Knowledge restructing and the development of expertise in computer programming

This thesis reports a number of empirical studies exploring the development of expertise in computer programming. Experiments 1 and 2 are concerned with the way in which the possession of design experience can influence the perception and use of cues to various program structures. Experiment 3 examines how violations to standard conventions for constructing programs can affect the comprehension of expert, intermediate and novice subjects. Experiment 4 looks at the differences in strategy that are exhibited by subjects of varying skill level when constructing programs in different languages. Experiment 5 takes these ideas further to examine the temporal distribution of different forms of strategy during a program generation task. Experiment 6 provides evidence for salient cognitive structures derived from reaction time and error data in the context of a recognition task. Experiments 7 and 8 are concerned with the role of working memory in program generation and suggest that one aspect of expertise in the programming domain involves the acquisition of strategies for utilising display-based information. The final chapter attempts to bring these experimental findings together in terms of a model of knowledge organisation that stresses the importance of knowledge restructuring processes in the development of expertise. This is contrasted with existing models which have tended to place emphasis upon schemata acquisition and generalisation as the fundamental modes of learning associated with skill development. The work reported here suggests that a fine-grained restructuring of individual schemata takes places during the later stages of skill development. It is argued that those mechanisms currently thought to be associated with the development of expertise may not fully account for the strategic changes and the types of error typically found in the transition between novice, intermediate and expert problem solvers. This work has a number of implications for existing theories of skill acquisition. In particular, it questions the ability of such theories to account for subtle changes in the various manifestations of skilled performance that are associated with increasing expertise. Secondly, the work reported in this thesis attempts to show how specific forms of training might give rise to the knowledge restructuring process that is proposed. Finally, the thesis stresses the important role of display-based problem solving in complex tasks such as programming and highlights the role of programming language notation as a mediating factor in the development and acquisition of problem solving strategies

Standards for Scalable Clinical Decision Support: Need, Current and Emerging Standards, Gaps, and Proposal for Progress

Despite their potential to significantly improve health care, advanced clinical decision support (CDS) capabilities are not widely available in the clinical setting. An important reason for this limited availability of CDS capabilities is the application-specific and institution-specific nature of most current CDS implementations. Thus, a critical need for enabling CDS capabilities on a much larger scale is the development and adoption of standards that enable current and emerging CDS resources to be more effectively leveraged across multiple applications and care settings. Standards required for such effective scaling of CDS include (i) standard terminologies and information models to represent and communicate about health care data; (ii) standard approaches to representing clinical knowledge in both human-readable and machine-executable formats; and (iii) standard approaches for leveraging these knowledge resources to provide CDS capabilities across various applications and care settings. A number of standards do exist or are under development to meet these needs. However, many gaps and challenges remain, including the excessive complexity of many standards; the limited availability of easily accessible knowledge resources implemented using standard approaches; and the lack of tooling and other practical resources to enable the efficient adoption of existing standards. Thus, the future development and widespread adoption of current CDS standards will depend critically on the availability of tooling, knowledge bases, and other resources that make the adoption of CDS standards not only the right approach to take, but the cost-effective path to follow given the alternative of using a traditional, ad hoc approach to implementing CDS

Genre, schema, and the academic writing process : an enquiry into the generalisability of generic structure and its relationship to schematic knowledge.

SIGLEAvailable from British Library Document Supply Centre-DSC:DXN029010 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Deconstructing Thinking Like a Lawyer: Analyzing the Cognitive Components of the Analytical Mind

This article ... tackles the task of identifying the cognitive components of legal thinking. The article begins this task by discussing the development of modern law school pedagogy, which gave rise to the emphasis on thinking like a lawyer. The article then considers current conceptions of legal thinking which have divided the skill into cognitive and practical components, and it examines why the cognitive components remain at the center of the skill. The article then surveys empirical research on legal thinking by examining recent research on personality and learning styles as well as research on law student and lawyer surveys. The article next analyzes the cognitive skills tested by the paradigmatic examinations relevant to law school and lawyering, the Law School Admissions Test ( LSAT ) and the bar examination. The article then draws upon these previous sections to delineate and discuss specific components of legal thinking. In this discussion, the article references numerous resources that have analyzed the components of legal thinking, and it examines the results of a survey on legal thinking given to 250 law students at Regent University School of Law

