Acoustic-Prosodic Entrainment and Rapport in Collaborative Learning Dialogues

Lubold, Nichola; Pon-Barry, Heather

doi:10.1145/2666633.2666635

Cited by 68 publications

(72 citation statements)

References 27 publications

(22 reference statements)

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“…These studies have demonstrated the utility of these types of data as a means of convergent triangulation in correlating non-verbal elements with learning gains. Other co-located computer-based, CPS studies using the MMLA approach have included verbal data in the analysis, but these studies focus not on the semantic meaning of the utterances, rather they focus on prosodic elements (pitch, tone), duration of speaking time and/or turn-taking to help interpret group functioning (Lubold & Pon-Barry, 2014, Praharaj, Scheffel, Drachsler, & Specht, 2018. While all of these studies are important and are contributing to our understanding of body language and turn taking in colocated, computer-based, CPS, none of them are seeking to use learning analytics to semantically analyze human dialogue.…”

Section: Learning Analyticsmentioning

confidence: 99%

Exploring the potential of natural language processing to support microgenetic analysis of collaborative learning discussions

Sullivan¹,

Keith²

2019

Brit J Educational Tech

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In this study, we explore the potential of a natural language processing (NLP) approach to support discourse analysis of in‐situ, small group learning conversations. The theoretical basis of this work derives from Bakhtin’s notion of speech genres as bounded by educational robotics activity. Our goal is to leverage computational linguistics methods to advance and improve educational research methods. We used a parts‐of‐speech (POS) tagging program to automatically parse a transcript of spoken dialogue collected from a small group of middle school students involved in solving a robotics challenge. We grammatically parsed the dialogue at the level of the trigram. Then, through a deliberative process, we mapped the POS trigrams to our theoretically derived problem solving in computational environments coding system. Next, we developed a stacked histogram visualization to identify rich interactional segments in the data. Seven segments of the transcript were thus identified for closer analysis. Our NLP‐based approach partially replicated prior findings. Here, we present the theoretical basis for the work, our analytical approach in exploring this NLP‐based method, and our research findings. What is already known about this topic Over the last 10 years, several educational research papers indicate that natural language processing (NLP) techniques can be used to help interpret well‐structured, written dialogue, eg, conversations in online class discussions. Two recent papers indicate that NLP techniques can also be used to help interpret well‐structured, spoken dialogue, eg, replies to interview questions and/or comments made during think aloud protocols. Multimodal learning analytic techniques are being used to investigate collaborative learning. These studies use non‐verbal features of data (gaze, gesture, physical actions), prosodic features of verbal data (pitch and tone) and/or turn‐taking and duration of talk per speaker data, as means of predicting group success. None of the MMLA studies attempt semantic analysis of student talk in collaborative settings. What this paper adds A theoretical framework for why and how an automated NLP approach can support discourse analysis research on co‐located, computer‐based, collaborative problem solving interactions. This framework, entitled the Problem Solving in Computational Environment Speech Genre, links children’s physical interactions with computational devices to their verbal exchanges and presents a theoretical rationale for the use of NLP methods in educational research. Description of an interdisciplinary method that combines NLP techniques with qualitative coding approaches to support analysis of student collaborative learning with educational robotics. Identification of student learning outcomes derived from the semantic, PSCE Speech Genre and NLP approach. Implications for practice and/or policy Educational researchers will be able to expand upon our findings towards the goal of using computation and automation to support microgenetic analysis of la...

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Section: Learning Analyticsmentioning

confidence: 99%

Exploring the potential of natural language processing to support microgenetic analysis of collaborative learning discussions

Sullivan¹,

Keith²

2019

Brit J Educational Tech

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“…Entrainment was found to occur in more interactively oriented scenarios and is generally regarded as a phenomenon conveying collaboration or rapport [25]. In a previous study, [20], [21] found pitch synchrony in speed dating dialogues both on a global level across conversations as well as on a local level within conversations.…”

Section: Introductionmentioning

confidence: 98%

Pitch Convergence as an Effect of Perceived Attractiveness and Likability

Michalsky

Schoormann

2017

Interspeech 2017

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While there is a growing body of research on which and how pitch features are perceived as attractive or likable, there are few studies investigating how the impression of a speaker as attractive or likable affects the speech behavior of his/her interlocutor. Recent studies have shown that perceived attractiveness and likability may not only have an effect on a speaker's pitch features in isolation but also on the prosodic entrainment. It has been shown that how speakers synchronize their pitch features relatively to their interlocutor is affected by such impressions. This study investigates pitch convergence, examining whether speakers become more similar over the course of a conversation depending on perceived attractiveness and/or likability. The expected pitch convergence is thereby investigated on two levels, over the entire conversation (globally) as well as turn-wise (locally). The results from a speed dating experiment with 98 mixed-sex dialogues of heterosexual singles show that speakers become more similar globally and locally over time both in register and range. Furthermore, the degree of pitch convergence is greatly affected by perceived attractiveness and likability with effects differing between attractiveness and likability as well as between the global and the local level.

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“…The intensity feature was included for comparison purposes with previous local acoustic-prosodic entrainment measures. The parameters used to extract the intensity feature are described in [19].…”

Section: Intensitymentioning

confidence: 99%

A Discriminative Acoustic-Prosodic Approach for Measuring Local Entrainment

Willi¹,

Borrie²,

Barrett³

et al. 2018

Interspeech 2018

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Acoustic-prosodic entrainment describes the tendency of humans to align or adapt their speech acoustics to each other in conversation. This alignment of spoken behavior has important implications for conversational success. However, modeling the subtle nature of entrainment in spoken dialogue continues to pose a challenge. In this paper, we propose a straightforward definition for local entrainment in the speech domain and operationalize an algorithm based on this: acoustic-prosodic features that capture entrainment should be maximally different between real conversations involving two partners and sham conversations generated by randomly mixing the speaking turns from the original two conversational partners. We propose an approach for measuring local entrainment that quantifies alignment of behavior on a turn-by-turn basis, projecting the differences between interlocutors' acoustic-prosodic features for a given turn onto a discriminative feature subspace that maximizes the difference between real and sham conversations. We evaluate the method using the derived features to drive a classifier aiming to predict an objective measure of conversational success (i.e., low versus high), on a corpus of task-oriented conversations. The proposed entrainment approach achieves 72% classification accuracy using a Naive Bayes classifier, outperforming three previously established approaches evaluated on the same conversational corpus.

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Acoustic-Prosodic Entrainment and Rapport in Collaborative Learning Dialogues

Cited by 68 publications

References 27 publications

Exploring the potential of natural language processing to support microgenetic analysis of collaborative learning discussions

Exploring the potential of natural language processing to support microgenetic analysis of collaborative learning discussions

Pitch Convergence as an Effect of Perceived Attractiveness and Likability

A Discriminative Acoustic-Prosodic Approach for Measuring Local Entrainment

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