Exploring teachers' use of multiple intelligences with elementary-aged exceptional students

The purpose ofthis qualitative study was to explore teachers' reflections on Multiple Intelligences theory and the processes they engage in when using the theory with elementary-aged exceptional students. FOllr public school teachers took part in the study. An introductory observation visit, semistructured in-depth interviews, field notes, and teachers' own written reflections served as data sources. Content-analysis was applied to review the data for thenles related to the research topic. The findings indicated several benefits of using Multiple Intelligences. This tlleory appeared to affect teachers' views of exceptionalleamers, directing the teachers' fOClIS to the students' potentials. It also seemed to have value for assisting teachers in planning an inclusive approacll, enhancing exceptional students' self-esteem, developing nletacognition, and prolTIoting cognitive engagement. Finally, the findings suggest that Multiple Intelligences has inlplications for teachers' professional development to reach a more diverse range of students.

Beyond g : putting multiple intelligences theory to the test /

The purpose of this study was to investigate Howard Gardner's (1983) Multiple Intelligences theory, which proposes that there are eight independent intelligences: Linguistic, Spatial, Logical/Mathematical, Interpersonal, Intrapersonal, Naturalistic, Bodily-Kinesthetic, and Musical. To explore Gardner's theory, two measures of each ability area were administered to 200 participants. Each participant also completed a measure of general cognitive ability, a personality inventory, an ability self-rating scale, and an ability self-report questionnaire. Nonverbal measures were included for most intelligence domains, and a wide range of content was sampled in Gardner's domains. Results showed that all tests of purely cognitive abilities were significantly correlated with the measure of general cognitive ability, whereas Musical, Bodily-Kinesthetic, and one of the Intrapersonal measures were not. Contrary to what Multiple Intelligences theory would seem to predict, correlations among the tests revealed a positive manifold and factor analysis indicated a large factor of general intelligence, with a mathematical reasoning test and a classification task from the Naturalistic domain having the highest ^- loadings. There were only minor sex differences in performance on the ability tests. Participants' self-estimates of ability were significantly and positively correlated with actual performance in some, but not all, intelligences. With regard to personality, a hypothesized association between Openness to Experience and crystallized intelligence was supported. The implications of the findings in regards to the nature of mental abilities were discussed, and recommendations for further research were made.

Power, Contextual Intelligence and Leadership: Research Approaches for Understanding Participatory Community Climate Change Adaptation

Analysis of power in natural resources management is important as multiple stakeholders interact within complex, social-ecological systems. As a sub-set of these interactions, community climate change adaptation is increasingly using participatory processes to address issues of local concern. While some attention has been paid to power relations in this respect, e.g. evaluating international climate regimes or assessing vulnerability as part of integrated impact assessments, little attention has been paid to how a structured assessment of power could facilitate real adaptation and increase the potential for successful participatory processes. This paper surveys how the concept of power is currently being applied in natural resources management and links these ideas to agency and leadership for climate change adaptation. By exploring behavioural research on destructive leadership, a model is developed for informing participatory climate change adaptation. The working paper then concludes with a discussion of developing research questions in two specific areas - examining barriers to adaptation and mapping the evolution of specific participatory processes for climate change adaptation.

Modeling Metal Protein Complexes from Experimental Extended X-ray Absorption Fine Structure using Computational Intelligence

Experimental Extended X-ray Absorption Fine Structure (EXAFS) spectra carry information about the chemical structure of metal protein complexes. However, pre- dicting the structure of such complexes from EXAFS spectra is not a simple task. Currently methods such as Monte Carlo optimization or simulated annealing are used in structure refinement of EXAFS. These methods have proven somewhat successful in structure refinement but have not been successful in finding the global minima. Multiple population based algorithms, including a genetic algorithm, a restarting ge- netic algorithm, differential evolution, and particle swarm optimization, are studied for their effectiveness in structure refinement of EXAFS. The oxygen-evolving com- plex in S1 is used as a benchmark for comparing the algorithms. These algorithms were successful in finding new atomic structures that produced improved calculated EXAFS spectra over atomic structures previously found.

