An Explorative Analysis of the Feasibility of Implementing Metacognitive Strategies in Self-Regulated Learning with the Conversational Agents
Author(s):Smit Desai University of Illinois at Urbana-Champaign; Jessie Chin University of Illinois at Urbana-Champaign
Abstract: With the prevalence of commercially available conversational agents (CAs) little research examined the capacities and constraints of these devices to support adults to learn new information on their own. The article conducted systematic analysis on the commercially available CAs (using work domain analysis and literature review) synthesized the metacognitive strategies that have been implemented in the computer-based learning environments and examined the feasibility to implement these strategies on CAs to support self-regulated learning. This study has implications on designing scalable evidence-based theory-driven educational applications to support users to learn new information on their own using the off-the-shelf devices.

Bridging Psychology and Engineering: Undergraduate Conceptions of Human Systems Engineering
Author(s):Rod Roscoe Arizona State University; Samuel Arnold Arizona State University; Ashley Clark Arizona State University
Abstract: Instruction and coursework that link engineering and psychology may enable future engineers to better understand the people they are engineering for (e.g. users and clients) and themselves as engineers (e.g. teammates). In addition human-centered engineering education may empower engineering students to better solve problems at the intersection of technology and people. In this study we surveyed students’ conceptions and attitudes toward human systems engineering. We aggregate responses across three survey iterations to discuss students’ knowledge and beliefs and to consider instructional opportunities for introductory courses.

Educational Data Mining and Learning Analytics for Improving Online Learning Environments
Author(s):Yancy Vance Paredes Arizona State University; Robert Siegle Arizona State University; I-Han Hsiao Arizona State University; Scotty Craig Arizona State University
Abstract: The proliferation of educational technology systems has led to the advent of a large number of datasets related to learner interaction. New fields have emerged which aim to use this data to identify interventions that could help the learners become efficient and effective in their learning. However these systems have to follow user-centered design principles to ensure that the system is usable and the data is of high quality. Human factors literature is limited on the topics regarding Educational Data Mining (EDM) and Learning Analytics (LA). To develop improved educational systems it is important for human factors engineers to be exposed to these data-oriented fields. This paper aims to provide a brief introduction to the fields of EDM and LA discuss data visualization and dashboards that are used to convey results to learners and finally to identify where human factors can aid other fields.

Human-Centered and Psychological Concepts in Undergraduate Engineering Project Documentation
Author(s):Rod Roscoe Arizona State University; Samuel Arnold Arizona State University; Chelsea Johnson Arizona State University
Abstract: The success of engineering and design are facilitated by a working understanding of human thoughts feelings and behaviors. In this study we explored how undergraduate engineering students included such human-centered and psychological concepts in their project documentation. Although we observed a range of concepts related to design processes teams cognition and motivation these concepts appeared infrequently and superficially. We discuss how this analysis and approach may help to identify topics that could be leveraged for future human-centered engineering instruction.

Investigating the Effects of Demographics and Framing on Robot-theater Summer Youth Programs
Author(s):Chihab Nadri Virginia Polytechnic Institute and State University; Jiayuan Dong Virginia Polytechnic Institute and State University; Haley Swaim Virginia Polytechnic Institute and State University; Sangjin Ko Virginia Polytechnic Institute and State University; Harsh Sanghavi Virginia Polytechnic Institute and State University; Myounghoon Jeon Virginia Tech
Abstract: While robot-theater and other STEAM education programs have shown promise in increasing interest in STEM fields the effects of demographic and other contextual factors have not been thoroughly investigated yet. While conducting robot-theater summer youth sessions with forty participants of the TechGirls international summer exchange program we explored these factors. Participants in teams of four to six students were tasked with creating a script for a theater play that required the use of programmable robots. Results seem to suggest the influence of demographic factors such as nationality as well as the effect of framing on participant attitudes towards robots and STEAM education. Subsequent validation of these effects in other studies is expected to contribute to refining the design of robot-theater and other STEAM education programs.

The Influence of Voice on Pedagogical Agent's Persona and Recall Performance
Author(s):Thomas Morris Eastern Kentucky University; Michael Chen Eastern Kentucky University
Abstract: Despite the prevalence of computer-generated speech few studies have investigated the direct relationship between an agent's voice and students’ perception or recall performance. This study investigated the effects of voice (without visual information) on students’ perception ratings and recall performance. Our results indicated that in the absence of visual information students greatly preferred the human voice. The recall performance however indicated that the synthesized voices led to better recall performance. Implications for pedagogical practices will be discussed.