The Behaviour of Evolutionary Algorithms for the CFD-Driven Design Optimisation of Aerofoils

This thesis utilises aerodynamic shape optimisation software AerOpt and FLITE2D, to explore the behaviour of three Evolutionary Algorithms, Differential Evolution (DE), Modified Cuckoo Search (MCS), and Particle Swarm Optimisation (PSO), to optimise a 2D nonsymmetric aerofoil, providing an evaluation of their aerodynamic optimising capabilities.The aerofoil used in test cases is the NACA21120, where a variation of control node approaches are utilised to alter the aerofoil’s geometry. In the first set of test cases, a control node is placed on the upper surface to allow the thickness to be altered, and in the second set of cases, six control nodes are arranged along the boundary of the aerofoil, to examine the overall shape change.A mesh convergence study helped to determine the best mesh settings for the given problem. Each algorithm is tested in a subsonic, transonic, and supersonic flow regime to ensure the test cases fulfil the CFD aspect of the research. All flow regimes were treated as viscous with the relevant Reynolds number applied. To provide an analysis on how tuning the input parameters affects the algorithm’s behaviour, the number of agents were inputted were varied from 10 to 50 to 99. The generations number was set to 99, and the fitness objective was to optimise for the lift-drag ratio (L/D), throughout all optimisations.The first set of results (one control node) found that fitness improvements were largest in the transonic cases, increasing the L/D by an average percentage of 213%. The aerofoil’s L/D at Mach 0.5 was improved by an average of 80%, and Mach 1.5 by 33%. Each algorithm showed a similar trend in which the control node was positioned at the final generation in the design space, this varied depending on the Mach number being optimised for, either resulting in an increase or decrease in the aerofoil thickness. Varying the number of agents inputted, had a more significant effect on MCS, whereas DE and PSO showed more consistent results regardless of the number of inputted agents. Generally, PSO displayed fastest convergence of all the agents, shortly followed by DE, followed by MCS.The second set of results (six control nodes) were optimised for identical input parameters but for simplicity, at a single flow regime, Mach 1.5. Differing from the first set of results showing similar control node placement within the design space, the second set of results showed the algorithm’s position some of the control nodes in different locations within the design space. Despite the similar fitness improvement values seen between DE and PSO, the final geometries were observed to be somewhat varied, where DE reduced the thickness of the trailing edge, but PSO increased it. MCS displayed similar geometry change to PSO but with more conservative control node movement

