Goose Academy · Last revision: April 29, 2026
Scientific References
This document summarizes the research that informs Goose Academy's design.
The goal is not to replicate academic protocols, but to translate well-established findings from cognitive science into a simple, offline, and self-paced learning experience.
Goose Academy is research-informed, not research-prescriptive.
The systems described below are inspired by scientific work, but adapted for usability, short sessions, and accessibility.
Transparency note
While the principles cited are scientifically grounded, their implementation in Goose Academy has not yet been validated through controlled empirical studies.
Part 1 — Adaptive Item Selection
During a session, Goose Academy selects items using a dynamic weighting system based on:
- session mastery
- long-term retention (spaced repetition)
- recency (spacing)
- error history (confusion tracking)
- similarity between items
Research foundations
| Reference | Principle | Context |
|---|---|---|
| Mettler, E., et al. (2016) | Adaptive sequencing | Perceptual learning tasks |
| Kornell, N., & Bjork, R. A. (2008) | Interleaving vs blocking | Category learning |
| Carvalho, P. F., & Goldstone, R. L. (2015) | Discriminative contrast | Lab experiments |
| Settles, B., & Meeder, B. (2016) | Memory modeling (HLR) | Requires large datasets |
| Metcalfe, J. (2017) | Learning from errors | Review |
| Pyc, M. A., & Rawson, K. A. (2009) | Retrieval strengthening | Verbal materials |
| Roediger, H. L., & Karpicke, J. D. (2006) | Testing effect | Prose recall |
| Bjork, R. A. (1994) | Desirable difficulties | Theoretical framework |
Formula overview
Each item is assigned a weight:
Wᵢ = Eᵢ · Mᵢ · Bᵢ · Sᵢ · Rᵢ · (1 + α Ĉᵢ) · (1 + β K̂ᵢ Rᵢ)| Variable | Meaning | Basis |
|---|---|---|
| Bᵢ | Long-term mastery | Strong evidence (spacing) |
| Sᵢ | Session progress | Moderate evidence |
| Rᵢ | Recency | Strong evidence |
| Cᵢ | Accumulated confusion | Theoretical support |
| Kᵢ | Contrast with previous item | Context-dependent |
| Mᵢ | Down-weight mastered items | Heuristic |
| Eᵢ | Prevent repetition | UX constraint (avoids short-term priming that could inflate perceived mastery) |
Parameters α and β
These coefficients are not empirically calibrated. They are design choices balancing confusion and contrast effects.
Design interpretation
This system combines several principles:
- Spacing → strong evidence
- Interleaving → conditional effectiveness
- Error-driven learning → well established
- Contrast sequencing → dependent on item structure
The result is a deterministic, on-device adaptive system requiring no external data.
Critical note
The formula itself is a design synthesis, not a validated model. Its effectiveness relative to simpler approaches (e.g. pure SRS) is currently unknown.
Part 2 — Level Design & Progression
Goose Academy's progression system is designed for usability and incremental learning.
Design principles
- Item order optimized for **perceptual contrast**
- Multiple topics to engage **different knowledge domains**
- Previously learned items remain → **interleaving**
Design interpretation
The system balances:
- **Learnability** → small steps
- **Retention** → repetition over time
- **Usability** → short sessions
It is inspired by research, not a strict reproduction.
References (with persistent identifiers)
Last verified: April 2026
- Bjork, R. A. (1994).
- Carvalho, P. F., & Goldstone, R. L. (2015). https://doi.org/10.3389/fpsyg.2015.00505
- Kornell, N., & Bjork, R. A. (2008). https://doi.org/10.1111/j.1467-9280.2008.02127.x
- Metcalfe, J. (2017). https://doi.org/10.1146/annurev-psych-010416-044022
- Mettler, E., et al. (2016). https://doi.org/10.1037/xge0000176
- Pyc, M. A., & Rawson, K. A. (2009). https://doi.org/10.1016/j.jml.2009.01.001
- Roediger, H. L., & Karpicke, J. D. (2006). https://doi.org/10.1111/j.1467-9280.2006.01693.x
- Settles, B., & Meeder, B. (2016). https://doi.org/10.18653/v1/P16-1174
Goose Academy translates cognitive science into a playful, offline learning experience. This document reflects a commitment to transparency, rigor, and humility—bridging the gap between laboratory research and real-world use.