Applying Design Thinking: Tales from the Field
Author(s):Scott Confer Infosys; Sanjay Batra Google; Hugues Belanger 1904labs
Abstract: It has been over 30 years since The Design of Everyday Things was published by Don Norman. An increasingly popular approach of “Design Thinking” owes much of its popularity to Norman’s notion of “Human-Centered Design.” Design thinking at its core empowers everyone on a team to “think like a designer” with an array of creativity approaches to solve complex problems. This panel will focus on the utility of Design Thinking by bringing together viewpoints from three experience designers with over 60 years of combined tenure creating products and services. By discussing the challenges in each of their respective industries of business consulting internet software and hardware development the panelists will share their experiences how design thinking can be applied and extended.

Assessing Psychosocial and Personal Factors in Industrial Work: Issues and Challenges for the Occupational Ergonomics Practitioner and Researcher
Author(s):Robert Fox General Motors; Missie Smith Oakland University; Carisa Harris Adamson University of California San Francisco & Berkeley; Menekse Barim NIOSH; Ming-Lun Lu NIOSH; Sean Gallagher Auburn University; Jeannie Nigam NIOSH; Stephen Bao Washington State Department of Labor and Industries SHARP Program
Abstract: The occupational ergonomics practitioner has focused on physical aspects of industrial jobs (e.g. forces exerted joint moments repetitive motion etc.). However some studies have shown that physical risk factors alone do not account for a majority of the risk of many musculo-skeletal injuries. Psychosocial factors have been identified as important in understanding the incidence of many occupational musculo-skeletal disorders. Psychosocial and personal/individual factors have supplemented biomechanical assessments in studies of occupational injury. Others have examined incorporating personal/individual factors into risk assessment methods to give a more complete picture of injury risk that an individual or a subgroup of workers (e.g. gender age prior injury) may face on jobs.
This discussion panel will explore the research in the fields of psychosocial and personal risk factors and their relevance to the assessment of injury risk. This session is relevant for the practitioners who must apply the results of research to real-world problems.

- Providing a Foundation for Interpretable Autonomous Agents through Elicitation and Modelling of Criminal Investigation Pathways
Author(s):Sam Hepenstal Defence Science Technology Laboratory
Abstract: Criminal investigations are guided by repetitive and

Attentional Control in Young Drivers: Does Training Impact Hazard Anticipation in Dynamic Environments?
Author(s):Sarah Yahoodik Old Dominion University; Yusuke Yamani Old Dominion University
Abstract: The interaction between top-down and bottom-up processing is a way to characterize control of visual attention but it has not been extensively applied to the driving domain. The Risk Awareness and Perception Training (RAPT) has been effective in improving drivers’ latent hazard anticipation a top-down process. However it is unclear whether RAPT protects drivers from being distracted by salient items on the roadway diminishing latent hazard anticipation. The current driving simulator study examines the potential interaction between bottom-up and top-down processes by having RAPT- and Placebo-trained drivers navigate simulated environments with latent hazards and a stationary or dynamically moving pedestrian. While RAPT-trained drivers were better able to anticipate latent hazards than Placebo-trained drivers presence of salient bottom-up stimuli did not negatively impact hazard anticipation performance in either group. This implies RAPT-trained drivers were able to successfully divide their attention anticipating latent hazards even in the presence of dynamic driving-relevant objects.

Driver Visual Processing of Relevant and Irrelevant Information During Mind Wandering
Author(s):Richard Wagner North Carolina State University; Michael Geden North Carolina State University; Jing Feng NCSU; Sophie Forster University of Sussex
Abstract: Mind wandering is a common phenomenon in our daily lives especially in routine tasks such as driving familiar routes. Some evidence suggests that there are detrimental effects of mind wandering on driving performance but limited research has been conducted to examine the influence of mind wandering on a driver’s attentional processing of relevant or irrelevant information. More specifically it is unclear as to whether the effects of mind wandering depend on the task relevancy of information presented in the visual field. The current study expands literature on mind wandering during driving using eye tracking to measure driver visual processing of relevant/irrelevant signage information in a simulated driving task while drivers reported their mental states. Preliminary results showed no significant differences in frequency and duration of glances to roadway information based on the mental state of the individual as well as the task relevancy of the information. Implications and future directions are discussed.

Individual Differences in Glance Patterns under Distraction in Level 2 Automated Driving
Author(s):Shiyan Yang Seeing Machines; Jonny Kuo Seeing Machines; Michael Lenne Seeing Machines
Abstract: This paper investigated individual differences in attentional strategies during the non-driving-related tasks in Level 2 automated driving. Ward’s method was used to cluster participants into different groups according to the characteristics of their sequential off-road glances in the email-sorting task: duration frequency variance and intensity. The clustering results showed two types of sequential off-road glance patterns in distracted Level 2 driving: infrequent long glances vs. frequent short glances. However participants in the two groups showed similar workload driving engagement and email-sorting accuracy. They also reported similar feelings of safety and feedback on Level 2 vehicle automation. These findings demonstrated the complexity of driver attentional strategies across individuals in automated driving which is a necessary aspect of driver state to be monitored in real-time.

