who's afraid of job interviews? definitely a question for user modelling
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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) - PowerPoint PPT PresentationTRANSCRIPT
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
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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 skills for job
interviews
Context Continues
• TARDIS European project: – serious game for practicing job interviews with a virtual
recruiter
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
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
1. Human-to-human job interviews
• 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
8 Mental States:•Stressed•Embarassed•Hesitant•Ill-at-ease•Bored•Focused•Relieved •Relaxed
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
SSI-NoVA display shows no cues!
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3. Lab-based Wizard of Oz
• 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
• 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
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
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
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
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• 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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