Evolutionary computation for trading systems

2007/2008Evolutionary computations, also called evolutionary algorithms, consist of several heuristics, which are able to solve optimization tasks by imitating some aspects of natural evolution. They may use different levels of abstraction, but they are always working on populations of possible solutions for a given task. The basic idea is that if only those individuals of a population which meet a certain selection criteria reproduce, while the remaining individuals die, the population will converge to those individuals that best meet the selection criteria. If imperfect reproduction is added the population can begin to explore the search space and will move to individuals that have an increased selection probability and that hand down this property to their descendants. These population dynamics follow the basic rule of the Darwinian evolution theory, which can be described in short as the “survival of the fittest”. Although evolutionary computations belong to a relative new research area, from a computational perspective they have already showed some promising features such as: • evolutionary methods reveal a remarkable balance between efficiency and efficacy; • evolutionary computations are well suited for parameter optimisation; • this type of algorithms allows a wide variety of extensions and constraints that cannot be provided in traditional methods; • evolutionary methods are easily combined with other optimization techniques and can also be extended to multi-objective optimization. From an economic perspective, these methods appear to be particularly well suited for a wide range of possible financial applications, in particular in this thesis I study evolutionary algorithms • for time series prediction; • to generate trading rules; • for portfolio selection. It is commonly believed that asset prices are not random, but are permeated by complex interrelations that often translate in assets mispricing and may give rise to potentially profitable opportunities. Classical financial approaches, such as dividend discount models or even capital asset pricing theories, are not able to capture these market complexities. Thus, in the last decades, researchers have employed intensive econometric and statistical modeling that examine the effects of a multitude of variables, such as price- earnings ratios, dividend yields, interest rate spreads and changes in foreign exchange rates, on a broad and variegated range of stocks at the same time. However, these models often result in complex functional forms difficult to manage or interpret and, in the worst case, are solely able to fit a given time series but are useless to predict it. Parallelly to quantitative approaches, other researchers have focused on the impact of investor psychology (in particular, herding and overreaction) and on the consequences of considering informed signals from management and analysts, such as share repurchases and analyst recommendations. These theories are guided by intuition and experience, and thus are difficult to be translated into a mathematical environment. Hence, the necessity to combine together these point of views in order to develop models that examine simultaneously hundreds of variables, including qualitative informations, and that have user friendly representations, is urged. To this end, the thesis focuses on the study of methodologies that satisfy these requirements by integrating economic insights, derived from academic and professional knowledge, and evolutionary computations. The main task of this work is to provide efficient algorithms based on the evolutionary paradigm of biological systems in order to compute optimal trading strategies for various profit objectives under economic and statistical constraints. The motivations for constructing such optimal strategies are: i) the necessity to overcome data-snooping and supervisorship bias in order to learn to predict good trading opportunities by using market and/or technical indicators as features on which to base the forecasting; ii) the feasibility of using these rules as benchmark for real trading systems; iii) the capability of ranking quantitatively various markets with respect to their profitability according to a given criterion, thus making possible portfolio allocations. More precisely, I present two algorithms that use artificial expert trading systems to predict financial time series, and a procedure to generate integrated neutral strategies for active portfolio management. The first algorithm is an automated procedure that simultaneously selects variables and detect outliers in a dynamic linear model using information criteria as objective functions and diagnostic tests as constraints for the distributional properties of errors. The novelties are the automatic implementation of econometric conditions in the model selection step, making possible a better exploration of the solution space on one hand, and the use of evolutionary computations to efficiently generate a reduction procedure from a very large number of independent variables on the other hand. In the second algorithm, the novelty is given by the definition of evolutionary learning in financial terms and its use in a multi-objective genetic algorithm in order to generate technical trading systems. The last tool is based on a trading strategy on six assets, where future movements of each variable are obtained by an evolutionary procedure that integrates various types of financial variables. The