The Way it Makes you Feel Predicting Users’ Engagement during Interviews with Biofeedback and Supervised Learning

Girardi, Daniela; Ferrari, Alessio; Novielli, Nicole; Spoletini, Paola; Fucci, Davide; Huichapa, Thaide

doi:10.1109/re48521.2020.00016

Cited by 5 publications

(9 citation statements)

References 79 publications

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“…As for biometrics, we observe that the performance significantly increases when SMOTE is applied for balancing our training data, achieving and F1measures up to 0.98 and 0.97 for valence and arousal, respectively (see Table 6), thus outperforming our previous classifier (Girardi et al, 2020a). A direct comparison is possible also possible with the performance achieved in the empirical study by Girardi et al (2020b), as we use the same device (i.e., Empatica E4 wristband) and include the same metrics for EDA, BVP, and HR.…”

Section: Performance Comparison With Related Studiesmentioning

confidence: 82%

“…In line with these theories and consistently with the operationalization adopted in our previous study Girardi et al (2020a), we use emotions as a proxy for users' engagement during interviews. Our choice is further supported by previous empirical findings demonstrating how emotions can be leveraged for detecting engagement in speech-based analysis of conversations (Yu et al, 2004) or to detect students' motivation (Barhenke et al, 2011).…”

Section: Engagement and Emotionsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

“…This paper builds upon a previous conference contribution by the same authors (Girardi et al, 2020a), in which only the biofeedback signals were used for prediction. The current paper repeats and expands the experiments from Girardi et al (2020a). In particular, we introduce additional biometric features, based on voice signals, as well as additional data preparation options-namely standard scaling, oversampling and data imputation-that allow us to radically improve the previous results, even with voice features alone.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews

Ferrari¹,

Huichapa²,

Spoletini³

et al. 2021

Preprint

Self Cite

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Capturing users' engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collect and analyze users' feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in bespoke software development, or in contexts in which one needs to gather information from specific users. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biometric data, in terms of physiological and voice features, to complement interviews with information

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Section: Performance Comparison With Related Studiesmentioning

confidence: 82%

Section: Engagement and Emotionsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews

Ferrari¹,

Huichapa²,

Spoletini³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Engagement and Emotionsmentioning

confidence: 98%

Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews

Ferrari¹,

Huichapa²,

Spoletini³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Capturing users’ engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collect and analyze users’ feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in bespoke software development, or in contexts in which one needs to gather information from specific users. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biometric data, in terms of physiological and voice features, to complement interviews with information about the engagement of the user on the discussed product-relevant topics. We evaluate our approach by interviewing users while gathering their physiological data (i.e., biofeedback) using an Empatica E4 wristband, and capturing their voice through the default audio-recorder of a common laptop. Our results show that we can predict users' engagement by training supervised machine learning algorithms on biometric data, and that voice features alone can be sufficiently effective. The performance of the prediction algorithms are maximised when pre-processing the training data with the synthetic minority oversampling technique (SMOTE). The results of our work suggest that biofeedback and voice analysis can be used to facilitate prioritization of requirements oriented to product improvement, and to steer the interview based on users' engagement. Furthermore, the usage of voice features can be particularly helpful for emotion-aware requirements elicitation in remote communication, either performed by human analysts or voice-based chatbots.

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RoboREIT: An interactive robotic tutor with instructive feedback component for requirements elicitation interview training

Görer,

Aydemir

2023

J Software Evolu Process

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Interviewing stakeholders is the most popular technique for eliciting requirements. The success of an interview depends on the interviewer's theoretical knowledge, preparedness, and communication skills. Practice interviews allow students to apply their knowledge and improve their skills through experience. This practical training is resource‐intensive, requiring the time and effort of a stakeholder for each student, which may not be feasible for a large number of students. This paper introduces RoboREIT, an interactive Robotic tutor for Requirements Elicitation Interview Training. RoboREIT addresses the scalability problem of practice sessions with a robotic tutor acting as a stakeholder during the interview and providing feedback after the interview. We performed an exploratory user study to evaluate RoboREIT and demonstrate its applicability in requirements elicitation interview training. The quantitative and qualitative analyses of the users' responses reveal the appreciation of RoboREIT. Our study is the first in the literature that utilizes a social robot in requirements elicitation interview education. RoboREIT's design incorporates replaying faulty interview stages and allows the student to learn from mistakes by a second time practicing. All participants praised the feedback component, which is not present in the state of the art, for being helpful in identifying the mistakes. A favorable response rate of 81% for the system's usefulness indicates the positive perception of the participants.

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The Way it Makes you Feel Predicting Users’ Engagement during Interviews with Biofeedback and Supervised Learning

Cited by 5 publications

References 79 publications

Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews

Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews

Using Voice and Biofeedback to Predict User Engagement during Requirements Interviews

RoboREIT: An interactive robotic tutor with instructive feedback component for requirements elicitation interview training

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