Speed–Accuracy Trade-offs in Knowledge Component Identification from Student Code

Jing Fan; Tsvetomila Mihaylova; Peter Brusilovsky; Juho Leinonen; Charles Koutcheme; Narges Norouzi; Bita Akram; Arto Hellas

Speed–Accuracy Trade-offs in Knowledge Component Identification from Student Code

Jing Fan, Tsvetomila Mihaylova, Peter Brusilovsky, Juho Leinonen, Charles Koutcheme, Narges Norouzi, Bita Akram, Arto Hellas

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Abstract

We compare traditional machine-learning models and instruction-tuned Gemma 3 models for identifying knowledge components from student code. On 1,800 GPT-4o-annotated Dart submissions, feature-based ML is more accurate and substantially faster than LLM approaches. Joint multi-KC prompting also suffers from format failures, highlighting deployment-oriented advantages of traditional ML.

Anthology ID:: 2026.aimecon-main.15
Volume:: Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
Month:: October
Year:: 2026
Address:: Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
Editors:: Joshua Wilson, Christopher Ormerod, Magdalen Beiting-Parrish
Venue:: AIME-Con
SIG:
Publisher:: National Council on Measurement in Education (NCME)
Note:
Pages:: 146–153
Language:
URL:: https://aclanthology.org/2026.aimecon-main.15/
DOI:
Bibkey:
Cite (ACL):: Jing Fan, Tsvetomila Mihaylova, Peter Brusilovsky, Juho Leinonen, Charles Koutcheme, Narges Norouzi, Bita Akram, and Arto Hellas. 2026. Speed–Accuracy Trade-offs in Knowledge Component Identification from Student Code. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 146–153, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):: Speed–Accuracy Trade-offs in Knowledge Component Identification from Student Code (Fan et al., AIME-Con 2026)
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PDF:: https://aclanthology.org/2026.aimecon-main.15.pdf

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@inproceedings{fan-etal-2026-speed,
    title = "Speed{--}Accuracy Trade-offs in Knowledge Component Identification from Student Code",
    author = "Fan, Jing  and
      Mihaylova, Tsvetomila  and
      Brusilovsky, Peter  and
      Leinonen, Juho  and
      Koutcheme, Charles  and
      Norouzi, Narges  and
      Akram, Bita  and
      Hellas, Arto",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting-Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Full Papers",
    month = oct,
    year = "2026",
    address = "Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States",
    publisher = "National Council on Measurement in Education (NCME)",
    url = "https://aclanthology.org/2026.aimecon-main.15/",
    pages = "146--153",
    ISBN = "979-8-9983004-0-0",
    abstract = "We compare traditional machine-learning models and instruction-tuned Gemma 3 models for identifying knowledge components from student code. On 1,800 GPT-4o-annotated Dart submissions, feature-based ML is more accurate and substantially faster than LLM approaches. Joint multi-KC prompting also suffers from format failures, highlighting deployment-oriented advantages of traditional ML."
}

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%0 Conference Proceedings
%T Speed–Accuracy Trade-offs in Knowledge Component Identification from Student Code
%A Fan, Jing
%A Mihaylova, Tsvetomila
%A Brusilovsky, Peter
%A Leinonen, Juho
%A Koutcheme, Charles
%A Norouzi, Narges
%A Akram, Bita
%A Hellas, Arto
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-0-0
%F fan-etal-2026-speed
%X We compare traditional machine-learning models and instruction-tuned Gemma 3 models for identifying knowledge components from student code. On 1,800 GPT-4o-annotated Dart submissions, feature-based ML is more accurate and substantially faster than LLM approaches. Joint multi-KC prompting also suffers from format failures, highlighting deployment-oriented advantages of traditional ML.
%U https://aclanthology.org/2026.aimecon-main.15/
%P 146-153

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Markdown (Informal)

[Speed–Accuracy Trade-offs in Knowledge Component Identification from Student Code](https://aclanthology.org/2026.aimecon-main.15/) (Fan et al., AIME-Con 2026)

Speed–Accuracy Trade-offs in Knowledge Component Identification from Student Code (Fan et al., AIME-Con 2026)

ACL

Jing Fan, Tsvetomila Mihaylova, Peter Brusilovsky, Juho Leinonen, Charles Koutcheme, Narges Norouzi, Bita Akram, and Arto Hellas. 2026. Speed–Accuracy Trade-offs in Knowledge Component Identification from Student Code. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 146–153, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).