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Who's afraid of job interviews? Definitely a Question for User Modelling

Who's afraid of job interviews? Definitely a Question for User Modelling. Kaśka Porayska-Pomsta, Paola Rizzo, Ionut Damian, Tobias Baur, Elisabeth André, Nicolas Sabouret, Hazael Jones, Keith Anderson, Evi Chryssafidou. Context. Young people Not in Education, Employment or Training (NEET)

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Who's afraid of job interviews? Definitely a Question for User Modelling

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  1. Who's afraid of job interviews? Definitely a Question for User Modelling Kaśka Porayska-Pomsta, Paola Rizzo, Ionut Damian, Tobias Baur, Elisabeth André, Nicolas Sabouret, Hazael Jones, Keith Anderson, Evi Chryssafidou

  2. Context • Young people Not in Education, Employment or Training (NEET) • 18-25 years old • High risk of social exclusion • In EU: ~20%. • Lack of social skillsfor job interviews

  3. Context Continues • TARDIS European project: • serious game for practicing job interviews with a virtual recruiter

  4. User Modelling in TARDIS • user modelling in job interviews: • need to detect youngsters’ social cues • infer complex mental states from social cues in real time to tailor the virtual recruiter’s behaviour • Display the information to the youngsters and helping practitioners openssi.net

  5. Questions and studies • What social cues and mental states are relevant? • What is feasible to detect with non-intrusive technology? • What aspects of the interaction lead to (detectable) nonverbal behaviours in users? • How to evaluate anxiety? 4 types of studies: • field-based human-to-human • field-based computer-mediated human-to-human • lab-based WOZ • field-based human-to-agent

  6. Goal: identify social cues and hidden mental states video recorded interactions among 10 youngsters and 5 practitioners Post-hoc video walkthroughs with practitioners Manual annotation of social cues and mental states identification of 19 cues and 8 mental states 1. Human-to-human job interviews

  7. 1. Human-to-human job interviews • 8 Mental States: • Stressed • Embarassed • Hesitant • Ill-at-ease • Bored • Focused • Relieved • Relaxed

  8. 2. Computer-mediated human-to-human • Goal: verify automatic detection of social cues • mock interviews (5 youngsters, 2 practitioners) mediated through video link and headsets • cues recorded by social cue recognition component using Kinect & mics • refinement of social cues according to sensitivity of devices, background noise, available software libraries

  9. SSI-NoVA display shows no cues!

  10. Goals: try sensors that could enhance cue recognition ascertain the impact of specific questions on participants sensors: Kinect, headset, eye tracking glasses, motion tracking glove, SC/BVP sensors subjects: 3 university students 3. Lab-based Wizard of Oz

  11. 3. Lab-based Wizard of Oz • even with the more challenging scenario, users still performed very few and small physical movements • SC values showed the impact of the interview questions on users • e.g. “What are your weaknesses?” or “I don't think you are right for this job” correlated with higher SC values

  12. 4. Field-based human-to-agent • Goal: pilot a pop-up questionnaire to elicit self-reports about anxiety • 7 subjects, 2 virtual recruiters: “Demanding” vs “Understanding” • 3 question categories: (i) skills required,(ii) knowledge of the job, (iii) salary sought

  13. 4. Field-based human-to-agent • small sample with no statistically significant effects of: • 2 recruiter conditions • three question categories: (i)skills required, (ii) knowledge of the job,(iii) salary level sought • nevertheless, 2 possible trends: • trait anxiety • some types of questions may lead to greater anxiety

  14. Conclusions • one lesson learnt: non-intrusive sensors in field conditions, and the emotion suppression in this interaction domain, lead to a reduced set of detectable cues • need for an initial training phase of the user model during which individual users' baseline of social cues can be established • allows for a tailored parameter adjustment based on the frequency of a given users' cues • users' behaviours are compared to their typical baseline and peak behaviours are identified

  15. Focus on key social cues, such as voice, that can be reliably detected through the sensing technologies, coupled with a reduced focus on state anxiety • A complementary approach, currently piloted: open user modelling –the models generated online are displayed to the users who can accept or correct them according to their self-perception • this allows to both validate TARDIS' user models and to foster self-awareness in the youngsters - a pre-requisite job interview skill

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