contribution is given by the introduction of a genetic algorithm to optimize trading signals parameters and the way in which different informations are represented and collected. In order to compare the contribution of this work to “classical” techniques and theories, the thesis is divided into three parts. The first part, titled Background, collects Chapters 2 and 3. Its purpose is to provide an introduction to search/optimization evolutionary techniques on one hand, and to the theories that relate the predictability in financial markets with the concept of efficiency proposed over time by scholars on the other hand. More precisely, Chapter 2 introduces the basic concepts and major areas of evolutionary computation. It presents a brief history of three major types of evolutionary algorithms, i.e. evolution strategies, evolutionary programming and genetic algorithms, and points out similarities and differences among them. Moreover it gives an overview of genetic algorithms and describes classical and genetic multi-objective optimization techniques. Chapter 3 first presents an overview of the literature on the predictability of financial time series. In particular, the extent to which the efficiency paradigm is affected by the introduction of new theories, such as behavioral finance, is described in order to justify the market forecasting methodologies developed by practitioners and academics in the last decades. Then, a description of the econometric and financial techniques that will be used in conjunction with evolutionary algorithms in the successive chapters is provided. Special attention is paid to economic implications, in order to highlight merits and shortcomings from a practitioner perspective. The second part of the thesis, titled Trading Systems, is devoted to the description of two procedures I have developed in order to generate artificial trading strategies on the basis of evolutionary algorithms, and it groups Chapters 4 and 5. In particular, chapter 4 presents a genetic algorithm for variable selection by minimizing the error in a multiple regression model. Measures of errors such as ME, RMSE, MAE, Theil’s inequality coefficient and CDC are analyzed choosing models based on AIC, BIC, ICOMP and similar criteria. Two components of penalty functions are taken in analysis- level of significance and Durbin Watson statistics. Asymptotic properties of functions are tested on several financial variables including stocks, bonds, returns, composite prices indices from the US and the EU economies. Variables with outliers that distort the efficiency and consistency of estimators are removed to solve masking and smearing problems that they may cause in estimations. Two examples complete the chapter. In both cases, models are designed to produce short-term forecasts for the excess returns of the MSCI Europe Energy sector on the MSCI Europe index and a recursive estimation- window is used to shed light on their predictability performances. In the first application the data-set is obtained by a reduction procedure from a very large number of leading macro indicators and financial variables stacked at various lags, while in the second the complete set of 1-month lagged variables is considered. Results show a promising capability to predict excess sector returns through the selection, using the proposed methodology, of most valuable predictors. In Chapter 5 the paradigm of evolutionary learning is defined and applied in the context of technical trading rules for stock timing. A new genetic algorithm is developed by integrating statistical learning methods and bootstrap to a multi-objective non dominated sorting algorithm with variable string length, making possible to evaluate statistical and economic criteria at the same time. Subsequently, the chapter discusses a practical case, represented by a simple trading strategy where total funds are invested in either the S&P 500 Composite Index or in 3-month Treasury Bills. In this application, the most informative technical indicators are selected from a set of almost 5000 signals by the algorithm. Successively, these signals are combined into a unique trading signal by a learning method. I test the expert weighting solution obtained by the plurality voting committee, the Bayesian model averaging and Boosting procedures with data from the the S&P 500 Composite Index, in three market phases, up-trend, down- trend and sideways-movements, covering the period 2000–2006. In the third part, titled Portfolio Selection, I explain how portfolio optimization models may be constructed on the basis of evolutionary algorithms and on the signals produced by artificial trading systems. First, market neutral strategies from an economic point of view are introduced, highlighting their risks and benefits and focusing on their quantitative formulation. Then, a description of the GA-Integrated Neutral tool, a MATLAB set of functions based on genetic algorithms for active portfolio management, is given. The algorithm specializes in the parameter optimization of trading signals for an integrated market neutral strategy. The chapter concludes showing an application of the tool as a support to decisions in the Absolute Return Interest Rate Strategies sub-fund of Generali Investments.Gli “algoritmi evolutivi”, noti anche come “evolutionary computations” comprendono varie tecniche di ottimizzazione per la risoluzione di problemi, mediante alcuni aspetti suggeriti dall’evoluzione naturale. Tali metodologie sono accomunate dal