Enhancement of a Gamified Situational Judgment Test Scoring System for Behavioral Assessment

• Published in: 2022 International Conference on Advanced Learning Technologies (ICALT) pp. 374 - 378
• Main Authors: Hernandez, Jerome, Muratet, Mathieu, Pierotti, Matthis, Carron, Thibault
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2022
• Subjects: Behavioral sciences; Calibration; Companies; computational psychometrics; Data science; Force; Machine learning; personnel selection; scoring matrix; serious game; Sociology; soft skills assessment
• ISSN: 2161-377X
• DOI: 10.1109/ICALT55010.2022.00116

Computational psychometrics data and soft skills recognition have become prevalent means in personnel selection processes. Likewise, companies have shown a growing interest in using computational data and machine learning to predict employee behavior. With the aim to enhance selection strategies and applicant reactions simultaneously, the human resources population is researching and developing reliable and valid tools to fit the right person with the right job. In an innovative approach, gamified situational judgment tests have recently received positive results in behavior assessment in combining the acknowledged traditional situation judgment test approach with the advantages of gamification. To pursue previous work in the field and explore this new area of research, we proposed a novel approach to enhance the reliability and validity of gamified situational judgment test's scoring system based on computational psychometrics data. Our approach has been tested and compared to the initial scoring system of an existing gamified situational judgment test intended to assess bank managers across four soft skills.