Mental Representation and the Construction of Conceptual Understanding in Electronics Education

Learning about abstract electronics concepts can be difficult due to the hidden nature of the phenomena of interest. Developing understanding about electronics is therefore challenging because voltage cannot be readily observed; only the outcomes of the behaviour of voltage can be observed. Consequently modelling the phenomena of interest becomes a crucial factor in supporting learners in their development of knowledge and understanding. Visualisation skills have been promoted as important when modelling knowledge in different forms, supporting learners in their development of knowledge and understanding. Current research about electronics education, however, has tended to focus on learners’ misconceptions, experimental methods and interventions focusing on theoretical aspects of knowledge. Perspectives on learners’ actual constructions of knowledge in practice are not common. The aim of this research study, therefore, was to explore the use of external visual representations in support of learning about electronics concepts, within the context of Secondary Design and Technology education. The study adopts a case study approach and uses an interpretative cross-case synthesis methodology to explore a specific case of representation use among one class of Year 10 students. The analytical framework is designed to focus on the translation of and transition between multiple representations, including computer program code, and the representation of phenomena at three levels of representation: observable, symbolic and abstract. Data collection involved the observation of learners engaged with learning activities, documents collected from these activities, individual semi-structured interviews and participant characteristics data collected from course records. The findings show that common processes of learning are accompanied by individual developments in meaning and understanding. Individual understanding was characterised with the creation of four cognitive profiles representing key learner constructs. Understanding about abstract concepts was shown to benefit from representations where concrete referents linked with practical experience. Electronics understanding was also shown to benefit from the explanatory use of program code as a supporting method with which to model and simulate circuit behaviour. The research approach involving the close observation of learners engaging with learning activities was found to provide a greater understanding of learners’ approaches to learning in practice. The outcomes are applied to the practice of teaching electronics and modifications to the research are suggested for future researchers interested in the issues of teaching, learning and concept development in electronics education

Mental imagery and software visualization in high-performance software development teams

This paper considers the relationship between mental imagery and software visualization in professional, high-performance software development. It presents overviews of four empirical studies of professional software developers in high-performing teams: (1) expert programmers' mental imagery, (2) how experts externalize their mental imagery as part of teamwork, (3) experts' use of commercially available visualization software, and (4) what tools experts build themselves, how they use the tools they build for themselves, and why they build tools for themselves. Through this series of studies, the paper provides insight into a relationship between how experts reason about and imagine solutions, and their use of and requirements for external representations and software visualization. In particular, it provides insight into how experts use visualization in reasoning about software design, and how their requirements for the support of design tasks differ from those for the support of other software development tasks. The paper draws on theory from other disciplines to explicate issues in this area, and it discusses implications for future work in this field

Detecting Prolog programming techniques using abstract interpretation

There have been a number of attempts at developing intelligent tutoring systems (ITSs) for teaching students various programming languages. An important component of such an ITS is a debugger capable of recognizing errors in the code the student writes and possibly suggesting ways of correcting such errors. The debugging process involves a wealth of knowledge about the programming language, the student and the individual problem at hand, and an automated debugging component makes use of a number of tools which apply this knowledge. Successive ITSs have incorporated a wider range of knowledge and more powerful tools. The research described in this thesis should be seen as carrying on with this succes¬ sion. Specifically, we attempt to enhance an existing Prolog ITS (PITS) debugger called APR0P0S2 developed by Looi. The enhancements take the form of a richer language with which to describe Prolog code and more powerful tools with which constructs in this language may be detected in Prolog code. The richer language is based on the notion of programming techniques—common patterns in code which capture in some sense an expert's understanding of Prolog. The tools are based on Prolog abstract interpretation—a program analysis method for inferring dynamic properties of code. Our research makes contributions to both these areas. We develop a language for describing classes of Prolog programming techniques that manipulate data-structures. We define classes in this language for common Prolog techniques such as accumulator pairs and difference structures. We use abstract interpretation to infer the dynamic features with which techniques are described. We develop a general framework for abstract interpretation which is described in Prolog, so leading directly to an implementation. We develop two abstract domains—one which infers general data flow information about the code and one which infers particularly detailed type information—and describe the implementation of the former

