---
title: "Exploring Effective Dialogue Act Sequences in One-on-one Computer Science Tutoring Dialogues (2011)"
description: "The paper asks which patterns of tutor and student moves actually help students learn in one-on-one Computer… 27 references, cited by 2 papers here."
authors: ["Lin Chen", "Barbara Di Eugenio", "Davide Fossati", "Stellan Ohlsson", "David Cosejo"]
year: 2011
venue: "Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications"
source: "https://aclanthology.org/W11-1408/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2011-exploring-effective-dialogue-act-sequences/"
---
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# Exploring Effective Dialogue Act Sequences in One-on-one Computer Science Tutoring Dialogues

**Authors:** [Lin Chen](https://md.research.elamur.ai/free/en/authors/lin-chen/), [Barbara Di Eugenio](https://md.research.elamur.ai/free/en/authors/barbara-di-eugenio/), Davide Fossati, Stellan Ohlsson and David Cosejo · **Year:** 2011 · **Venue:** [Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications](https://md.research.elamur.ai/free/en/venues/bea/) ([BEA 2011](https://md.research.elamur.ai/free/en/venues/bea/2011/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/W11-1408/) · [PDF](https://aclanthology.org/W11-1408.pdf) · [OpenAlex](https://openalex.org/W2165281967)

**Citations:** 12 citations (OpenAlex, 2026-10-14) · 2 in our library, of the 141,840 papers whose bibliographies we read

## Summary

The paper asks which patterns of tutor and student moves actually help students learn in one-on-one Computer Science tutoring. It also aims to turn those patterns into design guidance for iList, an intelligent tutoring system for linked-list problems. The authors recorded and transcribed 54 sessions on Lists, Stacks and Trees. Each session had one of two tutors: an experienced professor (LOW) or an undergraduate (JAC). The tutors turned out to be equally effective, so the authors set aside expert-versus-novice comparisons. Instead, they annotated a small set of dialogue acts and used multiple linear regression to predict learning gain from pre-test score, session length and counts of DA sequences.

The key methodological step is "flexible" bigrams and trigrams that may contain gaps, so that filler turns such as a student's "ok" do not break a pattern. Several sequences correlate significantly with learning, and the best trigram models beat models built from single DAs. These sequences are cognitively plausible and directly usable as rules when designing tutoring systems.

## Key points

- **Corpus and conditions.**
  - 54 sessions were collected over 3 semesters, with 30 students tutored by LOW and 24 by JAC.
  - A control group of 53 students attended an unrelated 40-minute lecture instead of tutoring.
  - Tutored students gained significantly more than the controls on every topic (ANOVA, p \< 0.01).
  - The two tutors did not differ significantly in learning gain.
- **Dialogue act inventory.** The DA set is minimalist and grounded in skill-acquisition theory:
  - DPI (procedural instruction) and DDI (declarative instruction);
  - +FB and -FB (positive and negative feedback);
  - Prompt (PT) and Student Initiative (SI).
  - Tutors were highly directive, producing 93.5% of all words.
- **Flexible matching.** Sequences with gaps of 0–3 intervening DAs were matched using fuzzy matching in Apache Lucene. Transcription followed the CHILDES conventions.
- **Best models (Table 8).**
  - Lists: {PT, DPI, FB}, R² = .415.
  - Stacks: {DDI, FB, PT}, R² = .416.
  - Trees: {FB, SI, DDI}, R² = .732.
  - R² gains over the best simpler models were 0.038, 0.12 and 0.056 respectively.
- **Other findings.**
  - The trigram {DPI, FB, DDI} was significant for Lists.
  - Models combining two different sequences as predictors were never significant.

## Interesting ideas
- Allowing gaps inside n-grams lets a pattern survive interruptions by meaningless backchannel turns, which keeps short exchanges from dominating sequence statistics.
- When the "expert vs. novice" contrast showed no difference, the authors switched to explaining outcomes with regression over behaviours rather than over group labels.
- A deliberately tiny, theory-motivated act inventory (tell how, tell what, confirm, correct, prompt, initiative) made the annotation tractable and every coefficient interpretable.
- A rare category such as student initiative can still surface as a significant predictor in regression, even though frequency counting would overlook it.
- Controlling for pre-test score separates the effect of a pattern from what the learner already knew.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (frames): When chaining speech acts into exchanges such as instruction → feedback → explanation, skip acknowledgement turns within a bounded window of up to 3 acts, so dialogue structure reflects real moves.
- **I2** (learn): Evaluate repair rules or parser variants by regressing downstream accuracy on how often each rule fires, not only by comparing system A with system B.
- **I3** (frames): A compact, explainable speech-act set for instructional texts in the prag crate could label teacher-style sentences in school-course material.
- **I4** (learn): In error analysis, test rare constructions as explicit predictors of failure instead of discarding them as low-count noise.
- **I5** (answer): When scoring student-reader improvements on tests, condition on baseline per-item difficulty so that gains are not credited to easy items.

*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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- [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) — University, Pittsburgh, United States · 365 works · 158,048 citations
- [University of Illinois Chicago](https://md.research.elamur.ai/free/en/universities/university-of-illinois-chicago/) — University, Chicago, United States · 38 works · 19,844 citations
- [Carnegie Mellon University Qatar](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university-qatar/) — University, Doha, Qatar · 13 works · 1,947 citations

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## Cite

Cite as: Lin Chen, Barbara Di Eugenio, Davide Fossati, Stellan Ohlsson and David Cosejo. 2011. Exploring Effective Dialogue Act Sequences in One-on-one Computer Science Tutoring Dialogues. Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications.

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

```bibtex
@inproceedings{chen2011exploring,
  title = {{Exploring Effective Dialogue Act Sequences in One-on-one Computer Science Tutoring Dialogues}},
  author = {Lin Chen, Barbara {Di Eugenio}, Davide Fossati, Stellan Ohlsson and David Cosejo},
  booktitle = {Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications},
  year = {2011},
  url = {https://aclanthology.org/W11-1408/}
}
```

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*Updated 24 October 2026*