221 research outputs found
Cloud technology options towards Free Flow of Data
This whitepaper collects the technology solutions that the projects in the Data Protection, Security and Privacy Cluster propose to address the challenges raised by the working areas of the Free Flow of Data initiative. The document describes the technologies, methodologies, models, and tools researched and developed by the clustered projects mapped to the ten areas of work of the Free Flow of Data initiative. The aim is to facilitate the identification of the state-of-the-art of technology options towards solving the data security and privacy challenges posed by the Free Flow of Data initiative in Europe. The document gives reference to the Cluster, the individual projects and the technologies produced by them
Analysis and verification of ECA rules in intelligent environments
Intelligent Environments (IEs) are physical spaces where Information Technology (IT) and other pervasive computing technologies are combined in order to achieve specific goals for the users and the environment. IEs have the goal of enriching user experience, increasing awareness of the environment. A number of applications are currently being deployed in domains ranging from smart homes to e-health and autonomous vehicles. Quite often IE support human activities, thus essential requirements to be ensured are correctness, reliability, safety and security. In this paper we present how a set of techniques and tools that have been developed for the verification of software can be employed in the verification of IE described by means of event-condition-action rules. More precisely, we reduce the problem of verifying key properties of these rules to satisfiability and termination problems that can be addressed using state-of-the-art Satisfiability Modulo Theory (SMT) solvers and program analysers. Our approach has been implemented in a tool called vIRONy. Our approach has been validated on a number of case studies from the literature
An Exploration of Traditional and Data Driven Predictors of Programming Performance
This thesis investigates factors that can be used to predict the success or failure of students taking an introductory programming course. Four studies were performed to explore how aspects of the teaching context, static factors based upon traditional learning theories, and data-driven metrics derived from aspects of programming behaviour were related to programming performance.
In the first study, a systematic review into the worldwide outcomes of programming courses revealed an average pass rate of 67.7\%. This was found to have not significantly changed over time, or to have differed based upon aspects of the teaching context, such as the programming language taught to students.
The second study showed that many of the factors based upon traditional learning theories, such as learning styles, are context dependent, and fail to consistently predict programming performance when they are applied across different teaching contexts.
The third study explored data-driven metrics derived from the programming behaviour of students. Analysing data logged from students using the BlueJ IDE, 10 new data-driven metrics were identified and validated on three independently gathered datasets. Weaker students were found to make a greater percentage of successive errors, and spend a greater percentage of their lab time resolving errors than stronger students. The Robust Relative algorithm was developed to hybridize four of the strongest data-driven metrics into a performance predictor. The novel relative scoring of students based upon how their resolve times for different types of errors compared to the resolve times of their peers, resulted in a predictor which could explain a large proportion of the variance in the performance of three independent cohorts, = 42.19\%, 43.65\% and 44.17\% - almost double the variance which could be explained by Jadud's Error Quotient metric.
The fourth study situated the findings of this thesis within the wider literature, by applying meta-analysis techniques to statistically synthesise fifty years of conflicting research, such that the most important factors for learning programming could be identified. 482 results describing the effects of 116 factors on programming performance were synthesised and consolidated to form a six class theoretical framework. The results showed that the strongest predictors identified over the past fifty years are data-driven metrics based upon programming behaviour. Several of the traditional predictors were also found to be influential, suggesting that both a certain level of scientific maturity and self-concept are necessary for programming. Two thirds of the weakest predictors were based upon demographic and psychological factors, suggesting that age, gender, self-perceived abilities, learning styles, and personality traits have no relevance for programming performance.
