# Studying Alignment in a Collaborative Learning Activity via Automatic Methods: The Link Between What We Say and Do

arXiv: https://arxiv.org/abs/2104.04429 · HTML: https://mlsanity.com/paper/2104.04429/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 92 references that are in the map.
- First posted on arXiv 9 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey](https://mlsanity.com/paper/2011.00564/) (2020)
- [Disfluencies and Human Speech Transcription Errors](https://mlsanity.com/paper/1904.04398/) (2019)

## Built on by (most cited first, 6 of 6)

- [Augmenting Automatic Speech Recognition Models with Disfluency Detection](https://mlsanity.com/paper/2409.10177/) (2024)
- [Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue](https://mlsanity.com/paper/2311.13061/) (2023)
- [When to generate hedges in peer-tutoring interactions](https://mlsanity.com/paper/2307.15582/) (2023)
- [TextAge: A Curated and Diverse Text Dataset for Age Classification](https://mlsanity.com/paper/2406.16890/) (2024)
- [On the Difficulty of Token-Level Modeling of Dysfluency and Fluency Shaping Artifacts](https://mlsanity.com/paper/2512.02027/) (2025)
- [Open Datasets in Learning Analytics: Trends, Challenges, and Best PRACTICE](https://mlsanity.com/paper/2602.17314/) (2026)

## Abstract (arXiv)

A dialogue is successful when there is alignment between the speakers at different linguistic levels. In this work, we consider the dialogue occurring between interlocutors engaged in a collaborative learning task, where they are not only evaluated on how well they performed, but also on how much they learnt. The main contribution of this work is to propose new automatic measures to study alignment; focusing on verbal (lexical) alignment, and behavioral alignment (when an instruction given by one was followed with concrete actions by another). A second contribution of our work is to study how spontaneous speech phenomena are used in the process of alignment. Lastly, we make public the dataset to study alignment in educational dialogues. Our results show that all teams verbally and behaviourally align to some degree regardless of their performance and learning, and our measures capture that teams that did not succeed in the task were simply slower to collaborate. Thus we find that teams that performed better, were faster to align. Furthermore, our methodology captures a productive period that includes the time where the interlocutors came up with their best solutions. We also find that well-performing teams verbalise the marker "oh" more when they are behaviourally aligned, compared to other times in the dialogue; showing that this marker is an important cue in alignment. To the best of our knowledge, we are the first to study the role of "oh" as an information management marker in a behavioral context (i.e. in connection to actions taken in a physical environment), compared to only a verbal one. Our measures contribute to the research in the field of educational dialogue and the intersection between dialogue and collaborative learning research.