Downscaling the Spine-Cluster Architecture: K-12 Adaptation of Star-Topology Skills for a Zero-to-Competitive Python Pathway
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2026
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| _version_ | 1866901478577274880 |
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| author | Le, That |
| author_facet | Le, That |
| contents | <p>The Spine-Cluster architecture for Star-Topology Skills instruction [1] specifies a two-axis decomposition of competitive-programming pedagogy: a seven-node linear spine of content-independent cognitive milestones (L1 . . . L7 ) and nine star clusters of algorithm families (S1 . . . S9 ), traversed under four deep-structure variation axes (input-size class, constraint-tightness, composition depth, problem-framing mode). The architecture targets Layer D — International Olympiad in Informatics-level trainees with C++ fluency and formaloperational cognition. We analyse whether the architecture can be downscaled to a K-12 audience spanning ages 8 through 18 while preserving its three load-bearing claims: contentindependent cognitive progression, deep-structure recognition through variation, and schema induction via multiple analogs. Drawing on ACT-R skill acquisition [2, 3], Chi-Feltovich-Glaser expert-novice categorisation [4], Gick-Holyoak schema induction [5], VanLehn's synthesis of cognitive skill acquisition [6], Sweller cognitive load theory [7, 8], Ericsson deliberate practice [9] bounded by [10], and post-Piagetian developmental psychology [11, 12, 13], we propose a minimum-deformation translation: a five-volume pathway Vol 0 (ages 8–11, unplugged) through Vol 4 (ages 15–18, contest-ready) with per-volume active milestone sets, a reduced five-cluster intermediate taxonomy (S1′ . . . S5′ ) for Vol 1–3 that expands to the full S1 . . . S9 at Vol 4, degenerateaxis bins for age-inaccessible variation dimensions (constrainttightness absent until Vol 3), and four per-boundary diagnostic instruments that certify milestone readiness without empirical cohort dependence. The adaptation preserves the architecture's transfer claim while respecting working-memory ceilings and domain-specific progression evidence. Five open questions — L5 bifurcation validity, intermediate taxonomy width, framingmode onset age, instrument psychometric validity, and textbookvs-tutor dispatch fidelity — remain for adult-elicitation probes under the program's no-cohort scope.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20131557 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Downscaling the Spine-Cluster Architecture: K-12 Adaptation of Star-Topology Skills for a Zero-to-Competitive Python Pathway Le, That elix-researches Vietnamese informatics education K-12 programming competitive programming pedagogy working paper <p>The Spine-Cluster architecture for Star-Topology Skills instruction [1] specifies a two-axis decomposition of competitive-programming pedagogy: a seven-node linear spine of content-independent cognitive milestones (L1 . . . L7 ) and nine star clusters of algorithm families (S1 . . . S9 ), traversed under four deep-structure variation axes (input-size class, constraint-tightness, composition depth, problem-framing mode). The architecture targets Layer D — International Olympiad in Informatics-level trainees with C++ fluency and formaloperational cognition. We analyse whether the architecture can be downscaled to a K-12 audience spanning ages 8 through 18 while preserving its three load-bearing claims: contentindependent cognitive progression, deep-structure recognition through variation, and schema induction via multiple analogs. Drawing on ACT-R skill acquisition [2, 3], Chi-Feltovich-Glaser expert-novice categorisation [4], Gick-Holyoak schema induction [5], VanLehn's synthesis of cognitive skill acquisition [6], Sweller cognitive load theory [7, 8], Ericsson deliberate practice [9] bounded by [10], and post-Piagetian developmental psychology [11, 12, 13], we propose a minimum-deformation translation: a five-volume pathway Vol 0 (ages 8–11, unplugged) through Vol 4 (ages 15–18, contest-ready) with per-volume active milestone sets, a reduced five-cluster intermediate taxonomy (S1′ . . . S5′ ) for Vol 1–3 that expands to the full S1 . . . S9 at Vol 4, degenerateaxis bins for age-inaccessible variation dimensions (constrainttightness absent until Vol 3), and four per-boundary diagnostic instruments that certify milestone readiness without empirical cohort dependence. The adaptation preserves the architecture's transfer claim while respecting working-memory ceilings and domain-specific progression evidence. Five open questions — L5 bifurcation validity, intermediate taxonomy width, framingmode onset age, instrument psychometric validity, and textbookvs-tutor dispatch fidelity — remain for adult-elicitation probes under the program's no-cohort scope.</p> |
| title | Downscaling the Spine-Cluster Architecture: K-12 Adaptation of Star-Topology Skills for a Zero-to-Competitive Python Pathway |
| topic | elix-researches Vietnamese informatics education K-12 programming competitive programming pedagogy working paper |
| url | https://doi.org/10.5281/zenodo.20131557 |