The use of artificial intelligence algorithms to guide surgical treatment of adolescent idiopathic scoliosis

La scoliose idiopathique de l’adolescent (SIA) est une déformation tri-dimensionelle du rachis. Son traitement comprend l’observation, l’utilisation de corsets pour limiter sa progression ou la chirurgie pour corriger la déformation squelettique et cesser sa progression. Le traitement chirurgical reste controversé au niveau des indications, mais aussi de la chirurgie à entreprendre. Malgré la présence de classifications pour guider le traitement de la SIA, une variabilité dans la stratégie opératoire intra et inter-observateur a été décrite dans la littérature. Cette variabilité s’accentue d’autant plus avec l’évolution des techniques chirurgicales et de l’instrumentation disponible. L’avancement de la technologie et son intégration dans le milieu médical a mené à l’utilisation d’algorithmes d’intelligence artificielle informatiques pour aider la classification et l’évaluation tridimensionnelle de la scoliose. Certains algorithmes ont démontré être efficace pour diminuer la variabilité dans la classification de la scoliose et pour guider le traitement. L’objectif général de cette thèse est de développer une application utilisant des outils d’intelligence artificielle pour intégrer les données d’un nouveau patient et les évidences disponibles dans la littérature pour guider le traitement chirurgical de la SIA. Pour cela une revue de la littérature sur les applications existantes dans l’évaluation de la SIA fut entreprise pour rassembler les éléments qui permettraient la mise en place d’une application efficace et acceptée dans le milieu clinique. Cette revue de la littérature nous a permis de réaliser que l’existence de “black box” dans les applications développées est une limitation pour l’intégration clinique ou la justification basée sur les évidence est essentielle. Dans une première étude nous avons développé un arbre décisionnel de classification de la scoliose idiopathique basé sur la classification de Lenke qui est la plus communément utilisée de nos jours mais a été critiquée pour sa complexité et la variabilité inter et intra-observateur. Cet arbre décisionnel a démontré qu’il permet d’augmenter la précision de classification proportionnellement au temps passé à classifier et ce indépendamment du niveau de connaissance sur la SIA. Dans une deuxième étude, un algorithme de stratégies chirurgicales basé sur des règles extraites de la littérature a été développé pour guider les chirurgiens dans la sélection de l’approche et les niveaux de fusion pour la SIA. Lorsque cet algorithme est appliqué à une large base de donnée de 1556 cas de SIA, il est capable de proposer une stratégie opératoire similaire à celle d’un chirurgien expert dans prêt de 70% des cas. Cette étude a confirmé la possibilité d’extraire des stratégies opératoires valides à l’aide d’un arbre décisionnel utilisant des règles extraites de la littérature. Dans une troisième étude, la classification de 1776 patients avec la SIA à l’aide d’une carte de Kohonen, un type de réseaux de neurone a permis de démontrer qu’il existe des scoliose typiques (scoliose à courbes uniques ou double thoracique) pour lesquelles la variabilité dans le traitement chirurgical varie peu des recommandations par la classification de Lenke tandis que les scolioses a courbes multiples ou tangentielles à deux groupes de courbes typiques étaient celles avec le plus de variation dans la stratégie opératoire. Finalement, une plateforme logicielle a été développée intégrant chacune des études ci-dessus. Cette interface logicielle permet l’entrée de données radiologiques pour un patient scoliotique, classifie la SIA à l’aide de l’arbre décisionnel de classification et suggère une approche chirurgicale basée sur l’arbre décisionnel de stratégies opératoires. Une analyse de la correction post-opératoire obtenue démontre une tendance, bien que non-statistiquement significative, à une meilleure balance chez les patients opérés suivant la stratégie recommandée par la plateforme logicielle que ceux aillant un traitement différent. Les études exposées dans cette thèse soulignent que l’utilisation d’algorithmes d’intelligence artificielle dans la classification et l’élaboration de stratégies opératoires de la SIA peuvent être intégrées dans une plateforme logicielle et pourraient assister les chirurgiens dans leur planification préopératoire.

3D path planning with novel multiple 2D layered approach for complex human-robot interaction

Navigating cluttered indoor environments is a difficult problem in indoor service robotics. The Acroboter concept, a novel approach to indoor locomotion, represents unique opportunity to avoid obstacles in indoor environments by navigating the ceiling plane. This mode of locomotion requires the ability to accurately detect obstacles, and plan 3D trajectories through the environment. This paper presents the development of a resilient object tracking system, as well as a novel approach to generating 3D paths suitable for such robot configurations. Distributed human-machine interfacing allowing simulation previewing of actions is also considered in the developed system architecture.

Enterprise intelligence: searching for a single version of the truth

Multiple versions of information and associated problems are well documented in both academic research and industry best practices. Many solutions have proposed a single version of the truth, with Business intelligence being adopted by many organizations. Business Intelligence (BI), however, is largely based on the collection of data, processing and presentation of information to meet different stakeholders’ requirement. This paper reviews the promise of Enterprise Intelligence, which promises to support decision-making based on a defined strategic understanding of the organizations goals and a unified version of the truth.

