Geosciences for Elementary Educators: A Course Assessment

Geosciences for Elementary Educators engages future elementary teachers in a hands-on investigation of topics aligned with the third and fifth grade Earth/Space Science and Scientific Inquiry benchmarks of the Oregon Content Standards. The course was designed to develop the content background of elementary teachers within the framework of the science described in the content standards, to provide an opportunity for future teachers to explore the content area in relation to what takes place in the classrooms of elementary schools, and to initiate a community of learners focused on teaching science to elementary students. The course focused on four themes: the classroom teacher as an activity and curriculum developer using diverse resources to keep the content current and alive; the classroom teacher as educator dealing with the diverse backgrounds of students in a developmentally appropriate manner; the classroom teacher as reflective practitioner exploring the links among pedagogy, content, and student learning; and, the classroom teacher as citizen staying current with emerging policy issues and debates that impact education. In a course where process is extremely important, participants are assessed on what they can do with content and process knowledge through preparing lesson plans, presenting lessons in a simulated classroom environment, and developing a portfolio and journal. Lesson plans demonstrate participant understanding of inquiry, using models, deductive and inductive approaches, links between communication skills and content knowledge, and effective use of technology, including the Internet. For each topic, the mixture of demonstration, experimentation, inquiry, and lecture models are explored through investigation, discovery, and analysis

Automation and Accountability in Decision Support System Interface Design

When the human element is introduced into decision support system design, entirely new layers of social and ethical issues emerge but are not always recognized as such. This paper discusses those ethical and social impact issues specific to decision support systems and highlights areas that interface designers should consider during design with an emphasis on military applications. Because of the inherent complexity of socio-technical systems, decision support systems are particularly vulnerable to certain potential ethical pitfalls that encompass automation and accountability issues. If computer systems diminish a user’s sense of moral agency and responsibility, an erosion of accountability could result. In addition, these problems are exacerbated when an interface is perceived as a legitimate authority. I argue that when developing human computer interfaces for decision support systems that have the ability to harm people, the possibility exists that a moral buffer, a form of psychological distancing, is created which allows people to ethically distance themselves from their actions

Partitioning Complexity in Air Traffic Management Task

Cognitive complexity is a term that appears frequently in air traffic control (ATC) research literature, yet there is little principled investigation of the potential sources of cognitive complexity. Three distinctly different sources of cognitive complexity are proposed which are environmental, organizational, and display. Two experiments were conducted to explore whether or not these proposed components of complexity could be effectively partitioned, measured, and compared. The findings demonstrate that sources of complexity can be decomposed and measured and furthermore, the use of color in displays, a display design intervention meant to reduce environmental complexity, can actually contribute to it.This research was sponsored by the Civil Aerospace Medical Institute

Assessing the Impact of Auditory Peripheral Displays for UAV Operators

A future implementation of unmanned aerial vehicle (UAV) operations is having a single operator control multiple UAVs. The research presented here explores possible avenues of enhancing audio cues of UAV interfaces for this futuristic control of multiple UAVs by a single operator. This project specifically evaluates the value of continuous and discrete audio cues as indicators of course deviations or late arrivals to targets for UAV missions. It also looks at the value of the audio cues in single and multiple UAV scenarios. To this end, an experiment was carried out on the Multiple Autonomous Unmanned Vehicle Experimental (MAUVE) test bed developed in the Humans and Automation Laboratory at the Massachusetts Institute of Technology with 44 military participants. Specifically, two continuous audio alerts were mapped to two human supervisory tasks within MAUVE. One of the continuous audio alerts, an oscillating course deviation alert was mapped to UAV course deviations which occurred over a continual scale. The other continuous audio alert tested was a modulated late arrival alert which alerted the operator when a UAV was going to be late to a target. In this case the continuous audio was mapped to a discrete event in that the UAV was either on time or late to a target. The audio was continuous in that it was continually on and alerting the participant to the current state of the UAV. It either was playing a tone indicating the UAV was on time to a target or playing a tone indicating the UAV was late to a target. These continuous alerts were tested against more traditional single beep alerts which acted as discrete alerts. The beeps were discrete in that when they were used for monitoring course deviations a single beep was played when the UAV got to specific threshold off of the course or for late arrivals a single beep was played when the UAV became late. The results show that the use of the continuous audio alerts enhances a single operator’s performance in monitoring single and multiple semi-autonomous vehicles. However, the results also emphasize the necessity to properly integrate the continuous audio with the other auditory alarms and visual representations in a display, as it is possible for discrete audio alerts to be lost in aural saliency of continuous audio, leaving operators reliant on the visual aspects of the display.Prepared for Charles River Analytics, Inc

Decision Support Design for Workload Mitigation in Human Supervisory Control of Multiple Unmanned Aerial Vehicles

