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Journal Articles International Journal of Selection and Assessment Year : 2023

Automatic identification of storytelling responses to past‐behavior interview questions via machine learning

Abstract

Structured interviews often feature past-behavior questions, where applicants are asked to tell a story about past work experience. Applicants often experience difficulties producing such stories. Automatic analyses of applicant behavior in responding to past-behavior questions may constitute a basis for delivering feedback and thus helping them improve their performance. We used machine learning algorithms to predict storytelling in transcribed speech of participants responding to past-behavior questions in a simulated selection interview. Responses were coded as to whether they featured a story or not. For each story, utterances were also manually coded as to whether they described the situation, the task/action performed, or results obtained. The algorithms predicted whether a response features a story or not (best accuracy: 78%), as well as the count of situation, task/action, and response utterances. These findings contribute to better automatic identification of verbal responses to past-behavior questions and may support automatic provision of feedback to applicants about their interview performance.
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Origin : Publication funded by an institution
Licence : CC BY NC - Attribution - NonCommercial

Dates and versions

hal-04131007 , version 1 (29-09-2023)

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Attribution - NonCommercial

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Adrian Bangerter, Eric Mayor, Skanda Muralidhar, Emmanuelle Kleinlogel, Daniel Gatica-Perez, et al.. Automatic identification of storytelling responses to past‐behavior interview questions via machine learning. International Journal of Selection and Assessment, 2023, ⟨10.1111/ijsa.12428⟩. ⟨hal-04131007⟩
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