Recolha e conceptualização de experiências de atividades robóticas baseadas em planos para melhoria de competências no longo prazo

Robot learning is a prominent research direction in intelligent robotics. Robotics involves dealing with the issue of integration of multiple technologies, such as sensing, planning, acting, and learning. In robot learning, the long term goal is to develop robots that learn to perform tasks and continuously improve their knowledge and skills through observation and exploration of the environment and interaction with users. While significant research has been performed in the area of learning motor behavior primitives, the topic of learning high-level representations of activities and classes of activities that, decompose into sequences of actions, has not been sufficiently addressed. Learning at the task level is key to increase the robots’ autonomy and flexibility. High-level task knowledge is essential for intelligent robotics since it makes robot programs less dependent on the platform and eases knowledge exchange between robots with different kinematics. The goal of this thesis is to contribute to the development of cognitive robotic capabilities, including supervised experience acquisition through human-robot interaction, high-level task learning from the acquired experiences, and task planning using the acquired task knowledge. A framework containing the required cognitive functions for learning and reproduction of high-level aspects of experiences is proposed. In particular, we propose and formalize the notion of Experience-Based Planning Domains (EBPDs) for long-term learning and planning. A human-robot interaction interface is used to provide a robot with step-by-step instructions on how to perform tasks. Approaches to recording plan-based robot activity experiences including relevant perceptions of the environment and actions taken by the robot are presented. A conceptualization methodology is presented for acquiring task knowledge in the form of activity schemata from experiences. The conceptualization approach is a combination of different techniques including deductive generalization, different forms of abstraction and feature extraction. Conceptualization includes loop detection, scope inference and goal inference. Problem solving in EBPDs is achieved using a two-layer problem solver comprising an abstract planner, to derive an abstract solution for a given task problem by applying a learned activity schema, and a concrete planner, to refine the abstract solution towards a concrete solution. The architecture and the learning and planning methods are applied and evaluated in several real and simulated world scenarios. Finally, the developed learning methods are compared, and conditions where each of them has better applicability are discussed.Aprendizagem de robôs é uma direção de pesquisa proeminente em robótica inteligente. Em robótica, é necessário lidar com a questão da integração de várias tecnologias, como percepção, planeamento, atuação e aprendizagem. Na aprendizagem de robôs, o objetivo a longo prazo é desenvolver robôs que aprendem a executar tarefas e melhoram continuamente os seus conhecimentos e habilidades através da observação e exploração do ambiente e interação com os utilizadores. A investigação tem-se centrado na aprendizagem de comportamentos básicos, ao passo que a aprendizagem de representações de atividades de alto nível, que se decompõem em sequências de ações, e de classes de actividades, não tem sido suficientemente abordada. A aprendizagem ao nível da tarefa é fundamental para aumentar a autonomia e a flexibilidade dos robôs. O conhecimento de alto nível permite tornar o software dos robôs menos dependente da plataforma e facilita a troca de conhecimento entre robôs diferentes. O objetivo desta tese é contribuir para o desenvolvimento de capacidades cognitivas para robôs, incluindo aquisição supervisionada de experiência através da interação humano-robô, aprendizagem de tarefas de alto nível com base nas experiências acumuladas e planeamento de tarefas usando o conhecimento adquirido. Propõe-se uma abordagem que integra diversas funcionalidades cognitivas para aprendizagem e reprodução de aspetos de alto nível detetados nas experiências acumuladas. Em particular, nós propomos e formalizamos a noção de Domínio de Planeamento Baseado na Experiência (Experience-Based Planning Domain, or EBPD) para aprendizagem e planeamento num âmbito temporal alargado. Uma interface para interação humano-robô é usada para fornecer ao robô instruções passo-a-passo sobre como realizar tarefas. Propõe-se uma abordagem para extrair experiências de atividades baseadas em planos, incluindo as percepções relevantes e as ações executadas pelo robô. Uma metodologia de conceitualização é apresentada para a aquisição de conhecimento de tarefa na forma de schemata a partir de experiências. São utilizadas diferentes técnicas, incluindo generalização dedutiva, diferentes formas de abstracção e extração de características. A metodologia inclui detecção de ciclos, inferência de âmbito de aplicação e inferência de objetivos. A resolução de problemas em EBPDs é alcançada usando um sistema de planeamento com duas camadas, uma para planeamento abstrato, aplicando um schema aprendido, e outra para planeamento detalhado. A arquitetura e os métodos de aprendizagem e planeamento são aplicados e avaliados em vários cenários reais e simulados. Finalmente, os métodos de aprendizagem desenvolvidos são comparados e as condições onde cada um deles tem melhor aplicabilidade são discutidos.Programa Doutoral em Informátic

