BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:US/Eastern
BEGIN:DAYLIGHT
DTSTART:20210314T030000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:EDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20211107T010000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:EST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20220123T221204Z
UID:EE6E7E35-D7DE-4F24-ADFC-5FD10A0CFBC8
DTSTART;TZID=US/Eastern:20210324T190000
DTEND;TZID=US/Eastern:20210324T203000
DESCRIPTION:The measurement of student engagement in STEM courses is invest
 igated using machine learning and biometrics to measure the emotional and 
 behavioral states of students in the classroom. The approach collects mult
 i-dimensional biometrics via camera and wristband monitors of facial expre
 ssions\, eye gaze\, hand/head/body movement\, and heart rate. From these d
 ata\, a software model is trained to classify student engagement. Engageme
 nt is classified from behavioral and emotional states with cognitive engag
 ement inferred by machine learning. The ability to measure student engagem
 ent in real time can be used by the instructor to tailor the presentation 
 of material in class\, identify course material that engages and disengage
 s with students\, and identify students that are engaged\, or disengaged a
 nd at risk of failure. Further\, this approach allows quantitative compari
 son of teaching methods\, such as lecture\, flipped classrooms\, classroom
  response systems\, etc. such that an objective metric can be used to clos
 e the loop on teaching evaluation.\n\nThis work has been funded by the NSF
 .\n\nSpeaker(s): Dr. Chris Foreman\, \n\nVirtual: https://events.vtools.ie
 ee.org/m/266154
LOCATION:Virtual: https://events.vtools.ieee.org/m/266154
ORGANIZER:adozier@awdozier.com
SEQUENCE:2
SUMMARY:Measurement of student engagement in the STEM classroom using machi
 ne learning and biometrics
URL;VALUE=URI:https://events.vtools.ieee.org/m/266154
X-ALT-DESC:Description: <br /><p>The measurement of student engagement in S
 TEM courses is investigated using machine learning and biometrics to measu
 re the emotional and behavioral states of students in the classroom. The a
 pproach collects multi-dimensional biometrics via camera and wristband mon
 itors of facial expressions\, eye gaze\, hand/head/body movement\, and hea
 rt rate. From these data\, a software model is trained to classify student
  engagement. Engagement is classified from behavioral and emotional states
  with cognitive engagement inferred by machine learning. The ability to me
 asure student engagement in real time can be used by the instructor to tai
 lor the presentation of material in class\, identify course material that 
 engages and disengages with students\, and identify students that are enga
 ged\, or disengaged and at risk of failure. Further\, this approach allows
  quantitative comparison of teaching methods\, such as lecture\, flipped c
 lassrooms\, classroom response systems\, etc. such that an objective metri
 c can be used to close the loop on teaching evaluation.</p>\n<p>This work 
 has been funded by the NSF.</p>
END:VEVENT
END:VCALENDAR