59 lines
2.8 KiB
Plaintext
59 lines
2.8 KiB
Plaintext
Skip to main content
|
||
Computer Science > Computation and Language
|
||
arXiv:2511.17069 (cs)
|
||
[Submitted on 21 Nov 2025]
|
||
Principled Design of Interpretable Automated Scoring for Large-Scale Educational Assessments
|
||
Yunsung Kim, Mike Hardy, Joseph Tey, Candace Thille, Chris Piech
|
||
View PDF
|
||
HTML (experimental)
|
||
AI-driven automated scoring systems offer scalable and efficient means of evaluating complex student-generated responses. Yet, despite increasing demand for transparency and interpretability, the field has yet to develop a widely accepted solution for interpretable automated scoring to be used in large-scale real-world assessments. This work takes a principled approach to address this challenge. We analyze the needs and potential benefits of interpretable automated scoring for various assessment stakeholders and develop four principles of interpretability -- Faithfulness, Groundedness, Traceability, and Interchangeability (FGTI) -- targeted at those needs. To illustrate the feasibility of implementing these principles, we develop the AnalyticScore framework for short answer scoring as a baseline reference framework for future research. AnalyticScore operates by (1) extracting explicitly identifiable elements of the responses, (2) featurizing each response into human-interpretable values using LLMs, and (3) applying an intuitive ordinal logistic regression model for scoring. In terms of scoring accuracy, AnalyticScore outperforms many uninterpretable scoring methods, and is within only 0.06 QWK of the uninterpretable SOTA on average across 10 items from the ASAP-SAS dataset. By comparing against human annotators conducting the same featurization task, we further demonstrate that the featurization behavior of AnalyticScore aligns well with that of humans.
|
||
Comments: 16 pages, 2 figures
|
||
Subjects: Computation and Language (cs.CL)
|
||
Cite as: arXiv:2511.17069 [cs.CL]
|
||
(or arXiv:2511.17069v1 [cs.CL] for this version)
|
||
|
||
https://doi.org/10.48550/arXiv.2511.17069
|
||
Focus to learn more
|
||
Submission history
|
||
From: Yunsung Kim [view email]
|
||
[v1] Fri, 21 Nov 2025 09:19:05 UTC (183 KB)
|
||
|
||
Access Paper:
|
||
View PDFHTML (experimental)TeX Source
|
||
view license
|
||
Current browse context: cs.CL
|
||
< prev next >
|
||
|
||
newrecent2025-11
|
||
Change to browse by: cs
|
||
References & Citations
|
||
NASA ADS
|
||
Google Scholar
|
||
Semantic Scholar
|
||
Export BibTeX Citation
|
||
Bookmark
|
||
Bibliographic Tools
|
||
Bibliographic and Citation Tools
|
||
Bibliographic Explorer Toggle
|
||
Bibliographic Explorer (What is the Explorer?)
|
||
Connected Papers Toggle
|
||
Connected Papers (What is Connected Papers?)
|
||
Litmaps Toggle
|
||
Litmaps (What is Litmaps?)
|
||
scite.ai Toggle
|
||
scite Smart Citations (What are Smart Citations?)
|
||
Code, Data, Media
|
||
Demos
|
||
Related Papers
|
||
About arXivLabs
|
||
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
|
||
About
|
||
Help
|
||
Contact
|
||
Subscribe
|
||
Copyright
|
||
Privacy Policy
|
||
Web Accessibility Assistance
|
||
|
||
arXiv Operational Status
|