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PRODID:Linklings LLC
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TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
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DTSTART:19700308T020000
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DTSTART:19701101T020000
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BEGIN:VEVENT
DTSTAMP:20251016T135128Z
LOCATION:Grand Hall M/N
DTSTART;TZID=America/Chicago:20251016T165000
DTEND;TZID=America/Chicago:20251016T171000
UID:HFESAM_ASPIRE 2025_sess129_LECT437@linklings.com
SUMMARY:Refining Mathematical Task Difficulty for Accurate Mental Workload
  Estimation
DESCRIPTION:Jingkun Wang, Dongyang Li, Emily Garcia, Eugenio Frias-Miranda
 , Zhengming Zhang, and Denny Yu (Purdue University)\n\nAccurately assessin
 g mental workload (MWL) is essential for optimizing task performance. Alth
 ough mathematical operations are widely used to induce MWL due to their sc
 alability, few studies provide clear criteria for defining difficulty leve
 ls. This study proposes a systematic classification method based on cognit
 ive demand, defined by the number of interim values stored during mental c
 alculation stages. Six math difficulty levels were developed and tested in
  a controlled experiment with 26 participants. Task performance and eye-tr
 acking metrics were recorded. Results showed that the number of digits and
  carrying operations significantly influenced performance, particularly fo
 r simpler problems. However, for complex problems, increased operations di
 d not yield distinguishable MWL levels, suggesting cognitive resource satu
 ration. Four distinct difficulty levels emerged based on performance, whil
 e eye-tracking data revealed fewer significant differences. The average nu
 mber of interim values stored per stage better predicted MWL than total in
 terim values, forming three cognitive demand tiers. These findings suggest
  that not all mathematical problems elicit distinguishable MWL differences
  and highlight the importance of task structure in MWL classification. Thi
 s work contributes to refining MWL assessment methodologies and supports t
 he development of better cognitive workload modeling using structured math
  problems.\n\nTrack: Augmented Cognition\n\nSession Chairs: Jason Sanders 
 (San José State University) and Michael Hildebrandt (Institute for Energy 
 Technology, Halden Project)\n\n
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