Analysis of Job Interview Training Feedback System Effectiveness Based on a Multimodal Machine Learning Model
-
- OHBA Tomoya
- Japan Advanced Institute of Science and Technology
-
- KUROKI Haruki
- Japan Advanced Institute of Science and Technology
-
- MAWALIM Candy Olivia
- Japan Advanced Institute of Science and Technology
-
- OKADA Shogo
- Japan Advanced Institute of Science and Technology
Bibliographic Information
- Other Title
-
- マルチモーダル機械学習モデルに基づく就職活動面接訓練フィードバックシステム効果の分析
Abstract
<p>We built a humanoid agent system for VR experiences and collected a job interview data corpus. The data corpus includes annotations of interview skill scores graded by third-party experts and self-efficacy annotations by the interviewees, for each question-answer. The data corpus contains various kinds of multimodal data, including audio, biological (i.e., physiological), gaze, and language data. In this study, we developed a feedback system for automated job interview training and analyzed the impact of the feedback. The feedback system utilizes a machine learning model that uses acoustic and linguistic features. In the control group, feedback was provided using a book. The results of the comparison of the effects of the proposed system and the book suggested that the proposed feedback system could suppress the self-confidence of the group that tended to overestimate their performance when compared with the book.</p>
Journal
-
- Proceedings of the Annual Conference of JSAI
-
Proceedings of the Annual Conference of JSAI JSAI2023 (0), 3Q1OS19a05-3Q1OS19a05, 2023
The Japanese Society for Artificial Intelligence
- Tweet
Details 詳細情報について
-
- CRID
- 1390578283198050048
-
- ISSN
- 27587347
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
-
- Abstract License Flag
- Disallowed