Feedback is essential to guide performance in simulation-based training (SBT) and to refine learning. Generally outcomes improve when feedback is delivered with personalized tutoring that tailors specific guidance and adapts feedback to the learner in a one-to-on environment. Therefore, emulating by automation these adaptive aspects of human tutors in SBT systems should be an effective way to train individuals. This study investigates the efficacy of automating different types of feedback in a SBT system. These include adaptive bottom-up feedback (i.e., detailed feedback, changing to general as proficiency develops) and adaptive top-down feedback (i.e., general feedback, changing to detailed if performance fails to improve). Other types of non-adaptive feedback were included for performance comparisons as well as to examine the overall cognitive load. To test hypotheses, 130 participants were randomly assigned to five conditions. Two feedback conditions employed adaptive approaches (bottom-up and top-down), two used non-adaptive approaches (constant detailed and constant general), and one functioned as a control group (i.e., only a performance score was given). After preliminary training on the simulator system, participants completed four simulated search and rescue missions (three training missions and one transfer mission). After each training mission, all participants received feedback relative to the condition they were assigned. Overall performance on missions, knowledge post-test scores, and subjective cognitive load were measured and analyzed to determine the effectiveness of the type of feedback. Results indicate that: (1) feedback generally improves performance, confirming prior research; (2) performance for the two adaptive approaches (bottom-up vs. top-down did not differ significantly at the end of training, but the bottom-up group achieved higher performance levels significantly sooner; (3) performance for the bottom-up and constant detailed groups did not differ significantly, although the trend suggests that adaptive bottom-up feedback may yield significant results in further studies. Overall, these results have implications for the implementation of feedback in SBT and beyond for other computer-based training systems.

Self-excited systems are open loop unstable plants having a nonlinearity that prevents an exponentially increasing time response. The resulting limit cycle is induced by any slight disturbance that causes the response of the system to grow to the saturation level of the nonlinearity. Because there is no external disturbance, control of these self-excited systems requires that the open loop system dynamics are altered so that any unstable open loop poles are stabilized in the closed loop. This work examines a variety of adaptive control approaches for controlling a thermoacoustic instability, a physical self-excited system. Initially, a static feedback controller loopshaping design and associated system identification method is presented. This design approach is shown to effectively stabilize an unstable Rijke tube combustor while preventing the creation of additional controller induced instabilities. The loopshaping design method is then used in conjunction with a trained artificial neural network to demonstrate stabilizing control in the presence of changing plant dynamics over a wide variety of operating conditions. However, because the ANN is designed specifically for a single combustor/actuator arrangement, its limited portability is a distinct disadvantage. Filtered-X least mean squares (LMS) adaptive feedback control approaches are examined when applied to both stable and unstable plants. An identification method for approximating the relevant plant dynamics to be modeled is proposed and shown to effectively stabilize the self-excited system in simulations and experiments. The adaptive feedback controller is further analyzed for robust performance when applied to the stable, disturbance rejection control problem. It is shown that robust stability cannot be guaranteed because arbitrarily small errors in the plant model can generate gradient divergence and unstable feedback loops. Finally, a time-averaged-gradient (TAG) algorithm is investigated for use in controlling self-excited systems such as the thermoacoustic instability. The TAG algorithm is shown to be very effective in stabilizing the unstable dynamics using a variety of controller parameterizations, without the need for plant estimation information from the system to be controlled. / Ph. D.

Effective software testing identifies potential bugs and helps correct them, producing more reliable and maintainable software. As software development processes have evolved, incremental testing techniques have grown in popularity, particularly with introduction of test-driven development (TDD). However, many programmers struggle to adopt TDD's "test a little, code a little" approach and conventional computer science classrooms neglect evaluating software development as a process. In response, we explore influences on students' testing behaviors, effects of incremental testing strategies, and describe approaches to help computer science students adopt good testing practices. First, to understand students' perspectives and adoption of testing strategies, we investigated their attitudes toward different aspects of TDD. In addition, we observed trends in when and how thoroughly students tested their code and how these choices impacted the quality of their assignments. However, with insight into why students struggle to adopt incremental testing, we identified a need to assess their behaviors during the software development process as a departure from traditional product-oriented evaluation. By building upon an existing automated grading system, we developed an adaptive feedback system to provide customized incentives to reinforce incremental testing behaviors while students solved programming assignments. We investigated how students react to concrete testing goals and hint reward mechanisms and found approaches for identifying testing behaviors and influencing short-term behavioral change. Moreover, we discovered how students incorporate automated feedback systems into their software development strategies. Finally, we compared testing strategies students exhibited through analyzing five years and thousands of snapshots of students' code during development. Even when accounting for factors such as procrastinating on assignments, we found that testing early and consistently maintaining testing throughout development helps produce better quality code and tests. By applying our findings of student software development behaviors to effective testing strategies and teaching techniques, we developed a framework for adaptively scaffolding feedback to empower students to critically reflect over their code and adopt incremental testing approaches. / Ph. D.

