A preliminary system for recognizing boredom

Jacobs, Allison; Fransen, Benjamin R.; McCurry, J. Malcolm; Heckel, Frederick W. P.; Wagner, Alan R.; Trafton, J. Gregory

doi:10.1145/1514095.1514185

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…This approach, however, relies on the honesty of the students, has the potential of interrupting the primary task of learning, and cues students to the fact that the tutor is explicitly monitoring their boredom levels. Alternatively, sensors that track facial features, physiology, reaction time, and other informational channels could be used to corroborate the gazebased diagnosis of boredom (Beck, 2005;Cocea & Weibelzahl, 2009;D'Mello & Graesser, 2010b;Drummond & Litman, 2010;Jacobs et al, 2009).…”

Section: Limitations and Future Workmentioning

confidence: 99%

Gaze tutor: A gaze-reactive intelligent tutoring system

D’Mello

Olney

Williams

et al. 2012

International Journal of Human-Computer Studies

273

100

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We developed an Intelligent Tutoring System (ITS) that aims to promote engagement and learning by dynamically detecting and responding to students' boredom and disengagement. The tutor uses a commercial eye tracker to monitor a student's gaze patterns and identify when the student is bored, disengaged, and has zoned out. The tutor then attempts to reengage the student with dialogue moves that direct the student to reorient his or her attentional patterns towards the animated pedagogical agent embodying the tutor. We evaluated the efficacy of the gaze-reactive tutor in promoting learning, motivation, and engagement in a controlled experiment where 48 students were tutored on four biology topics with both gaze-reactive and non gaze-reactive (control condition) versions of the tutor. The results indicated that: (a) gaze-sensitive dialogues were successful in dynamically reorienting students' attentional patterns to the important areas of the interface, (b) gaze-reactivity was effective in promoting learning gains for questions that required deep reasoning, (c) gaze-reactivity had minimal impact on students' state motivation and on self-reported engagement, and (d) individual differences in scholastic aptitude moderated the impact of gaze-reactivity on overall learning gains. We discuss the implications of our findings, limitations, future work, and consider the possibility of using gaze-reactive ITSs in classrooms.

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Section: Limitations and Future Workmentioning

confidence: 99%

Gaze tutor: A gaze-reactive intelligent tutoring system

D’Mello

Olney

Williams

et al. 2012

International Journal of Human-Computer Studies

273

100

View full text Add to dashboard Cite

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“…These categories are based primarily on physical actions, and coded from videos taken of the entire experiments, a technique used in coding attention states in previous studies [71,72]. We recognize that they are not absolute in that just because someone was looking at a screen, this does not mean he or she was cognitively engaged with a UAV control activity.…”

Section: Attention Statesmentioning

confidence: 99%

Boredom and Distraction in Multiple Unmanned Vehicle Supervisory Control

Cummings¹,

Mastracchio²,

Thornburg³

et al. 2013

Interacting With Computers

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Operators currently controlling Unmanned Aerial Vehicles report significant boredom, and such systems will likely become more automated in the future. Similar problems are found in process control, commercial aviation, and medical settings. To examine the effect of boredom in such settings, a long duration low task load experiment was conducted. Three low task load levels requiring operator input every 10, 20, or 30 minutes were tested in a four-hour study using a multiple unmanned vehicle simulation environment that leverages decentralized algorithms for sometimes imperfect vehicle scheduling. Reaction times to system-generated events generally decreased across the four hours, as did participants' ability to maintain directed attention. Overall, participants spent almost half of the time in a distracted state. The top performer spent the majority of time in directed and divided attention states.Unexpectedly, the second-best participant, only 1% worse than the top performer, was distracted almost one third of the experiment, but exhibited a periodic switching strategy, allowing him to pay just enough attention to assist the automation when needed. Indeed, four of the five top performers were distracted more than one-third of the time. These findings suggest that distraction due to boring, low task load environments can be effectively managed through efficient attention switching. Future work is needed to determine optimal frequency and duration of attention state switches given various exogenous attributes, as well as individual variability. These findings have implications for the design of and personnel selection for supervisory control systems where operators monitor highly automated systems for long durations with only occasional or rare input.

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