As UAVs become increasingly autonomous, the multiple personnel currently required to operate a single UAV may eventually be superseded by a single operator concurrently managing multiple UAVs. Instead of lower-level tasks performed by today’s UAV teams, the sole operator would focus on high-level supervisory control tasks such as monitoring mission timelines and reacting to emergent mission events. A key challenge in the design of such single-operator systems will be the need to minimize periods of excessive workload that could arise when critical tasks for several UAVs occur simultaneously. To a certain degree, it is possible to predict and mitigate such periods in advance. However, actions that mitigate a particular period of high workload in the short term may create long term episodes of high workload that were previously non-existent. Thus some kind of decision support is needed that facilitates an operator’s ability to evaluate different options for managing a mission schedule in real-time. This paper describes two decision support visualizations designed for supervisory control of four UAVs performing a time-critical targeting mission. A configural display common to both visualizations, named the StarVis, was designed to highlight potential periods of high workload corresponding to the current mission timeline, as well as “what if” projections of possible high workload periods based upon different operator options. The first visualization design allows an operator to compare different high workload mitigation options for individual UAVs. This is termed the local visualization. The second visualization is indicates the combined effects of multiple high workload mitigation decisions on the timeline. This is termed the global visualization. The main advantage of the local visualization is that options can be compared directly; however, the possible effects of these options on the mission timeline are only indicated for the individual UAV primarily affected by the decision. For the global visualization, different decisions can be combined to show possible effects on the system propagated across all UAVs, but the different alternatives of a single decision option alternative cannot be directly compared. An experiment was conducted testing these visualizations against a control with no visualization. Results showed that subject using the local visualization had better performance, higher situational awareness, and no significant increase in workload over the other two experimental conditions. This occurred despite the fact that the local and global StarVis displays were very similar. Not only did the Global StarVis produce degraded results as compared to the local StarVis, but those participants with no visualization performed as well as those with the global StarVis. This disparity in performance despite strong visual similarities in the StarVis designs is attributed to operators’ inability to process all the information presented in the global StarVis as well as the fact that participants with the local StarVis were able to rapidly develop effective cognitive problem strategies. This research effort highlights a very important design consideration, in that a single decision support design can produce very different performance results when applied at different levels of abstraction.Prepared for Kevin Burns, The MITRE Corporatio

Modeling multiple human operators in the supervisory control of heterogeneous unmanned vehicles

In the near future, large, complex, time-critical missions, such as disaster relief, will likely require multiple unmanned vehicle (UV) operators, each controlling multiple vehicles, to combine their efforts as a team. However, is the effort of the team equal to the sum of the operator's individual efforts? To help answer this question, a discrete event simulation model of a team of human operators, each performing supervisory control of multiple unmanned vehicles, was developed. The model consists of exogenous and internal inputs, operator servers, and a task allocation mechanism that disseminates events to the operators according to the team structure and state of the system. To generate the data necessary for model building and validation, an experimental test-bed was developed where teams of three operators controlled multiple UVs by using a simulated ground control station software interface. The team structure and interarrival time of exogenous events were both varied in a 2×2 full factorial design to gather data on the impact on system performance that occurs as a result of changing both exogenous and internal inputs. From the data that was gathered, the model was able to replicate the empirical results within a 95% confidence interval for all four treatments, however more empirical data is needed to build confidence in the model's predictive ability.United States. Office of Naval ResearchUnited States. Air Force Office of Scientific Researc

A Predictive Model for Human-Unmanned Vehicle Systems : Final Report

Advances in automation are making it possible for a single operator to control multiple unmanned vehicles (UVs). This capability is desirable in order to reduce the operational costs of human-UV systems (HUVS), extend human capabilities, and improve system effectiveness. However, the high complexity of these systems introduces many significant challenges to system designers. To help understand and overcome these challenges, high-fidelity computational models of the HUVS must be developed. These models should have two capabilities. First, they must be able to describe the behavior of the various entities in the team, including both the human operator and the UVs in the team. Second, these models must have the ability to predict how changes in the HUVS and its mission will alter the performance characteristics of the system. In this report, we describe our work toward developing such a model. Via user studies, we show that our model has the ability to describe the behavior of a HUVS consisting of a single human operator and multiple independent UVs with homogeneous capabilities. We also evaluate the model’s ability to predict how changes in the team size, the human-UV interface, the UV’s autonomy levels, and operator strategies affect the system’s performance.Prepared for MIT Lincoln Laborator

Identifying Predictive Metrics for Supervisory Control of Multiple Robots

In recent years, much research has focused on making possible single operator control of multiple robots. In these high workload situations, many questions arise including how many robots should be in the team, which autonomy levels should they employ, and when should these autonomy levels change? To answer these questions, sets of metric classes should be identified that capture these aspects of the human-robot team. Such a set of metric classes should have three properties. First, it should contain the key performance parameters of the system. Second, it should identify the limitations of the agents in the system. Third, it should have predictive power. In this paper, we decompose a human-robot team consisting of a single human and multiple robots in an effort to identify such a set of metric classes. We assess the ability of this set of metric classes to (a) predict the number of robots that should be in the team and (b) predict system effectiveness. We do so by comparing predictions with actual data from a user study, which is also described.This research was funded by MIT Lincoln Laboratory

Operator Choice Modeling for Collaborative UAV Visual Search Tasks

Unmanned aerial vehicles (UAVs) provide unprecedented access to imagery of possible ground targets of interest in real time. The availability of this imagery is expected to increase with envisaged future missions of one operator controlling multiple UAVs. This research investigates decision models that can be used to develop assistive decision support for UAV operators involved in these complex search missions. Previous human-in-the-loop experiments have shown that operator detection probabilities may decay with increased search time. Providing the operators with the ability to requeue difficult images with the option of relooking at targets later was hypothesized to help operators improve their search accuracy. However, it was not well understood how mission performance could be impacted by operators performing requeues with multiple UAVs. This work extends a queuing model of the human operator by developing a retrial queue model (ReQM) that mathematically describes the use of relooks. We use ReQM to generate performance predictions through discrete event simulation. We validate these predictions through a human-in-the-loop experiment that evaluates the impact of requeuing on a simulated multiple-UAV mission. Our results suggest that, while requeuing can improve detection accuracy and decrease mean search times, operators may need additional decision support to use relooks effectively.Michigan/AFRL Collaborative Center in Control ScienceUnited States. Office of Naval Research (Grant N00014-07-1-0230