About the Feed Simulator

Open the activity Open worksheet 3A

What pupils do

What it teaches, and why it is true of the real thing

How LOOP works

LOOP and real feeds

What we simplified, and why

What it does not teach

In class

Sources

1. Chaney, A. J. B., Stewart, B. M. & Engelhardt, B. E. (2018). How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. Proceedings of the 12th ACM Conference on Recommender Systems (RecSys '18).
2. Covington, P., Adams, J. & Sargin, E. (2016). Deep neural networks for YouTube recommendations. Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16).
3. Joachims, T., Granka, L., Pan, B., Hembrooke, H. & Gay, G. (2005). Accurately interpreting clickthrough data as implicit feedback. Proceedings of SIGIR '05.
4. Pariser, E. (2011). The Filter Bubble: What the Internet Is Hiding from You. Penguin Press.
5. Wang, G., Zhao, J., Van Kleek, M. & Shadbolt, N. (2022). 'Don't make assumptions about me!': Understanding children's perception of datafication online. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2).
6. Walan, S. (2024). Primary school students' perceptions of artificial intelligence – for good or bad. International Journal of Technology and Design Education.