fatto che non considerano un’unica soluzione alla volta, bens`ı trattano intere popolazioni di possibili soluzioni per un dato problema. L’idea sottostante `e che, se un algoritmo fa evolvere solamente gli individui di una data popolazione che soddisfano a un certo criterio di selezione, e lascia morire i restanti, la popolazione converger`a agli individui che meglio soddisfano il criterio di selezione. Con una selezione non ottimale, cio`e una che ammette pure soluzioni sub-ottimali, la popolazione rappresenter` a meglio l’intero spazio di ricerca e sar`a in grado di individuare in modo pi`u consistente gli individui migliori da far evolvere. Queste dinamiche interne alle popolazioni seguono i principi Darwiniani dell’evoluzione, che si possono sinteticamente riassumere nella dicitura “la sopravvivenza del più adatto”. Sebbene gli algoritmi evolutivi siano un’area di ricerca relativamente nuova, dal punto di vista computazionale hanno dimostrato alcune caratteristiche interessanti fra cui le seguenti: • permettono un notevole equilibrio tra efficienza ed efficacia; • sono particolarmente indicati per la configurazione dei parametri in problemi di ottimizzazione; • consentono una flessibilit`a nella definizione matematica dei problemi e dei vincoli che non si trova nei metodi tradizionali; • possono facilmente essere integrati con altre tecniche di ottimizzazione ed essere essere modificati per risolvere problemi multi-obiettivo. Dal un punto di vista economico, l’applicazione di queste procedure pu`o risultare utile specialmente in campo finanziario. In particolare, nella mia tesi ho studiato degli algoritmi evolutivi per • la previsione di serie storiche finanziarie; • la costruzione di regole di trading; • la selezione di portafogli. Da un punto di vista pi`u ampio, lo scopo di questa ricerca `e dunque l’analisi dell’evoluzione e della complessit`a dei mercati finanziari. In tal senso, dal momento che i prezzi non seguono andamenti puramente casuali, ma sono governati da un insieme molto articolato di eventi correlati, i modelli e le teorie classiche, come i dividend discount model e le varie capital asset pricing theories, non sono pi`u sufficienti per determinare potenziali opportunit`a di profitto. A tal fine, negli ultimi decenni, alcuni ricercatori hanno sviluppato una vasta gamma di modelli econometrici e statistici in grado di esaminare contemporaneamente le relazioni e gli effetti di centinaia di variabili, come ad esempio, price-earnings ratios, dividendi, differenziali fra tassi di interesse e variazioni dei tassi di cambio, per una vasta gamma di assets. Comunque, questo approccio, che fa largo impiego di strumenti di calcolo, spesso porta a dei modelli troppo complicati per essere gestiti o interpretati, e, nel peggiore dei casi, pur essendo ottimi per descrivere situazioni passate, risultano inutili per fare previsioni. Parallelamente a questi approcci quantitativi, si `e manifestato un grande interesse sulla psicologia degli investitori e sulle conseguenze derivanti dalle opinioni di esperti e analisti nelle dinamiche del mercato. Questi studi sono difficilmente traducibili in modelli matematici e si basano principalmente sull’intuizione e sull’esperienza. Da qui la necessit` a di combinare insieme questi due punti di vista, al fine di sviluppare modelli che siano in grado da una parte di trattare contemporaneamente un elevato numero di variabili in modo efficiente e, dall’altra, di incorporare informazioni e opinioni qualitative. La tesi affronta queste tematiche integrando le conoscenze economiche, sia accademiche che professionali, con gli algoritmi evolutivi. Pi`u pecisamente, il principale obiettivo di questo lavoro `e lo sviluppo di algoritmi efficienti basati sul paradigma dell’evoluzione dei sistemi biologici al fine di determinare strategie di trading ottimali in termini di profitto e di vincoli economici e statistici. Le ragioni che motivano lo studio di tali strategie ottimali sono: i) la necessit`a di risolvere i problemi di data-snooping e supervivorship bias al fine di ottenere regole di investimento vantaggiose utilizzando indicatori di mercato e/o tecnici per la previsione; ii) la possibilità di impiegare queste regole come benchmark per sistemi di trading reali; iii) la capacit`a di individuare gli asset pi`u vantaggiosi in termini di profitto, o di altri criteri, rendendo possibile una migliore allocazione di risorse nei portafogli. In particolare, nella tesi descrivo due algoritmi che impiegano sistemi di trading artificiali per predire serie storiche finanziarie e una procedura di calcolo per strategie integrate neutral market per la gestione attiva di portafogli. Il primo algoritmo `e una procedura automatica che seleziona le variabili e simultaneamente determina gli outlier in un modello dinamico lineare utilizzando criteri informazionali come funzioni obiettivo e test diagnostici come vincoli per le caratteristiche delle distribuzioni degli errori. Le novit`a del metodo sono da una parte l’implementazione automatica di condizioni econometriche nella fase di selezione, consentendo una migliore analisi dello EVOLUTIONARY COMPUTATIONS FOR TRADING SYSTEMS 3 spazio delle soluzioni, e dall’altra parte, l’introduzione di una procedura di riduzione evolutiva capace di riconoscere in modo efficiente le variabili pi`u informative. Nel secondo algoritmo, le novità sono costituite dalla definizione dell’apprendimento evolutivo in termini finanziari e dall’applicazione di un algoritmo genetico multi-obiettivo per la