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  • 1
    Language: English
    In: ACM Transactions on Computing Education (TOCE), 01 June 2013, Vol.13(2), pp.1-21
    Description: Lecturing is known to be a controversial form of teaching. With massed classrooms, in particular, it tends to constrain the active participation of students. One of the remedies applied to programming education is to use technology that can vitalize interaction in the classroom, while another is to base teaching increasingly on programming activities. In this article, we present the first results of an exploratory study, in which we teach programming without lectures, exams, or grades, by heavily emphasizing programming activity, and, in a pedagogical sense, student self-direction. This article investigates how students reacted to the active role required of them and what issues emerged in this setting where self-direction was required. The results indicate three issues that should be taken into account when designing a student-driven course: the challenge of supporting students' theoretical synthesis of the topics to be learned, the individual's opportunities for self-direction in a group work setting, and mismatch between individual learning processes and academic course scheduling.
    Keywords: Self-Direction ; Programming Education ; Computer Science
    E-ISSN: 1946-6226
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  • 2
    Language: English
    In: Artificial Intelligence Review, 2013, Vol.39(3), pp.195-224
    Description: This paper studies the use of multiple scale factor values within distributed Differential Evolution structures employing the so-called exponential crossover. Four different scale factor schemes are proposed, tested, compared and analyzed. Two schemes simply employ multiple scale factor values and two also include an update logic during the evolution. The four schemes have been integrated for comparison within three recently proposed distributed Differential Evolution structures and tested on several various test problems. The results are then compared to those of a previous study where the so-called binomial crossover was employed. Numerical results show that, when associated to the exponential crossover, the employment of multiple scale factors is not systematically beneficial and in some cases even detrimental to the performance of the algorithm. The exponential crossover accentuates the exploitative character of the Differential Evolution, which cannot always be counterbalanced by the increase in the explorative aspect of the algorithm introduced by the employment of multiple scale factor values.
    Keywords: Differential evolution ; Evolutionary algorithms ; Distributed algorithms ; Scale factor
    ISSN: 0269-2821
    E-ISSN: 1573-7462
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  • 3
    Language: English
    In: Int. J. of Innovative Computing and Applications, 2011, Vol 3 Issue 1, pp 20 - 30
    Description: This paper proposes the use of two algorithms based on the parallel differential evolution. The first algorithm proposes the use of endemic control parameters within a parallel differential evolution algorithm; the differential evolution running at each subpopulation is associated with randomly initialised scale factor and crossover rate, which are then repeatedly updated during the optimisation process. The second algorithm proposes decomposing the search space of large-scale problems into lower-dimensionality subspaces, and associating each of these to one subpopulation of a parallel differential evolution algorithm. Each subpopulation is running a modified differential evolution algorithm, where the crossover function is limited to components of the subpopulation's associated subspace. According to numerical results, both algorithms seem to be clear improvements over the original parallel distributed evolution; they are simple, robust, and efficient algorithms suited for various applications.
    Keywords: evolutionary algorithms; structured population; parallel differential evolution; PDE; component decomposition; endemic control parameters.
