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Battlespace Management. In this battle problem, Fighter 1 is re-assigned to the East, leaving a gap in air defenses. The student should move Fighters 2 and 3 to fill the gap; otherwise, enemy Fighter 4 may penetrate the defenses.
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Training simulators have become increasingly popular tools for instructing humans on performance in complex environments. However, the question of how to provide individualized and scenario-specific assessment and feedback to students remains largely an open question. To maximize training efficiency, new technologies are required that assist instru...
Context in source publication
Context 1
... one test scenario, the student was instructed to re- task fighter aircraft away from the initial combat air patrol station. Moving the fighters created a gap in air defenses, possibly allowing an incursion into protected air space as shown in Figure 2. The student was expected to notice this vulnerability and re-assign other fighter assets to fill the gap. ...
Citations
... For instance, in the 1970's, games were predicted to act as coaches or tutors in the future [1] and there are still efforts in creating algorithms that will perform certain task that are now performed by a human instructor (e.g. [2]- [5]). However, more and more scholars are starting to realise that serious games, as artefacts used for learning and training, cannot fully replace the instructors' tasks, but must rather be designed to support them. ...
... • labour-intensive and time-consuming individualised instruction or coaching [2], [5], • lack of competent facilitators [1], [8], [54], and • increased need for technical and pedagogical support for inexperienced instructors [20], [35], [55]; • Practical difficulties in harmonising serious games with the constraints of the educational setting, such as time needed for playing a game [8], [20], [50], [56]; • Perceived or real lack of technology reliability and validity [8]; • Difficulties in adapting games with fixed content [35], [49]; • Difficulties in following dynamic gameplay in real-time [1], at least without additional functionalities that support in-game facilitation; • Difficulties in providing real-time feedback and support when gameplay is asynchronous and distributed, such as during a distance course [31]; • Incompatible learning theories with regard to instructorled serious gaming, such as ill-defined instructor roles [27], or other organisational obstacles [38]. ...
... Technical solutions that aim to relieve some of the cognitive workload put on human instructors seem to be a more promising approach. Most research efforts have focused on automatic assessment systems that are integrated into the game [5], [50], [70] and many also express the need for more work on debriefing tools [48], [65]. To give an example, Ekanayake et al. [2] created an algorithm that assesses driving behaviour in a driving simulator. ...
While there is a wealth of studies on the subject of serious games, the same cannot be said on the issue of teaching with games, especially in game-based learning settings with adult learners. Over the years, most research in this area has been focused on the ‘active substance(s)’ of games for learning, focusing mainly on characteristics of games, but often failing to take the whole context of game-based learning into consideration, such as the role(s) of the teacher. However, the past two or three years has seen a shift in focus from merely the game as an isolated artefact, to also include more discussions on how games can successfully be integrated into an educational setting, as well as challenges as pitfalls of which instructors need to be aware. This paper aims to outline the contemporary research on instructor-led serious gaming and its implications for the design of serious gaming environments.
... On one hand, some form of automation may be present in the goal system being trained, and therefore training must effectively instruct the person on the use of this performance support tool. On the other hand, automation may be introduced within the learning process as a training aid, with the intent of supporting the novice by changing workload demands or directing his or her learning only temporarily (Stevens- Adams, Basilico, Abbott, Gieseler, & Forsythe, 2010). In this article we focus on whether automation has been, and may be, useful in training and how automation might influence underlying learning. ...
Automation often elicits a divide-and-conquer outlook: By definition, automation has been suggested to assume control over a part or whole task that was previously performed by a human (Parasuraman & Riley, 1997). When such notions of automation are taken as grounds for training, they readily invoke a part-task training (PTT) approach. This article outlines broad functions of automation as a source of PTT and reviews the PTT literature, focusing on the potential benefits and costs related to using automation as a mechanism for PTT. The article reviews some past work in this area and suggests a path to move beyond the type of work captured by the “automation as PTT” framework. An illustrative experiment shows how automation in training and PTT are actually separable issues. PTT with automation has some utility but ultimately remains an unsatisfactory framework for the future broad potential of automation during training, and we suggest that a new conceptualization is needed.
[http://www.jstor.org/stable/10.5406/amerjpsyc.126.4.0417]
... Within the military domain, there is increasing interest in instructor-less game-based training that relies on intelligent tutoring systems (e.g. Salas & Cannon-Bowers, 2001;Stevens-Adams et al, 2010). Instructor-less training has several advantages, the most obvious being lower costs for staff, vehicles and facility maintenance (Stevens-Adams et al, 2010). ...
... Salas & Cannon-Bowers, 2001;Stevens-Adams et al, 2010). Instructor-less training has several advantages, the most obvious being lower costs for staff, vehicles and facility maintenance (Stevens-Adams et al, 2010). Another significant factor is flexibility; trainees can practice whenever and wherever they want. ...
