| author | Alan Dipert
<alan@dipert.org> 2025-12-04 05:51:07 UTC |
| committer | Alan Dipert
<alan@dipert.org> 2025-12-04 05:51:07 UTC |
| parent | afc76876c9a2504541254d88232714fd16d2090e |
| README.md | +7 | -0 |
diff --git a/README.md b/README.md index 10ee29a..2a48c24 100644 --- a/README.md +++ b/README.md @@ -14,6 +14,13 @@ ALAN is a fully self-contained artificial-language aptitude assessment inspired - **Reliability controls:** Property-based tests enforce grammar validity, one-correct-answer semantics, irregular contrasts, structural diversity (ditransitives, feminine-plural receivers, adjective-bearing NPs), and coverage quotas. - **Construct alignment with software practice:** Success requires precise rule following, rapid pattern spotting, and handling edge cases (irregulars)—abilities useful in commercial software roles (debugging, code review, protocol/spec compliance). +## Background Reading (Psychometrics & AGL) +- Artificial Grammar Learning: Reber, A. S. (1967). “Implicit learning of artificial grammars.” *Journal of Verbal Learning and Verbal Behavior*. [Wikipedia](https://en.wikipedia.org/wiki/Artificial_grammar_learning) +- Language aptitude and grammar inference: Ellis, N. C. (2005). “At the interface: Dynamic interactions of explicit and implicit language knowledge.” *Studies in Second Language Acquisition*. +- Item response theory and minimal pairs: Hambleton, R. K., & Swaminathan, H. (2010). *Item Response Theory: Principles and Applications*. [Wikipedia](https://en.wikipedia.org/wiki/Item_response_theory) +- DLAB overview: [Wikipedia – Defense Language Aptitude Battery](https://en.wikipedia.org/wiki/Defense_Language_Aptitude_Battery) +- Distractor design and discrimination: Haladyna, T. M., & Downing, S. M. (1989). “Validating multiple-choice test items.” + ## Quick Start ```bash make run # generates JSON, booklet, and key