Investigating the Effect of Education and Drowsiness Detection on Nurses' Beliefs and Attitudes towards Drowsy Driving
Author(s):Alec Smith Texas A&M University; Farzan Sasangohar Texas A&M University; Anthony McDonald Texas A&M
Abstract: Drowsy Driving is a common yet dangerous problem among nurses with various studies conducted to investigate how to mitigate this issue. While technological interventions show promise there is a gap in understanding nurses’ expectations from such technologies and how such beliefs affect their intention to use technological interventions. To address this issue an integrated model drawing from the constructs of the theory of planned behavior and health belief model was used to elicit nurses’ beliefs in a large health system. Forty-four nurses were split into control educational intervention and education plus technology intervention groups then had their beliefs compared between the beginning and end of the study. Behavioral intention was accurately predicted primarily by attitude and perceived health threat. The results also showed that the intervention groups had more noticeable changes in beliefs than the control group.

Police Officer Interactions with In-vehicle Technologies: An On-Road Investigation
Author(s):Farzaneh Shahini Texas A&M university; Marayam Zahabi Texas A&M university; Benjamin Patranella Texas A&M university; Ashiq Mohammed Abdul Razak Texas A&M university
Abstract: :Police motor vehicle crashes are a leading cause of officers’ fatalities in line of duty. These crashes have been attributed not only to driving at high speed in emergency situations but more importantly to interaction
with different in-vehicle technologies. Prior studies in this domain have been limited to specific equipment
and short exposure time and were typically conducted in laboratory settings with simulated environment or
tasks which limit their generalizability to actual police operations. The objective of this study was to identify
the most frequently used and cognitively demanding in-vehicle technologies for police officers while driving.
Ten officers participated in a three-hour ride-along study. Findings suggested that the mobile computer
terminal is the most frequently used and visually and cognitively demanding in-vehicle technology for police
officers. Other factors such as work shift duration and average time spent in the vehicle per shift can also
affect workload. The results indicated the need for improvements in in-vehicle technology design and
implementation officer training protocols and departmental policies in order to reduce officers’ mental
workload and improve safety in police operations.

Designing an Augmented Reality Based Interface for Wearable Exoskeletons
Author(s):Chaitanya Kulkarni Virginia Tech; Hsiang-Wen Hsing Virginia Tech; Dina Kandil Virginia Tech; Shriya Kommaraju Virginia Tech; Nathan Lau Virginia Tech; Divya Srinivasan Virginia Tech
Abstract: Full-body powered wearable exoskeletons combine the capabilities of machines and humans to maximize productivity. Powered exoskeletons can ease industrial workers in manipulating heavy loads in a manner that is difficult to automate. However introduction of exoskeletons may result in unexpected work hazards due to the mismatch between user-intended and executed actions thereby creating difficulties in sensing the physical operational envelope need for increased clearance and maneuverability limitations. To control such hazards this paper presents a rearview human localization augmented reality (AR) platform to enhance spatial awareness of people behind the exoskeleton users. This platform leverages a computer vision algorithm called Monocular 3D Pedestrian Localization and Uncertainty Estimation (MonoLoco) for identifying humans and estimating their distance from a video camera feed and off-the-shelf AR goggles for visualizing the surrounding. Augmenting rear view awareness of humans can help exoskeleton users to avoid accidental collisions that can lead to severe injuries.

Detection and Mitigation of Inefficient Visual Searching
Author(s):Alex Kamrud United States Air Force; Josh Gallaher ; Brett Borghetti Air Force Institute of Technology
Abstract: A commonly known cognitive bias is a confirmation bias: the overweighting of evidence supporting a hypothesis and underweighting evidence countering that hypothesis. Due to high-stress and fast-paced operations military decisions can be affected by confirmation bias. One military decision task prone to confirmation bias is a visual search. During a visual search the operator scans an environment to locate a specific target. If confirmation bias causes the operator to scan the wrong portion of the environment first the search is inefficient. This study has two primary goals: 1) detect inefficient visual search using machine learning and Electroencephalography (EEG) signals and 2) apply various mitigation techniques in an effort to improve the efficiency of searches. Early findings are presented showing how machine learning models can use EEG signals to detect when a person might be performing an inefficient visual search. Four mitigation techniques were evaluated: a nudge which indirectly slows search speed a hint on how to search efficiently an explanation for why the participant was receiving a nudge and instructions to in-struct the participant to search efficiently. These mitigation techniques are evaluated revealing the most effective mitigations found to be the nudge and hint techniques.

