13,364 research outputs found

    Hybrid approach of the fuzzy C-Means and the K-Nearest neighbors methods during the retrieve phase of dynamic case based reasoning for personalized Follow-up of learners in real time

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    The goal of adaptive learning systems is to help the learner achieve their goals and guide their learning. These systems make it possible to adapt the presentation of learning resources according to learners' needs, characteristics and learning styles, by offering them personalized courses. We propose an approach to an adaptive learning system that takes into account the initial learning profile based on Felder Silverman's learning style model in order to propose an initial learning path and the dynamic change of his behavior during the learning process using the Incremental Dynamic Case Based Reasoning approach to monitor and control its behavior in real time, based on the successful experiences of other learners, to personalize the learning. These learner experiences are grouped into homogeneous classes at the behavioral level, using the Fuzzy C-Means unsupervised machine learning method to facilitate the search for learners with similar behaviors using the supervised machine learning method K- Nearest Neighbors

    The Fourth Amendment in the Twenty-First Century: Technology, Privacy, and Human Emotions

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    Police and local political officials in Tampa FL argued that the FaceIt system promotes safety, but privacy advocates objected to the city\u27s recording or utilizing facial images without the victims\u27 consent, some staging protests against the FaceIt system. Privacy objects seem to be far more widely shared than this small protest might suggest

    Towards an Intelligent Tutor for Mathematical Proofs

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    Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

    MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

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    The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,

    Procedural-Reasoning Architecture for Applied Behavior Analysis-based Instructions

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    Autism Spectrum Disorder (ASD) is a complex developmental disability affecting as many as 1 in every 88 children. While there is no known cure for ASD, there are known behavioral and developmental interventions, based on demonstrated efficacy, that have become the predominant treatments for improving social, adaptive, and behavioral functions in children. Applied Behavioral Analysis (ABA)-based early childhood interventions are evidence based, efficacious therapies for autism that are widely recognized as effective approaches to remediation of the symptoms of ASD. They are, however, labor intensive and consequently often inaccessible at the recommended levels. Recent advancements in socially assistive robotics and applications of virtual intelligent agents have shown that children with ASD accept intelligent agents as effective and often preferred substitutes for human therapists. This research is nascent and highly experimental with no unifying, interdisciplinary, and integral approach to development of intelligent agents based therapies, especially not in the area of behavioral interventions. Motivated by the absence of the unifying framework, we developed a conceptual procedural-reasoning agent architecture (PRA-ABA) that, we propose, could serve as a foundation for ABA-based assistive technologies involving virtual, mixed or embodied agents, including robots. This architecture and related research presented in this disser- tation encompass two main areas: (a) knowledge representation and computational model of the behavioral aspects of ABA as applicable to autism intervention practices, and (b) abstract architecture for multi-modal, agent-mediated implementation of these practices

    Comparison of Simulation-Based Performance with Metrics of Critical Thinking Skills in Nursing Students: A Pilot Study

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    Alternative approaches to evaluating critical thinking skills are needed, as pencil and paper assessments may not accurately predict simulated or actual clinical performance. To ensure patient safety, it is imperative to determine how to best promote and measure critical thinking skills. Few studies have examined how these skills are related to performance in a simulated or actual clinical setting. The purpose of this study was to examine the relationship between metrics of critical thinking skills and performance in simulated clinical scenarios and identify predictors of simulation-based performance of nursing students in their last term of academic preparation. A convenience sample of 36 student nurses prepared at the diploma (n=14), associate (n=12), and baccalaureate (n=10) level in their last term of academic preparation participated in a measurement of critical thinking skills and simulation-based performance using videotaped vignettes (VTV), high-fidelity human simulation (HFHS), and two standardized tests: the California Critical Thinking Disposition Inventory (CCTDI) and California Critical Thinking Skills Test (CCTST). Simulation-based performance on the VTV and HFHS assessment was rated as "meeting" or "not meeting" overall expectations and in six categories: problem recognition, reports essential data, initiates appropriate nursing interventions, anticipates medical orders, provides rationale, and prioritizes the situation. Student scores on the CCTDI and CCTST were categorized as strong, average, or weak critical thinking disposition or skills. A majority (75.0%) of students did not meet overall performance expectations when assessed using VTV and HFHS. Those not meeting expectations had difficulty recognizing the clinical problem and reporting the appropriate findings to the physician. There was no significant difference between overall performance based on the method of simulation (VTV or HFHS). However, more students met performance expectations for the category of initiating nursing interventions (p=0.0002) using HFHS. The relationships between VTV performance and CCTDI or CCTST scores were not significant except for the relationship between the category of problem recognition and overall CCTST scores (Cramer's V = 0.444, p = 0.029). There was a statistically significant relationship between HFHS performance and overall CCTDI scores (Cramer's V = .413, p = 0.047). Gender, educational preparation, internship/residency participation, prior nursing aide experience, and number of classes using HFHS as a teaching method were not related to overall VTV or HFHS performance or scores on the CCTDI or CCTST. However, there was a significant relationship between age and overall CCTST scores (Cramer's V = .388, p = 0.029). The CCTDI, CCTST, and level of educational preparation were not statistically significant predictors of VTV performance. Student nurses' performance reflected difficulty meeting expectations when tested in both simulated settings. HFHS appeared to best approximate scores on a standardized metric of critical thinking. Further research is needed to determine if results of simulated performance predicts application of critical thinking skills in a clinical setting

    The Youth Discount: Old Enough to Do the Crime, Too Young to Do the Time

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    In a trilogy of cases, the Supreme Court applied the Eighth Amendment to the entire category of juvenile offenders, repudiated its “death is different” jurisprudence, and required states to consider youthfulness as a mitigating factor in sentencing. Roper v. Simmons prohibited states from executing offenders for murder they committed when younger than eighteen years of age.1 Roper reasoned that immature judgment, susceptibility to negative influences, and transitory personalities reduced youths’ culpability and barred the most severe sentence.2 Graham v. Florida extended Roper’s diminished responsibility rationale and prohibited states from imposing life without parole (LWOP) sentences on youths convicted of nonhomicide offenses,3 and repudiated the Court’s earlier Eighth Amendment position that “death is different.”4 Miller v. Alabama and Jackson v. Hobbs [Miller/Jackson], combined Roper and Graham’s diminished responsibility rationale with another strand of death penalty jurisprudence to bar mandatory LWOP sentences for youths convicted of murder,5 required judges to make individualized sentencing decisions, and emphasized the importance of youthfulness as a mitigating factor
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