Research overview

Working title: Teaching After the Feed: A Framework for Learning in Algorithmic Culture

Core question: How should classroom teaching respond to the logics of the feed, recommendation, and AI-shaped discovery?

Current status: Proposal accepted. Research design now being revised in response to committee feedback.

Committee feedback to address

• Clarify what is meant by algorithmic logic.
• Define feed-like discovery more precisely and distinguish it from current classroom methods.
• Make intended outcomes measurable.
• Specify instructional moves for each approach.
• Narrow the scope so the study remains manageable within one year.
• Produce a final research report plus a transferable resource for other teachers.

Revised working focus

This project compares two instructional approaches within a one-year action research design:

1. Teaching with the feed
   • Teacher-curated adjacency
   • Strong signalling of what comes next
   • Pattern recognition before full explanation
   • Feed-like discovery without using actual platform algorithms
2. Teaching against the feed
   • Stable archive-first structure
   • Slow revisiting and reuse
   • Explicit pathways and memory-oriented learning
   • Reduced novelty and stronger retrieval

Second project aim (added 2026-07-04): teaching after the feed is not only about slowing students down. It is also about making the hidden systems of image organization visible. Metadata, catalogues, archives, tags, feeds, and collections all teach students how to see before they interpret an image, so a second aim is a set of metadata literacies: teaching students to read the sorting itself, not only the sorted image.

Key definitions

Algorithmic logic

A mode of structuring attention in which content is surfaced through ranking, adjacency, novelty, compression, and strong signalling of the next step.

What makes feed-like content easy to attend to