Dynamic Causal Modeling of Gender Differences in Emotion: Implications for Augmented Cognition
Author(s):Jiali Huang North Carolina State University; Chang Nam North Carolina State University; Kristen Lindquist
Abstract: The goal of this study is to investigate the neural basis of gender difference in emotion processing. Electroencephalogram (EEG) signals were recorded when the same set of emotion-eliciting images was shown to male and female participants. Neural connections were estimated using Dynamic Causal Modeling (DCM) and results for both genders were compared. We found that dorsolateral prefrontal cortex exerts modulatory effects differently for males and females. These findings on the gender differences in neural mechanisms of emotion processing may be utilized in applications of the augmented cognition program.

Emotion Recognition with a CNN using Functional Connectivity-based EEG Features
Author(s):Chang Nam North Carolina State University; Sanghyun Choo North Carolina State University
Abstract: Emotion recognition plays a pivotal role in our life since it directly affects decision making. To recognize emotion power-based EEG image features have been used for a Convolutional Neural Network (CNN) classifier. However the power-based EEG features use spectral information without considering information flows between channels. To overcome the limitation of the power-based EEG features for emotion classification we propose a CNN-based emotion recognition using Functional Connectivity (FC)-based EEG feature including spatial spectral and temporal information. Forty-three participants engaged in an International Affective Picture System (IAPS)-based emotion experiment that included three emotions (fear sad neutral). The proposed framework was tested in the following two cases in within-subject and cross-subject: (1) binary-class (negative neutral) (2) multi-class (fear sad neutral) with FC-based EEG features according to frequency bands (?????). The results of the study showed that the CNN classifier using an FC-based EEG feature with alpha oscillation had the highest classification accuracy.

How Long Can a Driver (Safely) Glance at an Augmented-Reality Head-Up Display?
Author(s):Nayara De Oliveira Faria Virginia Tech; Joseph Gabbard Virginia Tech
Abstract: Augmented-Reality (AR) head-up display (HUD) is one of the promising solutions to reduce distraction potential while driving and performing secondary visual tasks; however we currently don’t know how to effectively evaluate interfaces in this area. In this study we show that current visual distraction standards for evaluating in-vehicle displays may not be applicable for AR HUDs. We provide evidence that AR HUDs can afford longer glances with no decrement in driving performance. We propose that the selection of measurement methods for driver distraction research should be guided not only by the nature of the task under evaluation but also by the properties of the method itself.

More than Means: Characterizing Individual Differences in Pupillary Dilations
Author(s):Ciara Sibley Naval Research Laboratory; Cyrus Foroughi Naval Research Laboratory; Noelle Brown Naval Research Laboratory; Henry Phillips NAMI; Sabrina Drollinger ; Michael Eagle ; Joseph Coyne Naval Research Laboratory
Abstract: This study sought to characterize individual differences in pupillary dilations during a simple cognitive task. Eighty-four Navy and Marine Corps student pilots performed a digit memory recall test while their pupillary data were recorded. Results showed that peak pupil sizes significantly increased with difficulty of the memory task however variability in pupillary dilations was substantial with only 51% of individuals’ data corresponding with the aggregate results and dilations varying between participants by as much as 1 millimeter. The analyses presented in this paper illustrate the large individual variability that exists in pupil data between individuals and even within individuals on a trial by trial basis. This work serves as a benchmark for understanding variability in pupillary dilations and encourages follow on work to explore casual mechanisms of differences in pupil dilations across individuals especially before using pupil data for applied purposes.

“We Didn’t Catch That!” Using Voice Text Input on a Mixed-Reality Headset in Noisy Environments
Author(s):Jessyca Derby Embry-Riddle Aeronautical University.; Emily Rickel Embry-Riddle Aeronautical University; Kelly Harris Embry-Riddle Aeronautical University; Jade Lovell Embry-Riddle Aeronautical University; Barbara Chaparro Embry-Riddle Aeronautical University
Abstract: The Microsoft HoloLens a mixed reality head-mounted display (HMD) has been demonstrated in domains such as medicine engineering and manufacturing. In order to interact with the device voice input may be required. Given this range of environments it is necessary to understand the impact of noise on voice dictation speed and accuracy. In this study we evaluated the dictation feature of the HoloLens through speed (WPM) accuracy (WER) perceived workload and perceived usability at three different noise levels: 40 dB 55 dB and 70 dB. No differences were found across noise levels in speed (67-75 WPM) or perceived workload. Accuracy and perceived usability worsened in the 70 dB noise condition. Only 37.5% of participants were able to successfully dictate in the 70 dB condition. This study shows that if the HoloLens is to be accepted in environments with high noise levels improvements to dictation need to be made.