Dynamic distributed lanes: motion planning for multiple autonomous vehicles

Unorganized traffic is a generalized form of travel wherein vehicles do not adhere to any predefined lanes and can travel in-between lanes. Such travel is visible in a number of countries e.g. India, wherein it enables a higher traffic bandwidth, more overtaking and more efficient travel. These advantages are visible when the vehicles vary considerably in size and speed, in the absence of which the predefined lanes are near-optimal. Motion planning for multiple autonomous vehicles in unorganized traffic deals with deciding on the manner in which every vehicle travels, ensuring no collision either with each other or with static obstacles. In this paper the notion of predefined lanes is generalized to model unorganized travel for the purpose of planning vehicles travel. A uniform cost search is used for finding the optimal motion strategy of a vehicle, amidst the known travel plans of the other vehicles. The aim is to maximize the separation between the vehicles and static obstacles. The search is responsible for defining an optimal lane distribution among vehicles in the planning scenario. Clothoid curves are used for maintaining a lane or changing lanes. Experiments are performed by simulation over a set of challenging scenarios with a complex grid of obstacles. Additionally behaviours of overtaking, waiting for a vehicle to cross and following another vehicle are exhibited.

Multiple camera people detection and tracking using support integration

This paper proposes a method to locate and track people by combining evidence from multiple cameras using the homography constraint. The proposed method use foreground pixels from simple background subtraction to compute evidence of the location of people on a reference ground plane. The algorithm computes the amount of support that basically corresponds to the ""foreground mass"" above each pixel. Therefore, pixels that correspond to ground points have more support. The support is normalized to compensate for perspective effects and accumulated on the reference plane for all camera views. The detection of people on the reference plane becomes a search for regions of local maxima in the accumulator. Many false positives are filtered by checking the visibility consistency of the detected candidates against all camera views. The remaining candidates are tracked using Kalman filters and appearance models. Experimental results using challenging data from PETS`06 show good performance of the method in the presence of severe occlusion. Ground truth data also confirms the robustness of the method. (C) 2010 Elsevier B.V. All rights reserved.

Combining artificial intelligence and human problem solving: Proposing a new architecture for ITS's

This paper shows a comparative study between the Artificial Intelligence Problem Solving and the Human Problem Solving. The study is based on the solution by many ways of problems proposed via multiple-choice questions. General techniques used by humans to solve this kind of problems are grouped in blocks and each block is divided in steps. A new architecture for ITS - Intelligent Tutoring System is proposed to support experts' knowledge representation and novices' activities. Problems are represented by a text and feasible answers with particular meaning and form, to be rigorously analyzed by the solver to find the right one. Paths through a conceptual space of states represent each right solution.

Machine vision and planning applied on assembby line of multiple products

This article describes the use of Artificial Intelligence (IA) techniques applied in cells of a manufacturing system. Machine Vision was used to identify pieces and their positions of two different products to be assembled in the same productive line. This information is given as input for an IA planner embedded in the manufacturing system. Therefore, initial and final states are sent automatically to the planner capable to generate assembly plans for a robotic cell, in real time.

Cognitive rehabilitation of working memory in juvenile multiple sclerosis-effects on cognitive functioning, functional MRI and network related connectivity.

PURPOSE To assess possible effects of working memory (WM) training on cognitive functionality, functional MRI and brain connectivity in patients with juvenile MS. METHODS Cognitive status, fMRI and inter-network connectivity were assessed in 5 cases with juvenile MS aged between 12 and 18 years. Afterwards they received a computerized WM training for four weeks. Primary cognitive outcome measures were WM (visual and verbal) and alertness. Activation patterns related to WM were assessed during fMRI using an N-Back task with increasing difficulty. Inter-network connectivity analyses were focused on fronto-parietal (left and right), default-mode (dorsal and ventral) and the anterior salience network. Cognitive functioning, fMRI and inter-network connectivity were reassessed directly after the training and again nine months following training. RESULTS Response to treatment was seen in two patients. These patients showed increased performance in WM and alertness after the training. These behavioural changes were accompanied by increased WM network activation and systematic changes in inter-network connectivity. The remaining participants were non-responders to treatment. Effects on cognitive performance were maintained up to nine months after training, whereas effects observed by fMRI disappeared. CONCLUSIONS Responders revealed training effects on all applied outcome measures. Disease activity and general intelligence may be factors associated with response to treatment.