Mobile applications (apps) for learning technical scientific content are becoming increasingly popular in educational settings. Neuroscience is often considered complex and challenging for most students to understand conceptually. iNeuron is a recently developed iOS app that teaches basic neuroscience in the context of a series of scaffolded challenges to create neural circuits and increase understanding of nervous system structure and function. In this study, four different ways to implement the app within a classroom setting were explored. The goal of the study was to determine the app’s effectiveness under conditions closely approximating real-world use, and to evaluate whether collaborative play and student-driven navigational features contributed to its effectiveness. Students used the app either individually or in small groups, and used a version with either a fixed or variable learning sequence. Student performance on a pre- and post- neuroscience content assessment was analyzed and compared between students who used the app and a control group receiving standard instruction, and logged app data were analyzed. Significantly greater learning gains were found for all students who used the app compared to control. All four implementation modes were effective in producing student learning gains relative to controls, but did not differ in their effectiveness to one another. In addition, students demonstrated transfer of information learned in one context to another within the app. These results suggest that teacher-led neuroscience instruction can be effectively supported by a scaffolded, technology-based curriculum which can be implemented in multiple ways to enhance student learning.

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<div class="section abstract"><div class="htmlview paragraph">We describe how we apply the SAE AS 5506 Architecture and Analysis Design Language (AADL) [<span class="xref">4</span>] to reason about contextual and architectural concerns for cyber security. A system’s cyber security certification requires verification that the system’s cyber security mechanisms are correct, non-bypassable, and tamper-resistant. We can verify correctness by examining the mechanism itself, but verifying the other qualities requires us to examine the context in which that mechanism resides. Understanding that context and validating the system’s evolving design against that context is an objective for the Architecture Centric Virtual Integration Process (ACVIP), an AADL-based approach to model and detect system design defects before they become too costly to fix. We describe our work to apply AADL to assess non-bypassability and tamper-resistance. The results of our research - tool plugins for cyber security architectural validation - support system developers today in their ACVIP activities.</div></div>

In previous work, we described G2I2, a system that adjusts the cost function used by an off-road route planning system in order to more closely mimic the route choices made by humans. In this paper, we report on an extension to G2I2, called GUIDE, which adds significant new capabilities. GUIDE has the ability to induce a cost function starting with a set of historical tracks used as training input, with no requirement that these tracks be even close to cost-optimal. Given a cost function, either induced as above or provided from elsewhere, GUIDE can then compare planned routes with the actual tracks executed to adjust that cost function as either the environment or human preferences change over time. The features used by GUIDE in both the initial induction of the cost function and subsequent tuning include time-varying meta-data such as the temperature and precipitation at the time a given track was executed. We present results showing that, even when presented with tracks that are very far from cost-optimal, GUIDE can learn a set of preferences that closely mimics terrain choices made by humans.