Can Augmented Reality Assist Data Entry Task? A Preliminary Study
Author(s):Taylor Huynh University of Illinois at Chicago; Myunghee Kim University of Illinois at Chicago; Andrew Johnson University of Illinois at Chicago; Heejin Jeong University of Illinois at Chicago; Ankit Singh University of Illinois at Chicago
Abstract: Data entry is considered to be one of the common and essential tasks in workplaces. Almost every industry has a data entry department which is responsible for entering data into the database of the company’s systems. Data entry operators are required to input data from a handwritten paper into the computer systems using a keyboard and a mouse. The repetitive nature of the job that occurs in this scenario from looking at the paper and onto the computer screen induces fatigue in the operators. A study has shown that prolonged work creates cognitive fatigue that affects the cognitive components of the data entry process (Healy et al. 2004). The goal of the project described in this paper is to explore various methods and interfaces for data entry. We report the evaluation results of a data presentation interface introduced in Jeong et al. (2020). This interface was developed using a wearable augmented reality (AR) heads-up display. In the current study the interface was compared with two other interfaces for data presentation which include an extra monitor and a hand-written paper (as a baseline). Eighteen participants were asked to enter the information displayed to them on a separate stand-alone laptop using a keyboard. The participants were told that their performance would be judged based on two parameters: (1) how fast they can complete their tasks (time required to complete the task) and (2) how many errors they commit while typing the information onto the laptop. Better task performance is reflected by the lower values of the two parameters. The participants were presented with different interfaces in a random order to minimize bias. Participants were asked to fill out a NASA-TLX survey and a post-task survey for subjective evaluations of helpfulness preference and ease of use. It was found that AR was not as good for participants as we expected. Participants experienced various difficulties with the current AR interface as this was a novel method of data presentation and participants were more comfortable with something they had more experience with. An interesting take-away from this study was that the AR device performed equally well as the conventional paper-based data presentation method. After conducting this study it was inferred that an AR device could potentially be a good data presentation interface with slight adjustments in its weight and field of view as suggested by the participants.

Effect of Head-Mounted Augmented Reality Devices on Electric Utility Manhole Workers: Neck Muscle Activity and Eye Blink Rate
Author(s):Ashley Toll Milwaukee Tool; Richard Marklin Marquette University; Eric Bauman Electric Power Research Institute; John Simmons Alfred University
Abstract: Two head-mounted augmented reality (AR) systems Microsoft HoloLens and Real-Wear HMT-1 were tested to determine their effect on blink rate and muscle activity of the neck and shoulder muscles of electric utility manhole workers. The task of splicing a cable was performed under three conditions: HoloLens HMT-1 and No AR (normal). Surface electromyography (sEMG) of the right and left sternocleidomastoid splenius semispinalis capitis and upper trapezius muscles were measured on 13 manhole workers and a small camera recorded blink rate of the right eye. Results revealed in general no significant dif-ferences in 50th and 90th percentile sEMG between the three conditions. There was no dif-ference in blink rate between the HMT-1 and No AR but the HoloLens blink rate was 7.8 to 11 blinks/min lower than the HMT-1 for two of the three tasks. A decrease in blink rate of these magnitudes may indicate risk of eye strain to manhole workers who use an OST AR device without appropriate rest breaks. Head-mounted AR devices deployed for under-ground utility workers warrant further study.

Investigating a Virtual Reality-based Emergency Response Scenario and Intelligent User Interface for First Responders
Author(s):Randall Spain Center for Educational Informatics NCSU; Jason Saville Center for Educational Informatics NCSU; Barry Liu NCSU; Donia Slack RTI; Edward Hill RTI International; John Holloway RTI; Sarah Norsworthy RTI; Bradford Mott Center for Educational Informatics NCSU; James Lester Center for Educational Informatics NCSU
Abstract: Virtual reality offers new opportunities to develop and test technology for first responders. Because advances in broadband capabilities will soon allow first responders to access and use many forms of data it is critically important to design head-mounted displays to present first responders with information in a manner that does not induce extraneous mental workload or cause undue system interaction errors. In this paper we describe the development of a virtual reality-based emergency response scenario that was designed to support user experience research for evaluating the efficacy of intelligent user interfaces for firefighters. We describe the results of a usability test that captured firefighter’s feedback and reactions to the VR scenario and prototype intelligent user interface that presented task critical information through the VR headset and conclude with lessons learned from our development process and plans for future research.

