Power up! Using Digital Gaming Techniques to Enhance Library Instruction

ABSTRACT

Incorporating digital gaming into education is an increasingly popular topic in academia. Educators’ views of digital gaming (e.g., computer, video, and online games) have shifted from categorizing these games as an entertaining, yet pedagogically void activity to a promising medium that engages students in the learning process. With over 70% of college students enthusiastically playing digital games, educators are tapping into digital gaming as an effective means to teach millennials problem solving and communication skills. This article investigates gamer characteristics, motivational and engaging aspects of digital games, and the parallels between gaming and the information retrieval process. The authors build on this information to present approaches to incorporate digital gaming techniques into the library classroom since it is not feasible for all libraries to create instructional digital games.     

Improving trust modeling through the limit of advisor network size and use of referrals

This paper explores potential improvements to the trust modeling of agents in multi-agent systems when a social network of advisors is employed as part of the trust modeling, and in particular, examines means of optimizing the number of advisors that should be maintained in the social network. We propose three such improvements, two directly relating to the limit of advisor network size by either setting a maximum size for a buyer’s advisor network or setting a minimum trustworthiness threshold for agents to be accepted into that advisor network, and a third which uses an advisor referral system in combination with one of the first two network-limiting techniques. We provide experimental results in defence of our approach for two distinct trust modeling systems, and show how these optimizations can improve, sometimes significantly, the accuracy of different trust models (in the context of electronic marketplaces). We believe that the proposed techniques will be very useful for trust researchers seeking to improve the accuracy of their own trust models by setting the size and composition of advisor networks.     

A framework for simulating real-time multi-agent systems

In this paper, we describe an implementation of use in demonstrating the effectiveness of architectures for real-time multi-agent systems. The implementation provides a simulation of a simplified RoboCup Search and Rescue environment, with unexpected events, and includes a simulator for both a real-time operating system and a CPU. We present experimental evidence to demonstrate the benefit of the implementation in the context of a particular hybrid architecture for multi-agent systems that allows certain agents to remain fully autonomous, while others are fully controlled by a coordinating agent. In addition, we discuss the value of the implementation for testing any models for the construction of real-time multi-agent systems and include a comparison to related work.

A formative analysis of individual differences in the effectiveness of learning objects in secondary school

The purpose of this study was to examine individual differences in the effectiveness of learning objects in secondary school classrooms. Specifically, gender, age, grade, subject area, and computer comfort (self-efficacy) were examined in 850 students. Effectiveness was measured in terms of student attitude (learning, quality, and engagement) and student performance. No gender differences were observed between males and females with respect to student attitudes or performance. Age was significantly correlated with student attitudes and performance, however correlation coefficients were small. Grade 12 students were more positive about learning objects and performed better than grade 9 and 10 students. Science students had significantly more positive attitudes and performed better than mathematics students. Finally, students who were more comfortable about computers, appreciated learning objects more than their less confident peers, however performance was unaffected.     

Reducing control selection errors associated with underground bolting equipment

Selecting the incorrect control during the operation of underground bolting and drilling equipment causes serious injuries. Shape coding and the layout of dual control banks are two aspects of control design which require further examination. The aims of this research were: (i) to determine whether arbitrary shape coding was effective in reducing selection error rates in a virtual analogy of roof-bolting; and (ii) to determine whether any advantages exist for mirror or place layouts for dual control situations in this situation. Two experiments were conducted to address these questions. No benefits of arbitrary shape coding were evident while control location remained constant. When control location was altered, shape coding did provide a significant reduction in selection error rate. No differences between mirror or place arrangements were detected and this question remains open.     

Simulation-based workforce assignment in a multi-organizational social network for alliance-based software development

The development of alliance-based software requires the collaboration of many stakeholders. These different stakeholders across multiple organizations form a complex social network. The goal of this paper is to develop a novel modeling framework, which will help task managers devise optimal workforce assignments considering both short-term and long-term aspects of the software development process. The proposed framework is composed of an assignment module and a prediction module. For a given task, the assignment module first selects a candidate workforce mix. Based on the candidate workforce mix, the prediction module then predicts the short-term performance (productivity) as well as the long-term performance (workforce training and robustness of the organization) of the organization. Then, the assignment module selects another candidate mix, and this iteration continues until an optimal workforce mix is found. The prediction module and the assignment module are based on an agent-based simulation method and a multi-objective optimization model, respectively. The proposed modeling framework is illustrated with a software enhancement request process in Kuali, an alliance-based open source software development project involving 12 organizations. The constructed framework is executed with varying parameters to demonstrate its use and benefit in the software enhancement process.     

Gaming Prediction Markets: Equilibrium Strategies with a Market Maker

We study the equilibrium behavior of informed traders interacting with market scoring rule (MSR) market makers. One attractive feature of MSR is that it is myopically incentive compatible: it is optimal for traders to report their true beliefs about the likelihood of an event outcome provided that they ignore the impact of their reports on the profit they might garner from future trades. In this paper, we analyze non-myopic strategies and examine what information structures lead to truthful betting by traders. Specifically, we analyze the behavior of risk-neutral traders with incomplete information playing in a dynamic game. We consider finite-stage and infinite-stage game models. For each model, we study the logarithmic market scoring rule (LMSR) with two different information structures: conditionally independent signals and (unconditionally) independent signals. In the finite-stage model, when signals of traders are independent conditional on the state of the world, truthful betting is a Perfect Bayesian Equilibrium (PBE). Moreover, it is the unique Weak Perfect Bayesian Equilibrium (WPBE) of the game. In contrast, when signals of traders are unconditionally independent, truthful betting is not a WPBE. In the infinite-stage model with unconditionally independent signals, there does not exist an equilibrium in which all information is revealed in a finite amount of time. We propose a simple discounted market scoring rule that reduces the opportunity for bluffing strategies. We show that in any WPBE for the infinite-stage market with discounting, the market price converges to the fully-revealing price, and the rate of convergence can be bounded in terms of the discounting parameter. When signals are conditionally independent, truthful betting is the unique WPBE for the infinite-stage market with and without discounting.     

