Sandro Scielzo

Training individuals for distributed teams: problem solving assessment for distributed mission research §

In this paper we describe an effort investigating the feasibility and utility of cognitively diagnostic assessment of problem solving when training for distributed team tasks. We utilized computer-based knowledge elicitation methods to assess both relational problem solving, requiring the semantic integration of concepts, and dynamic problem solving, requiring the ability to integrate and apply these concepts. Additionally, we addressed how metacognitive processes interact with learning outcomes when training for complex synthetic task environments. We find first, that multiple methods of assessing problem solving performance are diagnostic of knowledge acquisition for a complex synthetic team task, and second, that general metacomprehension predisposition is related to metacomprehension accuracy in synthetic task environments.

Understanding performance and cognitive efficiency when training for x-ray security screening

We describe an experiment designed to understand the X-ray security screener task via investigation of how training environment and content influence perceptual learning. We examined both perceptual discrimination and the presence/absence of clutter during training and how this impacted performance. Overall, the data show that performance was generally better when there were clutter items in the training images. We also examined the diagnosticity of a measure of cognitive efficiency, a combinatory metric that simultaneously considers test performance and workload. In terms of cognitive efficiency, participants who trained in the difficult discrimination with clutter present experienced lower workload during the test relative to their actual performance. The discussion centers on how improved analytical techniques are better able to diagnose the relative effectiveness of training interventions.

Investigating Individual Differences and Instructional Efficiency in Computer-Based Training Environments

This study assessed the extent to which a guided learner-generated questioning strategy could facilitate the acquisition of task-relevant knowledge and improve the instructional efficiency of a computer-based training program for a complex dynamic distributed decision-making task. This study also investigated how individual differences in verbal comprehension ability may interact with this instructional strategy to impact post-training outcomes. Overall, results highlighted the importance of learner aptitudes in complex task training and also showed that the effect of the instructional strategy on knowledge acquisition and the training programa” instructional efficiency was strongest for learners with low verbal comprehension ability. Implications for the design of adaptive learning systems are discussed

Impact of Multimedia Presentation on Knowledge Acquisition for Complex Training

In this study we examined principles of multimedia training design within a complex task environment. We anticipated a differential effect for multimedia instructional format (redundant or non-redundant verbal information) on knowledge acquisition. Knowledge acquisition was assessed with increasingly more complex tasks, ranging from simple concept recognition assessment to transfer tasks tapping integrative knowledge. Our results show a significant benefit for redundant instruction on more complex transfer tasks. The discussion centers on the generalizability of multimedia design principles to computer-based training for complex systems.

Promoting Individual and Team Cognitive Readiness in Complex Domains

In the complex operational environments that prevail in many organizations, successful operations depend more on the ability to adapt to novel, emerging situations, rather than repeating a learned, proscribed task sequence. In these situations, the cognitive readiness of the individuals and teams involved is critical to insuring mission success. Our understanding of the factors underlying cognitive readiness, the interaction among those factors, and their impact on performance outcomes, however, is still developing. The current paper describes several recent research projects aimed at improving our understanding of cognitive readiness. It details the significant findings of these research efforts and suggests directions for future research needed to fully capitalize on the potential for cognitive readiness measures to predict performance.

Synthetic Learning Environment Games: Prototyping a Humanities-Based Game for Teaching African American History:

Generational differences and financial and motivational barriers have limited student access to humanities resources. We contend that Simulation-based Learning Games (SLGs) offer a potential means for delivering humanities content in a format that is more accessible to students and more appealing – both visually and interactively – than traditional educational materials. To explore this assertion, a prototype SLG was developed by students and faculty at the University of Central Florida. This prototype was created using off-the-shelf game technology, and was designed to incorporate sound principles drawn from both the science of learning and the area of game design. Additionally, the SLG is designed to reference and incorporate Floridas Sunshine State Educational Standards to ensure relevance to and support of traditional educational methods.

23. Cognition and Collaboration in Hybrid Human–Robot Teams: Viewing Workload and Performance through the Lens of Multimedia Cognitive Load

Cognitive Load Theory (CLT) is the product of over a decade of research in the instructional science domain (Chandler & Sweller, 1991; Sweller & Chandler, 1994), and its applications to other areas of inquiry continues to expand (see Cuevas, Fiore, & Oser, 2002; Paas, Renkl, & Sweller, 2003a; Paas, Tuovinen, Tabbers, & Van Gerven, 2003b; Scielzo, Fiore, Cuevas, & Salas, 2004). The core of CLT is based on two sets of what are termed cognitive load factors that are either endogenous or exogenous from the viewpoint of an operator interacting with the environment. Endogenous (or intrinsic) factors are sources of cognitive load in terms of the general amount and complexity of information with which the operator has to interact. In training environments, intrinsic load is directly proportional to the amount of materials that trainees need to acquire. As such, the more complex the information is in terms of volume and conceptual interactivity, the higher the cognitive load will be. In operational settings, high intrinsic load can occur whenever informational demands that need to be processed are high. Within the context of human–robot team environments, there is likely to be unique intrinsic load factors emerging from this hybrid teamwork interaction (e.g., information produced by synthetic team members). Another source of cognitive load comes from exogenous or extraneous factors. In training and operational settings alike, extraneous cognitive load may occur dependent upon the manner in which information needing attention is presented. Specifically, the more complex the human–robot team interface is in relation to the process by which information is displayed and/or communicated, the more extraneous cognitive load can be present. For example, the technological tools involved in the communication of information, and the associated modalities used to process information may inadvertently result in cognitive load. Simply put, high extraneous cognitive load can be produced as a result of using sub-optimal information presentation and communication. Overall, exogenous factors can stem from the added complexity of human–robot operations in terms of distinct command-and-control systems that emerge from using novel technology. Within such operations, it is particularly important to control sources of extraneous cognitive load that have been shown to produce two distinct negative effects on information processing – redundancy of information and split-attention. These have been shown to attenuate processing capacity thereby minimizing optimal information processing (e.g., Sweller, 1994; Mayer, 1999).