Predicting User Performance in Augmented Reality User Interfaces with Image Analysis Algorithms
Author(s):Jonathan Flittner Virginia Polytechnic Institute and State University; John Luksas Virginia Polytechnic and State Institution; Joseph Gabbard Virginia Tech
Abstract: This study applies existing image analysis measures of visual clutter to augmented reality user interfaces and explores other factors on performance; virtual object percentage target object type (real or virtual) and target object clutter. The end goal of this research is to develop an algorithm capable of predicting user performance. Results show significant differences in response time between clutter levels and between virtual object percentage but not target type. Participants consistently had more difficulty finding objects in more cluttered scenes where clutter was determined through image analysis methods and had more difficulty finding virtual of objects when the search area was 50% virtual as opposed to other scenarios. Response time positively correlated to measures of combined clutter (virtual and real) arrays but not for measures of clutter taken of the individual array components (virtual or real) and positively correlated with the clutter scores of the target objects themselves.

The Effects of Target Sizes on Biomechanical Exposures and Perceived Workload during Virtual and Augmented Reality Interactions
Author(s):Kiana Kia Oregon State University; Nizam Hakim MD Ishak Oregon State University; Jaejin Hwang Northern Illinois University; Jeong Ho Kim Oregon State University
Abstract: This repeated-measures laboratory study evaluated and compared muscle activity and postures of the neck and right shoulder as well as NASA TLX perceived workload while a total of 12 participants performed standardized virtual reality (VR) and augmented reality (AR) tasks (Omni-directional pointing square coloring and 3-dimensional cube placement tasks) with three different target sizes. The results showed that AR/VR interactions posed relatively high neck/shoulder muscle activity and shoulder flexion which was in line with moderate-to-high perceived workload (i.e. physical demand and effort measures). The results also showed that target sizes affected these biomechanical and perceived workload measures with a different degree between VR and AR tests. These results indicate that prolonged VR/AR interactions may increase risks for musculoskeletal discomfort in the neck and shoulders. Lastly a target size may be an important design factor in designing AR/VR interfaces to reduce potential neck and should strain.

A Preliminary Investigation of Driver Vigilance in Automated Vehicles in Real Traffic Environments
Author(s):Ruobing Zhao Lamar University; Tianjian Li Lamar University; Yueqing Li Lamar University; Yi Liu Lamar University
Abstract: The study investigated driver vigilance in partially automated vehicles under different traffic density and visibility conditions. Twelve participants “drove” a simulated automated vehicle for 40 min on a two-lane rural highway. Data were aggregated from four different scenarios (2 (Traffic density: Low High) × 2 (Visibility: Clear Foggy). The results showed that the driver's detection accuracy in foggy conditions is significantly higher than in clear conditions. Shorter reaction time was found in low density with clear weather. However the reaction time in high density with clear weather was higher than that in foggy conditions. The findings indicated that vigilance should be a key safety concern in automated driving conditions.

Decision Aiding for Nautical Collision Avoidance: Trust Dependence and Implicit Understanding of the Decision Algorithm
Author(s):Christopher Wickens Colorado State University; Nicholas Fitzgerald Fort Colllins PD; Benjamin Clegg Colorado State University; Cap Smith Dept of Business Administration; Dylan Orth Colorado State University Psychology; Katie Kincaid Colorado State University Psychology
Abstract: Two experiments examined an imperfect automation decision aid for maritime collision avoidance. In Experiment 1 the algorithm was driven purely by safety recommending turning your ship in the direction that produced the greatest separation from a hazard ship. In Experiment 2 the algorithm incorporated two additional factors known to influence ship collision avoidance maneuvers: efficiency and procedural adherence to established “rules of the road”. In both experiments results revealed heavy but not total reliance on the aid. A strong influence of rules of the road was indicated by non-compliance to recommendations contrary to those conventions even as they maximized safety; particularly in Experiment 2 when the rules were incorporated in the algorithm. This illustrates the powerful influence of categorical procedural algorithm elements over continuous quantitative ones in affecting automation compliance. Results also revealed the dissociation between rated trust in and behavioral dependence on decision aiding automation.

Driver Arousal and Workload under Partial Vehicle Automation a Pilot Study
Author(s):Monika Lohani University of Utah; Joel Cooper University of Utah; Gus Erickson University of Utah; Trent Simmons University of Utah; Amy McDonnell University of Utah; Amanda Carriero University of Utah; Kaedyn Crabtree University of Utah; David Strayer University of Utah
Abstract: Semi-automated vehicles (Level-2) provide driving assistance but they still require driver supervision to maintain safe driving. However little is known about potential differences in drivers’ cognitive states during manual vs. Level-2 automated driving. The current study systematically examined the effects of manual and Level-2 driving on drivers’ arousal and workload during on-road driving. No differences between the two driving modes were found for the five outcomes that assessed cognitive arousal and workload (i.e. heart rate root mean square of successive heart period differences EEG alpha power and hit rate and reaction time on a secondary task). A Bayes Factor analysis suggested that there is strong evidence that cognitive arousal and workload during Level-2 driving did not differ from manual driving. These novel and theoretically meaningful findings provide strong evidence of similar cognitive arousal and workload states in Level-2 automation and manual driving.