    ISSN: 1751-648X
    ISSN: 1751648X
    ISSN: 1751-6498
    ISSN: 17516498
    E-ISSN: 1751-6498
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  • 4
    Language: English
    In: Artificial Intelligence Review, 2010, Vol.33(1), pp.61-106
    Description: Differential Evolution (DE) is a simple and efficient optimizer, especially for continuous optimization. For these reasons DE has often been employed for solving various engineering problems. On the other hand, the DE structure has some limitations in the search logic, since it contains too narrow a set of exploration moves. This fact has inspired many computer scientists to improve upon DE by proposing modifications to the original algorithm. This paper presents a survey on DE and its recent advances. A classification, into two macro-groups, of the DE modifications is proposed here: (1) algorithms which integrate additional components within the DE structure, (2) algorithms which employ a modified DE structure. For each macro-group, four algorithms representative of the state-of-the-art in DE, have been selected for an in depth description of their working principles. In order to compare their performance, these eight algorithm have been tested on a set of benchmark problems. Experiments have been repeated for a (relatively) low dimensional case and a (relatively) high dimensional case. The working principles, differences and similarities of these recently proposed DE-based algorithms have also been highlighted throughout the paper. Although within both macro-groups, it is unclear whether there is a superiority of one algorithm with respect to the others, some conclusions can be drawn. At first, in order to improve upon the DE performance a modification which includes some additional and alternative search moves integrating those contained in a standard DE is necessary. These extra moves should assist the DE framework in detecting new promising search directions to be used by DE. Thus, a limited employment of these alternative moves appears to be the best option in successfully assisting DE. The successful extra moves are obtained in two ways: an increase in the exploitative pressure and the introduction of some randomization. This randomization should not be excessive though, since it would jeopardize the search. A proper increase in the randomization is crucial for obtaining significant improvements in the DE functioning. Numerical results show that, among the algorithms considered in this study, the most efficient additional components in a DE framework appear to be the population size reduction and the scale factor local search. Regarding the modified DE structures, the global and local neighborhood search and self-adaptive control parameter scheme, recently proposed in literature, seem to be the most promising modifications.
    Keywords: Differential Evolution ; Survey ; Comparative Analysis ; Self-Adaptation ; Continuous Optimization
    ISSN: 0269-2821
    E-ISSN: 1573-7462
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  • 5
    Language: English
    In: ACM Transactions on Computing Education (TOCE), 29 September 2016, Vol.17(1), pp.1-35
    Description: This article reports on an action research project on improving a functional programming course by moving toward a practical and flexible study environment-flipped and blended classroom. Teaching the topic of functional programming was found to be troublesome using a traditional lectured course format. The need to increase students' amount of practice emerged while subsequent challenges relating to students' independent practical coursework were observed. Particular concerns relating to group work, learning materials, and the attribute of flexibility were investigated during the third action research cycle. The research cycle was analyzed using a qualitative survey on students' views, teacher narrative, and students' study activity data. By this third research cycle, we found that (i) the "call for explanation" is an apt conceptualization for supporting independent work, and in particular for the design of learning materials; (ii) use of student-selected groups that can be flexibly resized or even disbanded enables spontaneous peer support and can avoid frustration about group work; and (iii) students greatly appreciate the high degree of flexibility in the course arrangements but find that it causes them to slip from their goals. The project has improved our understanding of a successful implementation of the target course based on group work and learning materials in the context of independent study, while the attribute of flexibility revealed a contradiction that indicates the need for further action.
    Keywords: Action Research ; Blended Learning ; Flipped Classroom ; Functional Programming ; Independent Study ; Computer Science
    E-ISSN: 1946-6226
    Source: BIBSAM:2015-2018 (ACM Digital Library)
    Source: Jisc Collections:Digital Library:2017-2019 (ACM Digital Library)
    Source: ACM Digital Library (Association for Computing Machinery)
    Source: KESLI (ACM Digital Library)
    Source: SHEDL- 2014 (ACM Digital Library)
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  • 6
    Language: English
    In: Journal of Functional Programming, 2015, Vol.25
    Description: Teaching functional programming as a second programming paradigm is often difficult as students can have strong preconceptions about programming. When most of these preconceived ideas fail to be confirmed, functional programming may be seen as an unnecessarily difficult topic. A typical topic that causes such difficulties is the language of types employed by many modern functional languages. In this paper, we focus on addressing this difficulty through the use of step-by-step calculations of type expressions. The outcome of the study is an elaboration of a worked example format and a methodical approach for teaching types to beginner functional programmers.
