Algorithm Architects and Detectives

This sequence examines the invisible algorithms that curate content on video platforms and social media. Students investigate the 'attention economy' and how algorithms predict user preferences to keep them engaged. By analyzing their own experiences and simulated feeds, they identify how recommendation engines can create 'filter bubbles.' The learning arc moves from understanding the mechanism of recommendation to evaluating its impact on critical thinking and exposure to new ideas.

Mechanisms of Recommendation Engines

Students play a paper-based game where they act as the 'algorithm,' predicting what a partner wants to see next based on their past choices. This demystifies the concept of prediction based on history.

The Loop Optimization and Engagement

Students explore the goal of most entertainment algorithms: maximizing time spent on the platform. They discuss the concept of the 'infinite scroll' and auto-play features.

Filter Bubbles and Echo Chambers

Through a simulation, students see how clicking only on one type of topic eventually removes all other topics from a feed. They define 'filter bubble' and discuss the downsides of only seeing things you agree with.

Analyzing User Autonomy

Students conduct a 'Feed Audit' (using mock profiles) to see how quickly a feed changes based on interaction. They discuss how much control the user has versus the algorithm.

Designing a Healthy Feed

Students prototype a user interface for a video app that prioritizes user well-being over watch time. They present features that help users explore diverse topics.