Driver Logo Sign Detection and Hazard Responses Under Partial Vehicle Automation
Author(s):Jing Feng NCSU; David Kaber University of Florida; Yunmei Liu Department of Industrial and Systems Engineering University of Florida; Christopher Cunningham Institute for Transportation Research and Education; Yulin Deng North Carolina State University; Stephen Cauffman North Carolina State University
Abstract: This study investigates the presentation of service logo information under partially automated driving. Drivers completed simulated drives with adaptive cruise control and lane keeping functions during which they had to detect target logo signs and react to hazards by taking over vehicle control. Driver performance was measured in terms of sign detection rate crash rate and takeover response time. Many factors including information source information load and driver age group were investigated. Our findings support the delivery of service logo information via in-vehicle display under partially automated driving especially when the in-vehicle display occurred simultaneously with the on-road signage. Despite that drivers tend to crash more and take longer to take over during a hazardous event when service logo was present this distraction effect was more apparent when service logo information was only presented via in-vehicle display or on-road signage but not when the information was simultaneously presented on both.

Driving with Robots: Mind Perception and Propensity for Aggressive Driving
Author(s):Karl Nachmann George Mason University; Benjamin Pillot George Mason University; Petrina Pervall George Mason University; Yi-Ching Lee George Mason University; Eva Wiese George Mason University
Abstract: Mind perception or the tendency to ascribe agency (i.e. the ability to plan and act) and experience (i.e. the ability to sense and feel) to others is an important design consideration for human-robot interaction since an agent’s mind status affects how we interact with it and how we interpret its behavior. The current study examines whether observable behaviors of robot-piloted autonomous vehicles are interpreted differently lead to different emotional reactions and trigger different behaviors of the observer as a function of the robot driver’s perceived mind status. We expect that aggressive behavior of robot drivers perceived to be high in agency would be interpreted as more intentional and as such would lead to stronger negative reactions and retaliatory behaviors. Consistent with our expectations the robot driver high in agency was perceived as more intentional and elicited more irritation in participants.

Lights Camera Autonomy! Exploring the Opinions of Older Adults Regarding Autonomous Vehicles Through Enactment
Author(s):Aaron Gluck Clemson University; Earl Huff Clemson University; Mengyuan Zhang Clemson University; Julian Brinkley Clemson University
Abstract: Autonomous vehicles (AV) one of the transportation industry’s biggest innovations of the past few decades bring the promise of safer roads and significantly lower vehicle-related fatalities. While many studies have found largely positive consumer opinions regarding operating and owning such a vehicle older adults (55+) tend to express concerns about the safety and operational risks of a vehicle with unknown capabilities. To investigate how older adults and AVs may interact we conducted an improv-style enactment-based participatory design pilot study. We found that initial concerns about trust and safety can be diminished through training and repetitive successful vehicle operation. Additionally our participants provided insights into the AV design considerations needs and interactions for older adults. These findings add to the collective body of autonomous vehicle research by demonstrating that the needs of this growing population who may benefit significantly from access to AVs should be considered by manufacturers.

Children with Medical Complexity: Challenges and Opportunities for Human Factors/Ergonomics
Author(s):Hanna Barton University of Wisconsin - Madison Department of Industrial and Systems Engineering; Sara Finesilver Madison College; Ryan Coller University of Wisconsin-Madison Department of Pediatrics; Christopher Lunsford Departments of Pediatrics and Orthopaedic Surgery; Rupa Valdez University of Virginia; Nicole Werner University of Wisconsin-Madison
Abstract: For vulnerable patient populations such as children with medical complexity (CMC) the patient journey is fraught with challenges. By providing a range of perspectives including clinicians a family caregiver and Human Factors/Ergonomics (HF/E) experts the present panel will describe the unique opportunities for HF/E to design jointly optimized systems for CMC and their family caregivers including an explication of some of the specific challenges and complexities related to studying the work of and designing systems for this population. We will also highlight the ways in which HF/E could help in the design of solutions to improve outcomes for families.

An Investigation of the Factors Predicting Participation in Social Media Challenges
Author(s):Amro Khasawneh Johns Hopkins University; Heidi Zinzow Clemson University; Kapil Chalil Madathil Clemson University; Patrick Rosopa Clemson University; Shraddhaa Narasimha Clemson University
Abstract: Online challenges phenomena are very familiar to adolescents and young adults who spend large portions of time on social media. We conducted a retrospective quantitative study with a total of 471 participants between the ages of 13 and 35 who either had participated in the Amyotrophic Lateral Sclerosis (ALS) Ice Bucket Challenge (IBC) the Cinnamon Challenge (CC) or had never participated in any online challenge. We used binomial logistic regression models to classify those who participated in ALS IBC or CC versus those who didn’t with the beliefs from the Integrated Behavioral Model (IBM) as predictors. Our findings showed that both CC and ALS IBC participants had significantly greater positive emotional responses value for the outcomes of the challenge and expectation of the public to participate in the challenge in comparison to individuals who never participated in any challenge.

