Mitigating the situational factors that give rise to state boredom is a consistent challenge facing educators. Despite the growing amount of literature devoted to the construct, the field has yet to arrive at a consensus regarding a clear theoretical or operational definition. Subsequently, inconsistencies exist in the assessment methodologies, research findings lack generalizability, and strategies for mitigation in educational settings remain elusive. In this cross-disciplinary analysis, the extant literature on state boredom is critically reviewed and synthesized, and a two-dimensional definition of state boredom as an unpleasant (subjective), low-arousal (objective) experience is proposed. Findings from the technological advances of the last decade that allow for the objective measurement of physiological states are used to inform recommendations for empirically sound assessment methodologies. Finally, the proposed definition of state boredom and related assessment strategies are discussed with respect to implications for enhancing educational practices.

Computer-assisted learning, in the form of simulation-based training, is heavily focused upon by the military. Because computer-based learning offers highly portable, reusable, and costefficient training options, the military has dedicated significant resources to the investigation of instructional strategies that improve learning efficiency within this environment. In order to identify efficient instructional strategies, this paper investigates the two major learning theories that dominate the recent literature on optimizing knowledge acquisition: cognitive load theory (CLT) and constructivism. According to CLT, instructional guidance that promotes efficient learning is most beneficial. Constructivist approaches, in contrast, emphasize the importance of developing a conceptual understanding of the learning material. Supporters of these theories have debated the merits and shortcomings of both positions. However, in the absence of consensus, instructional designers lack a well-defined model for training complex skills in a rapid, efficient manner. The current study investigates the relative utility of CLT and constructivistbased approaches for teaching complex skills using a military command and control task. Findings suggest that the acquisition of procedural, declarative, and conceptual knowledge, as well as decision-making skills, did not differ as a function of the type of instruction used. However, integrated knowledge was slightly better retained by the group provided with CLT-based instruction. These results are contrary to our expectation that constructivist approaches, which focus on the development and integration of information, would yield better performance in an applied problem-based environment. Thus, while contemporary researchers continue to defend the use of constructivist strategies for teaching, our research supports earlier findings that question the utility, efficiency, and impact of these strategies in applied domains.

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Given the fundamental importance of higher-order cognitive skills for military personnel, increasing learning efficiency during training is paramount. The current article expands upon the state-based information-loss processing model, a comprehensive framework elucidating the processes involved in acquiring higher-order cognitive skills, to enumerate best practices for military training. Emphasis is placed on identifying empirically supported, state-of-the-art learning efficiency strategies and methodologies to address points of information loss throughout the learning process. Implications and pragmatic recommendations for simulation-based military training are discussed.The military is beginning to place increasing emphasis on distributed decision making; even personnel at the lowest levels, who were previously required only to remember procedures and follow orders, must now make decisions in the field (Conway, 2008). Thus, real-life applicability represents a crucial component of military training; learners who cannot translate information into performance pose a potentially catastrophic risk to both the learner and his or her fellow troops. As such, individuals at all levels of service will be required to fully comprehend and be able to apply learned information, to understand the interconnectivity of the battlefield, and to envision ways in which their choices will impact the larger battle space. Collectively, these situational demands necessitate that military personnel receive substantial training in higher-order cognitive skills.

The overarching goal of learner assessments is to identify areas of skill and deficit and to use this information to guide future instruction or error correction through feedback. To date, a variety of methods have been used to better understand individual learners. However, the tools used to assess learners' internal states have only allowed researchers to infer these states and, as a result, the information provided lacks the prescriptive specificity needed to most appropriately address learners' needs. The technological advances of current neuro-physiological measures may provide such specificity in real-time learning environments. These data show preliminary support for the use of electroencephalography measurements of workload and engagement to predict learning and knowledge acquisition. Additionally, the data suggest that the relationship between these two internal states may differ based on the level of information being assessed.

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The learning efficiency of complex tasks is an area being widely investigated in the literature. Specifically, many different instructional strategies have been developed in an effort to improve efficiency, especially within automated systems. Of particular interest are application methodologies which provide individualized recommendations. In this paper we compared the impact of individualized feedback based on both performance and real-time workload levels to feedback based on performance alone. Our data suggest paper-based knowledge acquisition test scores were not impacted by the intervention timing assisted by neuro-physiological measures. However, scenario-based decision-making performance scores were significantly improved when utilizing EEG data to aid intervention timing but not with eye-tracking data.