Linking air quality and watershed models for environmental assessments: Analysis of the effects of model-specific precipitation estimates on calculated water flux

Directly linking air quality and watershed models could provide an effective method for estimating spatially-explicit inputs of atmospheric contaminants to watershed biogeochemical models. However, to adequately link air and watershed models for wet deposition estimates, each model’s temporal and spatial representation of precipitation needs to be consistent. We explore how precipitation implemented within the Community Multi-Scale Air Quality Model (CMAQ) model algorithms, and multiple spatially-explicit precipitation datasets that could be used to improve the CMAQ model deposition estimates, links with the standard precipitation sources used to calibrate watershed models (i.e., rain gage data) via modeled water fluxes. Simulations are run using a grid-based watershed mercury model (GBMM) in two watersheds. Modeled monthly runoff suggests that multiple resolution Parameter-elevations Regressions on Independent Slopes Model (PRISM) and National Multi-sensor Precipitation Analysis Stage IV (NPA) data generate similar monthly runoff estimates, with comparable or greater accuracy when evaluated against stream gage data than that produced by the base rain gage data. However, across longer time periods, simulated water balances using 36 km Pennsylvania State University/National Center for Atmospheric Research mesoscale model (MM5) data are similar to that of base data. The investigation also examines the implications our results, providing suggestions for linking air quality and watershed fate and transport models.     

Robot-Assisted Bridge Inspection

The Center for Robot-Assisted Search and Rescue (CRASAR®) deployed a customized AEOS man-portable unmanned surface vehicle and two commercially available underwater vehicles (the autonomous YSI EcoMapper and the tethered VideoRay) for inspection of the Rollover Pass bridge in the Bolivar peninsula of Texas in the aftermath of Hurricane Ike. A preliminary domain analysis with the vehicles identified key tasks in subsurface bridge inspection (mapping of the debris field and inspecting the bridge footings for scour), control challenges (navigation under loss of GPS, underwater obstacle avoidance, and stable positioning in high currents without GPS), possible improvements to human-robot interaction (having additional display units so that mission specialists can view and operate on imagery independently of the operator control unit, incorporating 2-way audio to allow operator and field personnel to communicate while launching or recovering the vehicle, and increased state sensing for reliability), and discussed the cooperative use of surface, underwater, and aerial vehicles. The article posits seven milestones in the development of a fully functional UMV for bridge inspection: standardize mission payloads, add health monitoring, improve teleoperation through better human-robot interaction, add 3D obstacle avoidance, improve station-keeping, handle large data sets, and support cooperative sensing.     

<Emphasis Type="Italic">A Midsummer Night’s Dream</Emphasis> (with flying robots)

Seven flying robot “fairies” joined human actors in the Texas A&;M production of William Shakespeare’s A Midsummer Night’s Dream. The production was a collaboration between the departments of Computer Science and Engineering, Electrical and Computer Engineering, and Theater Arts. The collaboration was motivated by two assertions. First, that the performing arts have principles for creating believable agents that will transfer to robots. Second, the theater is a natural testbed for evaluating the response of untrained human groups (both actors and the audience) to robots interacting with humans in shared spaces, i.e., were believable agents created? The production used two types of unmanned aerial vehicles, an AirRobot 100-b quadrotor platform about the size of a large pizza pan, and six E-flite Blade MCX palm-sized toy helicopters. The robots were used as alter egos for fairies in the play; the robots did not replace any actors, instead they were paired with them. The insertion of robots into the production was not widely advertised so the audience was the typical theatergoing demographic, not one consisting of people solely interested technology. The use of radio-controlled unmanned aerial vehicles provides insights into what types of autonomy are needed to create appropriate affective interactions with untrained human groups. The observations from the four weeks of practice and eight performances contribute (1) a taxonomy and methods for creating affect exchanges between robots and untrained human groups, (2) the importance of improvisation within robot theater, (3) insights into how untrained human groups form expectations about robots, and (4) awareness of the importance of safety and reliability as a design constraint for public engagement with robot platforms. The taxonomy captures that apparent affect can be created without explicit affective behaviors by the robot, but requires talented actors to convey the situation or express reactions. The audience’s response to robot crashes was a function of whether they had the opportunity to observe how the actors reacted to robot crashes on stage, suggesting that pre-existing expectations must be taken into account in the design of autonomy. Furthermore, it appears that the public expect robots to be more reliable (an expectation of consumer product hardening) and safe (an expectation from product liability) than the current capabilities and this may be a major challenge or even legal barrier for introducing robots into shared public spaces. These contributions are expected to inform design strategies for increasing public engagement with robot platforms through affect, and shows the value of arts-based approaches to public encounters with robots both for generating design strategies and for evaluation.