A game-based learning environment is more than just a digital artefact. Factors such as where the game is played, how the learning experience is designed, the level of social interaction and so on, need to be considered when designing a game for learning or training. For instance, during gaming, the learners might physically leave the virtual environment to continue the gameplay in the physical environment. If the instructor wants to keep track of, for example, learning progress or game states, the gaming system needs to support these activities both in-game and outside the game, via different logging tools, e.g. video and voice recording. Military organisations have a long history of using games and simulations for training. This means that they have had the opportunity to develop and refine training practices that are both cost-effective and valuable for learning. However, these practices are largely based on instructors’ own experiences rather than scientific studies. This study aims to describe game-based training practices in order to (1) extricate good practices that may be transferred as inspirational examples for others, and (2) identify areas for improvement. Empirical material was collected using observations and interviews and then analysed and categorised. Interpretations made from the analysis were later validated through a questionnaire survey with military personnel directly or indirectly involved in simulator- or game-based training. The analysis shows that a game-based training cycle consists mainly of four phases: preparation, introductory lecture, gameplay and debriefing. Although the systems used are advanced in that they log user activity and support quick changes to the scenario during gameplay, running a training session is highly demanding for the instructors. Offline tools (e.g. pen and paper) are commonly used when there is a lack of system support in a specific situation. The paper concludes with a list of system support features for different aspects of game-based training.
In the past several years, the shift from traditional task-based training to competency-based training has gained traction within the training community. Rather than the traditional one-size-fits-all training solution, a Competency-Based Training and Assessment (CBTA) approach encourages tailoring the learning experiences to the learner and using evidence of learning to determine the student’s competency for a variety of learning components. The challenge then is how best to assess student competency, and how to store this data and use it to adapt the training experience to the student’s needs. In order to establish an effective CBTA methodology, we need to understand the requirements for clearly and consistently evaluating competencies both across students and learning opportunities, but also across multiple instructors who might be assessing different students. This paper seeks to develop a vision towards a standardized approach for CBTA data collection, grading, and assessment.KeywordsAdaptive Instructional SystemsIntelligent Tutoring AuthoringVirtual RealityAdaptive Simulation-Based LearningData Ecosystem
Cyber threats have become ubiquitous as criminals extend their reach and cyber becomes a front in conflicts between different peoples and a major source of revenue for criminal organizations. Personnel responsible for cyber defense are becoming increasing critical. However, there is a shortfall between the number of individuals training to enter cyber security and the projected demand for these skills. Consequently, methods and technologies are needed to enhance and accelerate the training of cyber security personnel. Previous research has demonstrated the benefits of automated performance assessments as a means to target training to the specific needs of individual students. The current paper describes an extension of these capabilities to cyber security training exercises. In these exercises, students are placed in teams and must work together, using appropriate software tools and online resources, to conduct forensic analysis for cyber crimes. Individual and team performance is assessed on the basis of successfully solving individual challenges and applying information from individual challenges to correctly ascertain an overall picture of the who, what and why of the crimes. The current paper describes a framework for conducting cyber security training exercises with an emphasis on instrumentation to enable automated performance assessment. Instrumentation captures students' computer-based transactions in a log that is time-synched with the game-server used to deliver challenges and register student responses. Analyses were conducted to better understand the factors that distinguish more or less effective student performance and techniques developed to automatically parse logs of student activities into meaningful blocks of task-oriented activity. These capabilities are a prerequisite for the development of real-time automated assessment of student performance within the context of cyber security exercises.
In part, the US has sustained superpower status as a result of educational systems that are superior to much of the less industrialized world. If the US wishes to remain a dominant economic and political force, rigorous intellectual standards must be a priority. The pendulum of intellectual capital has perhaps begun to tilt in the direction of China, in particular due to the staggering manpower of the country, but also as a product of restructuring spanning decades, in both education and politics. This restructuring has prompted the US to question the level of threat China could pose if they increase their role on the global stage. A position is asserted that through an integrated, systems-level approach that treats intellectual capital as a vital national resource, the US may broaden the gap to amass an indomitable advantage. A target is proposed whereby through a combination of science, technology and practice, there is a shift such that our top 50% are performing at or above the levels of today’s top 2%. Such an accomplishment may be achieved partially through US educational policies, but more importantly, there must be commitments to essential science and technology. While investments in different areas of science and technology may impact the global balance of power, this paper, will focus on one facet of science and technology — specifically, cognitive neuroscience and related neurotechnology. The elements essential to achieve such a dramatic shift in the balance of intellectual capital exist — what is lacking is a vision to bring these pieces together, motivated by a desire to build upon our intellectual capital as the means to assure global dominance.
In this paper we performed analysis of speech communications in order to determine if we can differentiate between expert
and novice teams based on communication patterns. Two pairs of experts and novices performed numerous test sessions on the
E-2 Enhanced Deployable Readiness Trainer (EDRT) which is a medium-fidelity simulator of the Naval Flight Officer (NFO) stations
positioned at bank end of the E-2 Hawkeye. Results indicate that experts and novices can be differentiated based on communication
patterns. First, experts and novices differ significantly with regard to the frequency of utterances, with both expert teams
making many fewer radio calls than both novice teams. Next, the semantic content of utterances was considered. Using both
manual and automated speech-to-text conversion, the resulting text documents were compared. For 7 of 8 subjects, the two most
similar subjects (using cosine-similarity of term vectors) were in the same category of expertise (novice/expert). This means
that the semantic content of utterances by experts was more similar to other experts, than novices, and vice versa. Finally,
using machine learning techniques we constructed a classifier that, given as input the text of the speech of a subject, could
identify whether the individual was an expert or novice with a very low error rate. By looking at the parameters of the machine
learning algorithm we were also able to identify terms that are strongly associated with novices and experts.