This thesis argues that factors based upon traditional learning theories struggle to consistently predict programming performance across different teaching contexts because they were not intended to be applied for this purpose. In contrast, the main advantage of using data-driven approaches to derive metrics based upon students' programming processes, is that these metrics are directly based upon the programming behaviours of students, and therefore can encapsulate such changes in their programming knowledge over time. Researchers should continue to explore data-driven predictors in the future
ITiCSE'10 - Proceedings of the 2010 ACM SIGCSE Annual Conference on Innovation and Technology in Computer Science Education: Foreword
[No abstract available
Análisis de las propuestas de la enseñanza de la programación orientada a objetos en los primeros cursos
Existe un fuerte debate sobre qué paradigma conviene impartir en el primer curso de los planes de estudio de Informática: orientación a objetos (OO) o procedimental, así como sobre el lenguaje de soporte de las sesiones prácticas. Muchos artículos presentan sus experiencias o proponen nuevas metodologías de forma aislada, pero no encontramos un estudio sistemático de los distintos enfoques. En este trabajo se presenta una visión crítica de las propuestas de diferentes autores, se evalúan sus ventajas e inconvenientes. Se concluye la necesidad de reconducir el debate de la planificación de los primeros cursos de programación, hoy día centrado en el orden en que se enseñan los paradigmas y el lenguaje para implementarlo, hacia la búsqueda de los conceptos fundamentales y herramientas pedagógicas que permitan una exposición gradual de los conceptos de la programación.Este trabajo se ha financiado con el proyecto TIC2000-1413 de la CICYT
To Heck With Ethics: Thinking About Public Issues With a Framework for CS Students
This paper proposes that the ethics class in the CS curriculum incorporate the Lawrence Lessig model of regulation as an analytical tool for social issues. Lessig’s use of the notion of architecture, the rules and boundaries of the sometimes artificial world within which social issues play out, is particularly resonant with computing professionals. The CS curriculum guidelines include only ethical frameworks as the tool for our students to engage with societal issues. The regulation framework shows how the market, law, social norms, and architecture can all be applied toward understanding social issues
To Heck With Ethics: Thinking About Public Issues With a Framework for CS Students
This paper proposes that the ethics class in the CS curriculum incorporate the Lawrence Lessig model of regulation as an analytical tool for social issues. Lessig’s use of the notion of architecture, the rules and boundaries of the sometimes artificial world within which social issues play out, is particularly resonant with computing professionals. The CS curriculum guidelines include only ethical frameworks as the tool for our students to engage with societal issues. The regulation framework shows how the market, law, social norms, and architecture can all be applied toward understanding social issues
Analysis and verification of ECA rules in intelligent environments
Intelligent Environments (IEs) are physical spaces where Information Technology (IT) and other pervasive computing technologies are combined in order to achieve specific goals for the users and the environment. IEs have the goal of enriching user experience, increasing awareness of the environment. A number of applications are currently being deployed in domains ranging from smart homes to e-health and autonomous vehicles. Quite often IE support human activities, thus essential requirements to be ensured are correctness, reliability, safety and security. In this paper we present how a set of techniques and tools that have been developed for the verification of software can be employed in the verification of IE described by means of event-condition-action rules. More precisely, we reduce the problem of verifying key properties of these rules to satisfiability and termination problems that can be addressed using state-of-the-art Satisfiability Modulo Theory (SMT) solvers and program analysers. Our approach has been implemented in a tool called vIRONy. Our approach has been validated on a number of case studies from the literature
Teaching and learning introductory programming : a model-based approach
The dissertation identifies and discusses impact of a model-based approach to teaching and learning introductory object-oriented programming both for practitioners and for computer science education research.Learning to program is notoriously difficult. This dissertation investigates ways to teach introductory object-oriented programming at the university level. It focuses on a model-based approach, describes and argues for this approach and investigates several of its aspects. It gives an overview of the research in teaching introductory programming in an objects-first way. The dissertation also investigates ways for university teachers to share and document best practices in teaching introductory object-oriented programming through pedagogical patterns. The dissertation addresses both traditional young full-time students and experienced programmers (although not in object-orientation) participating in part-time education. It examines whether the same success factors for learning programming apply to a model-based approach as to introductory programming courses in general for full-time students and gives a general overview of research in success factors for introductory programming. Some factors are the same, because students‘ math competence is positively correlated with their success. The dissertation examines how experienced programmers link a model-based programming course to their professional practices. The general answer is that the part-time students do not need to have a direct link to their specific work-practice, they expect to create the link themselves; but the teacher must be aware of the conditions facing the part-time students in industry. Furthermore, the dissertation addresses interaction patterns for part-time students learning model-based introductory programming in a net-based environment. A previously prepared solution to an exercise is found to mediate the interaction in three different ways. Design patterns have had a major impact on the quality of object-oriented software. Inspired by this, researchers have suggested pedagogical patterns for sharing best practices in teaching introductory object-oriented programming. It was expected that university teachers‘ knowledge of pedagogical patterns was limited, but this research proved that to be wrong; about half of the teachers know pedagogical patterns. One of the problems this dissertation identifies is the lack of a structuring principle for pedagogical patterns; potential users have problems identifying the correct patterns to apply. An alternative structuring principle based on a constructivist learning theory is suggested and analysed
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