costruzione di sistemi di trading basati su indicatori tecnici. L’ultimo metodo proposto si basa su una strategia di trading su sei assets, in cui le dinamiche future di ciascuna variabile sono ottenute impiegando una procedura evolutiva che integra diverse tipologie di variabili finanziarie. Il contributo è dato dall’impiego di un algoritmo genetico per ottimizzare i parametri negli indicatori tecnici e dal modo in cui le differenti informazioni sono presentate e collegate. La tesi `e organizzata in tre parti. La prima parte, intitolata Background, comprende i Capitoli 2 e 3, ed è intesa a fornire un’introduzione alle tecniche di ricerca/ottimizzazione su base evolutiva da una parte, e alle teorie che si occupano di efficienza e prevedibilit`a dei mercati finanziari dall’altra. Più precisamente, il Capitolo 2 introduce i concetti base e i maggiori campi di studio della computazione evolutiva. In tal senso, si dà una breve presentazione storica di tre dei maggiori tipi di algoritmi evolutivi, ciò e le strategie evolutive, la programmazione evolutiva e gli algoritmi genetici, evidenziandone caratteri comuni e differenze. Il capitolo si chiude con una panoramica sugli algoritmi genetici e sulle tecniche classiche e genetiche di ottimizzazione multi-obiettivo. Il Capitolo 3 affronta nel dettaglio la problematica della prevedibilit`a delle serie storiche finanziarie mettendo in luce, in particolare, quanto il paradigma dell’efficienza sia influenzato dalle pi`u recenti teorie finanziarie, come ad esempio la finanza comportamentale. Lo scopo è quello di dare una giustificazione su basi teoriche per le metodologie di previsione sviluppate nella tesi. Segue una descrizione dei metodi econometrici e di analisi tecnica che nei capitoli successivi verrano impiegati assieme agli algoritmi evolutivi. Una particolare attenzione è data alle implicazioni economiche, al fine di evidenziare i loro meriti e i loro difetti da un punto di vista pratico. La seconda parte, intitolata Trading Systems, raggruppa i Capitoli 4 e 5 ed è dedicata alla descrizione di due procedure che ho sviluppato per generare sistemi di trading artificiali sulla base di algoritmi evolutivi. In particolare, il Capitolo 4 presenta un algortimo genetico per la selezione di variabili attraverso la minimizzazione dell’errore in un modello di regressione multipla. Misure di errore, quali il ME, il RMSE, il MAE, il coefficiente di Theil e il CDC sono analizzate a partire da modelli selezionati sulla scorta di criteri informazionali, come ad esempio AIC, BIC, ICOMP. A livello di vincoli diagnostici, ho considerato una funzione di penalità a due componenti e la statistica di Durbin Watson. Il programma impiega variabili finanziarie di vario tipo, come rendimenti di titoli, bond e prezzi di indici composti ottenuti dalle economie Statunitense ed Europea. Nel caso le serie storiche 4 MASSIMILIANO KAUCIC considerate presentino outliers che distorcono l’efficienza e la consistenza degli stimatori, l’algoritmo `e in grado di individuarle e rimuoverle dalla serie, risolvendo il problema di masking and smearing. Il capitolo si conclude con due applicazioni, in cui i modelli sono progettati per produrre previsioni di breve periodo per l’extra rendimento del settore MSCI Europe Energy sull’indice MSCI Europe e una procedura di tipo recursive estimation-window è utilizzata per evidenziarne le performance previsionali. Nel primo esempio, l’insieme dei dati `e ottenuto estraendo le variabili di interesse da un considerevole numero di indicatori di tipo macro e da variabili finanziarie ritardate rispetto alla variabile dipendente. Nel secondo esempio ho invece considerato l’intero insieme di variabili ritardate di 1 mese. I risultati mostrano una notevole capacità previsiva per l’extra rendimento, individuando gli indicatori maggiormente informativi. Nel Capitolo 5, il concetto di apprendimento evolutivo viene definito ed applicato alla costruzione di regole di trading su indicatori tecnici per lo stock timing. In tal senso, ho sviluppato un algoritmo che integra metodi di apprendimento statistico e di boostrap con un particolare algoritmo multi-obiettivo. La procedura derivante è in grado di valutare contemporaneamente criteri economici e statistici. Per descrivere il suo funzionamento, ho considerato un semplice esempio di trading in cui tutto il capitale è investito in un indice (che nel caso trattato è l’indice S&P 500 Composite) o in un titolo a basso rischio (nell’esempio, i Treasury Bills a 3 mesi). Il segnale finale di trading `e il risultato della selezione degli indicatori tecnici pi`u informativi a partire da un insieme di circa 5000 indicatori e la loro conseguente integrazione mediante un metodo di apprendimento (il plurality voting committee, il bayesian model averaging o il Boosting). L’analisi è stata condotta sull’intervallo temporale dal 2000 al 2006, suddiviso in tre sottoperiodi: il primo rappresenta l’indice in un

Development of a Hypermedia Database for the Elementary Classroom

A hypermedia database including selected flora and fauna in Kittitas County, Washington, was developed using HyperCard software for researching information about 400+ species. students had the opportunity to use the database in school libraries and in the author\u27s fifth-grade classroom. The database cards accessed videodisc images where available. On the basis of limited study to date, it appears that when studying the environment, student learning was enhanced by the use of database material created by the author