Techniques for organizational memory information systems

The KnowMore project aims at providing active support to humans working on knowledge-intensive tasks. To this end the knowledge available in the modeled business processes or their incarnations in specific workflows shall be used to improve information handling. We present a representation formalism for knowledge-intensive tasks and the specification of its object-oriented realization. An operational semantics is sketched by specifying the basic functionality of the Knowledge Agent which works on the knowledge intensive task representation. The Knowledge Agent uses a meta-level description of all information sources available in the Organizational Memory. We discuss the main dimensions that such a description scheme must be designed along, namely information content, structure, and context. On top of relational database management systems, we basically realize deductive object- oriented modeling with a comfortable annotation facility. The concrete knowledge descriptions are obtained by configuring the generic formalism with ontologies which describe the required modeling dimensions. To support the access to documents, data, and formal knowledge in an Organizational Memory an integrated domain ontology and thesaurus is proposed which can be constructed semi-automatically by combining document-analysis and knowledge engineering methods. Thereby the costs for up-front knowledge engineering and the need to consult domain experts can be considerably reduced. We present an automatic thesaurus generation tool and show how it can be applied to build and enhance an integrated ontology /thesaurus. A first evaluation shows that the proposed method does indeed facilitate knowledge acquisition and maintenance of an organizational memory

A phenomenological analysis of an instructional systems design creative project

This research paper is a phenomenological analysis of a creative project involving University of Northern Iowa undergraduate art students in the planning and creation of visual illustrations, graphic design concepts, .html documents, and imagery for a world wide web intranet/lnternet virtual space. This analysis looks at instructional design as a creative process and the phenomenology of the UNI Art/Cat (Art Resources Technology/Computer Assisted Training) computer laboratory. The mission, goals, and objectives of the creative project, experiential and experimental philosophies of education, and the phenomenologies of the instructional design process are the main considerations. The methodology of this thesis is primarily concerned with action research and research as lived experience. The generational aspects of computer hardware and software and the affective aspects of the evolution of the infrastructure upon instructional development is examined. This generation of techno-apparatus includes the Macintosh G3 Personal Computer in a network environment, Afga and Hewlett Packard Flatbed Scanners, Polaroid Slide Scanners, Adobe Graphic Design Software, and Symantec Visual Page Web Design Software. Commentary on the social and bureaucratic considerations in this particular creative project and discussion of the collaboration with UNI Art Department administration, faculty, and students is included with the final conclusions and recommendations

Prescriptive formalism for constructing domain-specific evolutionary algorithms

It has been widely recognised in the computational intelligence and machine learning communities that the key to understanding the behaviour of learning algorithms is to understand what representation is employed to capture and manipulate knowledge acquired during the learning process. However, traditional evolutionary algorithms have tended to employ a fixed representation space (binary strings), in order to allow the use of standardised genetic operators. This approach leads to complications for many problem domains, as it forces a somewhat artificial mapping between the problem variables and the canonical binary representation, especially when there are dependencies between problem variables (e.g. problems naturally defined over permutations). This often obscures the relationship between genetic structure and problem features, making it difficult to understand the actions of the standard genetic operators with reference to problem-specific structures. This thesis instead advocates m..