    ISSN: 0956-7968
    E-ISSN: 1469-7653
    Source: CrossRef
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  • 7
    Language: English
    In: Soft Computing, 2010, Vol.14(11), pp.1187-1207
    Description: This article proposes a distributed differential evolution which employs a novel self-adaptive scheme, namely scale factor inheritance. In the proposed algorithm, the population is distributed over several sub-populations allocated according to a ring topology. Each sub-population is characterized by its own scale factor value. With a probabilistic criterion, that individual displaying the best performance is migrated to the neighbor population and replaces a pseudo-randomly selected individual of the target sub-population. The target sub-population inherits not only this individual but also the scale factor if it seems promising at the current stage of evolution. In addition, a perturbation mechanism enhances the exploration feature of the algorithm. The proposed algorithm has been run on a set of various test problems and then compared to two sequential differential evolution algorithms and three distributed differential evolution algorithms recently proposed in literature and representing state-of-the-art in the field. Numerical results show that the proposed approach seems very efficient for most of the analyzed problems, and outperforms all other algorithms considered in this study.
    Keywords: Differential evolution ; Distributed evolutionary algorithms ; Evolutionary algorithms ; Continuous optimization
    ISSN: 1432-7643
    E-ISSN: 1433-7479
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  • 8
    Language: English
    In: Proceedings of the 2014 conference on innovation & technology in computer science education, 21 June 2014, pp.303-308
    Description: Functional programming is often presented as an advantageous programming paradigm by its advocates, but many students and teachers consider it to be hard to learn. One particular hurdle in learning functional programming is mastering the modern type systems employed in these languages. In this article, we identify student difficulties with means of multiple choice questions embedded into the on-line materials of an introductory functional programming course. The most prevalent misconceptions were confusing with parametric polymorphism with subtyping, the assigning too much meaning to variables names, and confounding general language patterns with special cases.
    Keywords: Experiment ; Functional Programming ; Misconceptions ; Type Systems ; Computer Science
    ISBN: 9781450328333
    ISBN: 1450328334
    Source: ACM Digital Library (Association for Computing Machinery)
    Source: KESLI (ACM Digital Library)
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  • 9
    Language: English
    In: Journal of Functional Programming, 2015, Vol.25
    Description: This article presents an overview of student difficulties in an introductory functional programming (FP) course taught in Haskell. The motivation for this study stems from our belief that many student difficulties can be alleviated by understanding the underlying causes of errors and by modifying the educational approach and, possibly, the teaching language accordingly. We analyze students' exercise submissions and categorize student errors according to compiler error messages and then manually according to the observed underlying cause. Our study complements earlier studies on the topic by applying computer and manual analysis while focusing on providing descriptive statistics of difficulties specific to FP languages. We conclude that the majority of student errors, regardless of cause, are reported by three different compiler error messages that are not well understood by students. In addition, syntactic features, such as precedence, the syntax of function application, and deeply nested statements, cause difficulties throughout the course.
    ISSN: 0956-7968
    E-ISSN: 1469-7653
    Source: CrossRef
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  • 10
    Language: English
    In: Information Sciences, 2011, Vol.181(12), pp.2488-2511
    Description: This paper proposes the employment of multiple scale factor values within distributed differential evolution structures. Four different scale factor schemes are proposed, tested, compared and analyzed. Two schemes simply employ multiple scale factor values and two also include an update logic during the evolution. The four schemes have been integrated for comparison within three recently proposed distributed differential evolution structures and tested on several various test problems. Numerical results show that, on average, the employment of multiple scale factors is beneficial since in most cases it leads to significant improvements in performance with respect to standard distributed algorithms. Although proper choice of a scale factor scheme appears to be dependent on the distributed structure, any of the proposed simple schemes has proven to significantly improve upon the single scale factor distributed differential evolution algorithms.
    Keywords: Differential Evolution ; Evolutionary Algorithms ; Distributed Algorithms ; Scale Factor ; Structured Populations ; Optimization Algorithms ; Computational Intelligence Optimization ; Engineering ; Library & Information Science
    ISSN: 0020-0255
    E-ISSN: 1872-6291
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