Automated grading systems provide feedback to computer science students in a variety of ways, but often focus on incorrect program behaviors. These tools will provide indications of test case failures or runtime errors, but without debugging skills, students often become frus- trated when they don't know where to start. They know their code has defects, but finding the problem may be beyond their experience, especially for beginners. An additional concern is balancing the need to provide enough direction to be useful, without giving the student so much direction that they are effectively given the answer. Drawing on the experiences of the software engineering community, in this work we apply a technique called statistical fault location (SFL) to student program assignments. Using the GZoltar software tool, we applied this technique to a set of previously-submitted student assignments gathered from students in our introductory CS course, CS 1114: Introduction to Software Design. After a manual inspection of the student code, this exercise demonstrated that the SFL technique identifies the defective method in the first three most suspicious methods in the student's code 90% of the time. We then developed a plug-in for Web-CAT to allow new student submissions to be evaluated with the GZoltar SFL system. Additionally, we developed a tool to create a heat map visualization to show the results of the SFL evaluation overlaid on the student's source code. We deployed this toolset for use in CS 1114 in Fall 2017. We then surveyed the students about their perceptions of the utility of the visualization for helping them understand how to find and correct the defects in their code, versus not having access to the heat map. Their responses led to refinements in our presentation of the feedback. We also evaluated the performance of CS 1114 classes from two semesters and discovered that having the heat maps led to more frequent incremental improvements in their code, as well as reaching their highest correctness score on instructor-provided tests more quickly than students that did not have access to the heat maps. Finally, we suggest several directions for future enhancements to the feedback interface. / Doctor of Philosophy / Automated grading systems provide feedback to computer science students in a variety of ways, but often focus on incorrect program behaviors. These tools will provide indications of test case failures or runtime errors, but without debugging skills, students often become frus- trated when they don't know where to start. They know their code has defects, but finding the problem may be beyond their experience, especially for beginners. An additional concern is balancing the need to provide enough direction to be useful, without giving the student so much direction that they are effectively given the answer. Drawing on the experiences of the software engineering community, in this work we apply a technique called statistical fault location (SFL) to student program assignments. Using the GZoltar software tool, we applied this technique to a set of previously-submitted student assignments gathered from students in our introductory CS course, CS 1114: Introduction to Software Design. After a manual inspection of the student code, this exercise demonstrated that the SFL technique identifies the defective method in the first three most suspicious methods in the student's code 90% of the time. We then developed a plug-in for Web-CAT to allow new student submissions to be evaluated with the GZoltar SFL system. Additionally, we developed a tool to create a heat map visualization to show the results of the SFL evaluation overlaid on the student's source code. We deployed this toolset for use in CS 1114 in Fall 2017. We then surveyed the students about their perceptions of the utility of the visualization for helping them understand how to find and correct the defects in their code, versus not having access to the heat map. Their responses led to refinements in our presentation of the feedback. We also evaluated the performance of CS 1114 classes from two semesters and discovered that having the heat maps led to more frequent incremental improvements in their code, as well as reaching their highest correctness score on instructor-provided tests more quickly than students that did not have access to the heat maps. Finally, we suggest several directions for future enhancements to the feedback interface.

<p>During the last years the need for real-time database services has increased due to the growing number of data-intensive applications needing to enforce real-time constraints. The COMponent-based Embedded real-Time database (COMET) is a real-time database developed to meet these demands. COMET is developed using the AspeCtual COmponent-based Real-time system Development (ACCORD) design method, and consists of a number of components and aspects, which can be composed into a number of different configurations depending on system demands, e.g., Quality of Service (QoS) management can be used in unpredictable environments. </p><p>In embedded systems with requirementson high up-time it may not be possible to temporarily shut down the system for reconfiguration. Instead it is desirable to enable dynamic reconfiguration of the system, exchanging components during run-time. This in turn sets demands on the feedback control of the system to adjust to these new conditions, since a new time variant system has been created. </p><p>This thesis project implements improvements in COMET to create a more stable database suitable for further development. A mechanism for dynamic reconfiguration of COMET is implemented, thus, enabling components and aspects to be swapped during run-time. Adaptive feedback control algorithms are also implemented in order to better adjust to workload variations and database reconfiguration.</p>

