too unpredictable?