Cognitive Work Analysis and Visualization Design for the Graduate Admission Decision Making Process
Author(s):Xiaomei Wang Texas A&M University; Ann Bisantz State University of New York at Buffalo; Matthew Bolton State University of New York at Buffalo; Lora Cavuoto State University of New York at Buffalo; Varun Chandola State University of New York at Buffalo
Abstract: Graduate admission has always been a complex decision making process. The link between application materials and student success has remained elusive and as such there is no validated method for making decisions. To understand the purposes processes difficulties and needs of the current graduate admission process semi-structured interviews were conducted with participants from engineering departments. Cognitive work analysis techniques were used to summarize the findings from the interviews. Visualizations were designed to improve the current online review system. User feedback was collected in an experiment.

Eye Tracking Data Analytics in Virtual Reality Training: Application in Deepwater Horizon Oil Drilling Operation
Author(s):Ziho Kang University of Oklahoma; Jiwon Jeon University of Oklahoma; Saeed Salehi University of Oklahoma
Abstract: Virtual reality (VR) enable us to train in a safe environment using computer-generated simulations. One such simulated environment is the Deepwater Horizon operation and VR enables us to evaluate trainees’ and operators’ situation awareness (SA) in a non-hazardous environment. One unobtrusive and viable SA evaluation method might be the use of eye movements specifically the time-ordered visual scan paths. In this research we investigated how SA can be associated with visual scan paths in an anomaly detection task within the oil drilling rig virtual reality simulator. The results show that the trainees having lower SA tended to create random visual scan paths whereas the trainees having higher SA tended to create concentrated and refined visual scan paths. The results show promise in developing timely intervention methods through analyzing the visual scan path characteristics of the trainees.

Inclusion by Design: A 75-Minute Crash Course on Accessible Design
Author(s):Julian Brinkley Clemson University; Earl Huff Clemson University
Abstract: The community of researchers supporting instruction on design thinking has a significant body of materials to help students understand and master the process of creative problem solving in design. Missing we argue are materials and processes which directly support the design of inclusive technologies for persons with disabilities. We present ‘Inclusion by Design’ an interactive and participative crash course designed to introduce students to techniques that may be useful in an inclusive design process. In a single 75-minute session students explore the inclusive design of a transportation technology for a visually impaired persona. We report on our findings from a single pilot of the crash course involving six diverse students within a graduate course on Inclusive Design. Our findings suggest that the course may be effective in introducing techniques like storyboarding scenario creation and low fidelity prototyping to students using an approach that may be effective for various learning styles.

Transfer of Training: Effectiveness of Context-Based Visual Decision Aids to Enhance the Situation Awareness of Windstorm Risk Engineers
Author(s):Kapil Chalil Madathil Clemson University; Jeffrey Bertrand Clemson University; Sruthy Agnisarman Clemson University
Abstract: Infrastructure risk inspection more specifically windstorm inspection survey involves the process of identifying wind vulnerabilities associated with a building to limit the extent of damages in the event of an extreme weather condition. It is important to support the situation awareness (SA) of engineers when completing risk inspection tasks as lack of SA can lead to inaccurate prediction of the future state of the infrastructure system. More specifically this study investigated the transfer of training effect of checklist based and predictive display based contextual decision aids in the absence of such aids. We investigated how SA of participants exposed to these decision aids were affected when they were removed. Our findings suggest that the participants in the checklist based decision aid maintained their SA in the absence of decision aids. However participants in the predictive display had significantly lower SA in the absence of it.

Utilizing Cognitive Load Theory and Evidence-Centered Design to Inform the Design of Game-Based Learning Environments
Author(s):Dolly Bounajim North Carolina State University; Eric Wiebe North Carolina State University; Arif Rachmatullah ; Danielle Boulden; Bradford Mott Center for Educational Informatics NCSU; James Lester Center for Educational Informatics NCSU; Trudi Lord ; Frieda Reichsman ; Paul Horwitz ; Chad Dorsey
Abstract: Digital game-based learning (DGBL) environments are increasingly utilized to facilitate classroom instruction. For the game in our study a formative stealth assessment tool in the form of an intelligent tutoring system (ITS) is guided by evidence-centered assessment design (ECD). Cognitive Load Theory and ECD are utilized as diagnostic tools to analyze upsurges in hints delivered by the ITS and inform game design revisions that will promote improved learner support and learning outcomes.