During the last years the need for real-time database services has increased due to the growing number of data-intensive applications needing to enforce real-time constraints. The COMponent-based Embedded real-Time database (COMET) is a real-time database developed to meet these demands. COMET is developed using the AspeCtual COmponent-based Real-time system Development (ACCORD) design method, and consists of a number of components and aspects, which can be composed into a number of different configurations depending on system demands, e.g., Quality of Service (QoS) management can be used in unpredictable environments. In embedded systems with requirementson high up-time it may not be possible to temporarily shut down the system for reconfiguration. Instead it is desirable to enable dynamic reconfiguration of the system, exchanging components during run-time. This in turn sets demands on the feedback control of the system to adjust to these new conditions, since a new time variant system has been created. This thesis project implements improvements in COMET to create a more stable database suitable for further development. A mechanism for dynamic reconfiguration of COMET is implemented, thus, enabling components and aspects to be swapped during run-time. Adaptive feedback control algorithms are also implemented in order to better adjust to workload variations and database reconfiguration.

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Dans cette thèse, nous proposons des solutions pour la conception de systèmes de contrôle actif de vibration robustes (AVC). Le manuscrit de thèse comporte deux grandes parties.Dans la première, les problèmes d'incertitude paramétrique dans les systèmes de contrôle actif de vibration sont étudiés. En plus des incertitudes sur la fréquence des perturbations, nous avons trouvé que la présence de zéros complexes peu amortis soulevait des problèmes de conception difficiles, même pour des systèmes et des modèles parfaitement connus. Dans ce contexte, nous avons proposé des solutions pour le problème linéaire. Une procédure améliorée d'identification en boucle fermée a été développée pour réduire les incertitudes dans l'identification de ces zéros. Pour traiter les incertitudes sur la perturbation, l'adaptation de la fréquence est de toute façon incontournable.La seconde partie est consacrée au développement et/ou à l'amélioration de deux algorithmes, désormais classiques, de compensation par feedback adaptatif direct, fondés sur la paramétrisation de Youla-Kučera. Le premier résulte de l'amélioration d'un précédent travail (Landau et al., 2005) ; les contributions concernent la synthèse du contrôleur central robuste et l'utilisation optionnelle de la surparamétrisation du filtre Q-FIR (réponse à temps fini) avec pour effet de minimiser l'effet « waterbed » sur la fonction de sensibilité de sortie. Le second algorithme présente une structure hybride directe/indirecte qui utilise un filtre Q-IIR (à temps de réponse infini). Les améliorations sont dues principalement au dénominateur du filtre, obtenu à partir d'une estimation de la perturbation. Cette solution permet également de simplifier la conception du contrôleur central.Les algorithmes ont été testés, comparés et validés sur un procédé réel du laboratoire Gipsa-lab, dans le cadre d'un benchmark international. / In this thesis, solutions for the design of robust Active Vibration Control (AVC) systems are presented. The thesis report is composed of two main parts.In the first part of the thesis uncertainties issues in Active Vibration Control systems are examined. In addition of the uncertainties on the frequency of the disturbances it has been found that the presence of low damped complex zeros raise difficult design problems even if plant and models are perfectly known. Solutions for the linear control in this context have been proposed. In order to reduce the uncertainties in the identification of low complex zeros and improved closed loop identification procedure has been developed. To handle the uncertainties on the disturbance frequency adaptation has any way to be used.The second part is concerned with the further development and/or the improvement of the now classical direct adaptive feedback compensation algorithms using Youla Kucera controller parametrization. Two new solutions have been proposed in this context. The first one results from the improvement of a previous work (Landau et al., 2005). The contributions are a new robust central controller design to the optional use of over parameterization of the Q-FIR filter which aims to ensure a small waterbed effect for the output sensitivity function and therefore reducing the unwanted amplification. The second algorithm presents a mixed direct/indirect structure which uses a Q-IIR filter. The improvements are mainly the effect of the Q filter denominator, which is obtained from a disturbance identification. This solution in addition drastically simplifies the design of the central controller.The algorithms have been tested, compared and validated on an international benchmark setup available at the Control System Department of GIPSA-Lab, Grenoble, France.