A Research Model For Automated Prediction And Analysis Of Job Interview Performance

One of the most important steps in the recruitment procedure is the interview. Job seekers typically practice with each other in mock interviews to ensure they are fully prepared for the interview with the recruiters. Trainee interviewers can learn and practice questioning techniques in a safe environment by using mock (simulated) interviews. Studies reveal that when mock interviews are designed in accordance with the body of scientific research on the best settings for learning for questioning skills, interviewers develop useful and enduring skills. However, earlier practice interviews conducted with peers are typically very different from the real interview process; for example, the fake interviewers may not always act properly or in a way that is representative of a real interview. Hence, in this work a research model for Automated prediction and analysis of job interview performance is presented. This analysis leverages advanced technology to provide students with a comprehensive and personalized mock interview experience. This innovative Sample Mock Interview Project revolutionizes the way candidates prepare for interviews. By offering a realistic, personalized, and insightful experience, it empowers users to refine their skills, boost their confidence, and excel in real-world interview situations. Additionally, users can get feedback on the manner in which they performed in the fake interview by using features like volume, head nodding, communication, and facial preference. The performance of presented mock interview is evaluated in terms of Tension Rate, Time Consumption and Practice Cost.