Research
Teaching After the Feed
A Framework for Learning in Algorithmic Culture
How should classroom teaching respond to the logics of the feed, recommendation, and AI-shaped discovery?
Students today encounter most information through ranked, adjacent, algorithmically curated surfaces: feeds, recommendation queues, search results. These surfaces share a logic: novelty over return, strong signals of what comes next, compression of context, rapid connection-making without full explanation.
This project asks whether classroom teaching should work with that logic or against it, and what each choice costs. Rather than treating the feed as a problem to solve, it takes the feed's structure seriously as a set of instructional techniques that can be adapted, rejected, or combined.
The research is an action research study comparing two instructional models across a single course and class.
Teaching with the feed
Teacher-designed sequences that borrow features from the feed without handing control to a platform.
- Curate 4–6 tightly related examples
- Begin with comparison before explanation
- Reveal context incrementally
- Signal the next move clearly
- Build lessons through adjacency, contrast, and pattern
Teaching against the feed
Archive-first structures that resist novelty in favour of return, reuse, and accumulation.
- Begin from a stable archive or reference bank
- Revisit prior material before adding new material
- Use slower pacing and stronger categorisation
- Make pathways explicit
- Build retrieval and annotation into each lesson
Research questions
- 1.How do feed-like and archive-first instructional models affect student attention, comprehension, transfer, and revision?
- 2.Which teacher moves matter most within each model?
- 3.When does teaching with the feed support learning, and when does teaching against the feed better support depth and transfer?
Current status
Proposal accepted. Research design in revision following committee feedback. One teacher, one A-Level course, one bounded unit. Same learning goals and assessment lenses across both cycles.