---
title: "Using Interaction Log Data to Evaluate and Improve Feedback"
description: "Intelligent tutoring systems record detailed learner interactions, but these logs are rarely used to check whether the system's… 74 references."
authors: ["Mariia Soliar", "Leona Colling", "Stephen Bodnar", "Detmar Meurers"]
year: 2026
venue: "Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)"
source: "https://aclanthology.org/2026.bea-1.14/"
doi: "10.18653/v1/2026.bea-1.14"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/soliar-2026-interaction-log-data-evaluate-improve/"
---
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# Using Interaction Log Data to Evaluate and Improve Feedback Accuracy in an Intelligent Language Tutoring System

**Authors:** Mariia Soliar, Leona Colling, Stephen Bodnar and [Detmar Meurers](https://md.research.elamur.ai/free/en/authors/detmar-meurers/) · **Year:** 2026 · **Venue:** [Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)](https://md.research.elamur.ai/free/en/venues/bea/) ([BEA 2026](https://md.research.elamur.ai/free/en/venues/bea/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.bea-1.14/) · [PDF](https://aclanthology.org/2026.bea-1.14.pdf) · [DOI 10.18653/v1/2026.bea-1.14](https://doi.org/10.18653/v1/2026.bea-1.14) · [OpenAlex](https://openalex.org/W7166873896)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Intelligent tutoring systems record detailed learner interactions, but these logs are rarely used to check whether the system's own feedback is correct. This paper does that for FeedBook, a web-based English ITS for 7th-grade learners in German secondary schools. FeedBook uses expert-written rules to turn target answers in gap-filling grammar exercises into anticipated learner errors, each paired with a teacher-written metalinguistic feedback message. The authors drew on authentic logs (5,646 logs from 368 students). First, they passed the logged answers through a pipeline that mimics the feedback mechanism and annotated whether the feedback was accurate, specific, or a generic fallback. This exposed frequent errors, mostly in question formation, that received wrong or unspecific responses. They then revised the relevant rules and tested the new rule set on a separate, unseen dataset. The updated rules performed significantly better than the original ones, and the gains held on new data. The study shows that log-based evaluation can complement expert intuition when maintaining transparent rule-based feedback. This matters for semi-open exercises, in which the acceptable answers are already defined and false positives from LLMs pose a risk.

## Key points
- **Data:** 5,646 interaction logs from 368 students, collected in the Interact4School and AI2Teach projects. The data come from two collection periods, one used for training (rule refinement) and one for testing generalization.
- **System and tools:** FeedBook's offline feedback generation relies on UIMA, ClearNLP, Morpha and Sfst. If no rule-based hypothesis matches a learner's answer, the system shows a default "try again" message.
- **Rule refinement focus:** The authors prioritized rules for interrogative constructions, such as question words and auxiliaries in simple past and simple present. They found that even a few rule changes gave statistically significant improvements that generalized to unseen data.
- **Error sources found:** Most incorrect feedback in the baseline was misapplied spelling feedback. Some failures came from NLP annotation errors, for example wrong lemmas from UIMA Java SDK 2.9.0 causing default feedback for do/does/did confusion.
- **Acknowledged limitations:** The two data collections differed in several ways that were not controlled for:
  - exercises were manual in the first period and automatically generated in the second;
  - difficulty was assigned adaptively in the second period;
  - capitalization errors were ignored in the second period;
  - front-end answer preprocessing was added in the second period.

  These differences may have favored the test results.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [University of Tübingen](https://md.research.elamur.ai/free/en/universities/university-of-tubingen/) — University, Tübingen, Germany · 75 works · 41,081 citations
- [Leibniz-Institut für Wissensmedien](https://md.research.elamur.ai/free/en/labs/leibniz-institut-fur-wissensmedien/) — Research institute, Tübingen, Germany · 2 works

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## Cite

Cite as: Mariia Soliar, Leona Colling, Stephen Bodnar and Detmar Meurers. 2026. Using Interaction Log Data to Evaluate and Improve Feedback Accuracy in an Intelligent Language Tutoring System. Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{soliar2026using,
  title = {{Using Interaction Log Data to Evaluate and Improve Feedback Accuracy in an Intelligent Language Tutoring System}},
  author = {Mariia Soliar, Leona Colling, Stephen Bodnar and Detmar Meurers},
  booktitle = {Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.bea-1.14},
  url = {https://doi.org/10.18653/v1/2026.bea-1.14}
}
```

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*Updated 1 November 2026*