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Automatic identification of storytelling responses to past‐behavior interview questions via machine learning
Auteur(s)
Skanda Muralidhar
Emmanuelle P. Kleinlogel
Daniel Gatica‐Perez
Date de parution
2023
In
International Journal of Selection and Assessment
Vol.
31
No
3
De la page
376
A la page
387
Revu par les pairs
true
Résumé
<jats:title>Abstract</jats:title><jats:p>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.</jats:p>
Identifiants
Type de publication
journal article
