Practical, appropriate, empirically-validated guidelines for designing educational games

There has recently been a great deal of interest in the potential of computer games to function as innovative educational tools. However, there is very little evidence of games fulfilling that potential. Indeed, the process of merging the disparate goals of education and games design appears problematic, and there are currently no practical guidelines for how to do so in a coherent manner. In this paper, we describe the successful, empirically validated teaching methods developed by behavioural psychologists and point out how they are uniquely suited to take advantage of the benefits that games offer to education. We conclude by proposing some practical steps for designing educational games, based on the techniques of Applied Behaviour Analysis. It is intended that this paper can both focus educational games designers on the features of games that are genuinely useful for education, and also introduce a successful form of teaching that this audience may not yet be familiar with

Physically-based auralization : design, implementation, and evaluation

The aim of this research is to implement an auralization system that renders audible a 3D model of an acoustic environment. The design of such a system is an iterative process where successive evaluation of auralization quality is utilized to further refine the model and develop the rendering methods. The work can be divided into two parts corresponding to design and implementation of an auralization system and evaluation of the system employing objective and subjective criteria. The presented auralization method enables both static and dynamic rendering. In dynamic rendering positions and orientations of sound sources, surfaces, or a listener can change. These changes are allowed by modeling the direct sound and early reflections with the image-source method. In addition, the late reverberation is modeled with a time-invariant recursive digital filter structure. The core of the thesis deals with the processing of image sources for auralization. The sound signal emitted by each image source is processed with digital filters modeling such acoustic phenomena as sound source directivity, distance delay and attenuation, air and material absorption, and the characteristics of spatial hearing. The digital filter design and implementation of these filters are presented in detail. The traditional image-source method has also been extended to handle diffraction in addition to specular reflections. The evaluation of quality of the implemented auralization system was performed by comparing recorded and auralized soundtracks subjectively. The compared soundtracks were prepared by recording sound signals in a real room and by auralizing these signals with a 3D model of the room. The auralization quality was assessed with objective and subjective methods. The objective analysis was based on both traditional room acoustic criteria and on a simplified auditory model developed for this purpose. This new analysis method mimics the behavior of human cochlea. Therefore, with the developed method, impulse responses and sound signals can be visualized with similar time and frequency resolution as human hearing applies. The evaluation was completed subjectively by conducting listening tests. The utilized listening test methodology is explained and the final results are presented. The results show that the implemented auralization system provides plausible and natural sounding auralizations in rooms similar to the lecture room employed for evaluation.reviewe

Characterizing dynamically evolving functional networks in humans with application to speech

Understanding how communication between brain areas evolves to support dynamic function remains a fundamental challenge in neuroscience. One approach to this question is functional connectivity analysis, in which statistical coupling measures are employed to detect signatures of interactions between brain regions. Because the brain uses multiple communication mechanisms at different temporal and spatial scales, and because the neuronal signatures of communication are often weak, powerful connectivity inference methodologies require continued development specific to these challenges. Here we address the challenge of inferring task-related functional connectivity in brain voltage recordings. We first develop a framework for detecting changes in statistical coupling that occur reliably in a task relative to a baseline period. The framework characterizes the dynamics of connectivity changes, allows inference on multiple spatial scales, and assesses statistical uncertainty. This general framework is modular and applicable to a wide range of tasks and research questions. We demonstrate the flexibility of the framework in the second part of this thesis, in which we refine the coupling statistics and hypothesis tests to improve statistical power and test different proposed connectivity mechanisms. In particular, we introduce frequency domain coupling measures and define test statistics that exploit theoretical properties and capture known sampling variability. The resulting test statistics use correlation, coherence, canonical correlation, and canonical coherence to infer task-related changes in coupling. Because canonical correlation and canonical coherence are not commonly used in functional connectivity analyses, we derive the theoretical values and statistical estimators for these measures. In the third part of this thesis, we present a sample application of these techniques to electrocorticography data collected during an overt reading task. We discuss the challenges that arise with task-related human data, which is often noisy and underpowered, and present functional connectivity results in the context of traditional and contemporary within-electrode analytics. In two of nine subjects we observe time-domain and frequency-domain network changes that accord with theoretical models of information routing during motor processing. Taken together, this work contributes a methodological framework for inferring task-related functional connectivity across spatial and temporal scales, and supports insight into the rapid, dynamic functional coupling of human speech

Analytic Provenance for Software Reverse Engineers

Reverse engineering is a time-consuming process essential to software-security tasks such as malware analysis and vulnerability discovery. During the process, an engineer will follow multiple leads to determine how the software functions. The combination of time and possible explanations makes it difficult for the engineers to maintain a context of their findings within the overall task. Analytic provenance tools have demonstrated value in similarly complex fields that require open-ended exploration and hypothesis vetting. However, they have not been explored in the reverse engineering domain. This dissertation presents SensorRE, the first analytic provenance tool designed to support software reverse engineers. A semi-structured interview with experts led to the design and implementation of the system. We describe the visual interfaces and their integration within an existing software analysis tool. SensorRE automatically captures user\u27s sense making actions and provides a graph and storyboard view to support further analysis. User study results with both experts and graduate students demonstrate that SensorRE is easy to use and that it improved the participants\u27 exploration process