algorithm visualisation (AV) tool is commonly used to learn data structures. However, since that tool does not address technical details, some students may not know how to implement the data structures. This paper int...
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algorithm visualisation (AV) tool is commonly used to learn data structures. However, since that tool does not address technical details, some students may not know how to implement the data structures. This paper integrates the AV tool with Program visualisation (PV) tool to help the students understanding the data structures' implementation. The integration (which is implemented as a tool named DS-PITON) works similarly as a PV tool except that the data structures are visualised with the AV tool. Through quasi experiments, it can be stated that DS-PITON helps students to get better assessment score and to complete their assessment faster (even though the impact on completion time can work in reverse on slow-paced students). Further, according to a questionnaire survey, the students believe that DS-PITON helps them learning data structure materials. (C) 2019 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University.
Wireless sensors provide services and collect data from environment. Some Wireless Sensor Networks (WSN) demand high-assurance performance such as WSN for military observation. To achieve the expected assurance, WSN c...
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ISBN:
(纸本)9781538684580
Wireless sensors provide services and collect data from environment. Some Wireless Sensor Networks (WSN) demand high-assurance performance such as WSN for military observation. To achieve the expected assurance, WSN constructors deploy the sensors in an experiential manner or using massive redundant nodes which leads to low efficiency and waste of sensor devices due to the unreasonable sensor layout. Although a number of approaches using mathematical algorithms are proposed trying to solve the sensor deployment location and efficiency issues, it still remains very difficult for WSN constructors without mathematical background to utilise these algorithms in calculating sensor positions during the deployment. This paper developed an approach using visualisation platform to make the WSN generation algorithms visible and understandable to constructors. It allows algorithm contributors register their algorithms and enables constructors to explore these algorithms through visualising the calculation configuration and results. An algorithm is implemented to verify the platform in this paper. As more algorithms register in the platform, constructors may have more options to conduct the high-assurance WSN generation.
The paper deals with learning of algorithms and protocols using visual media and it presents experience obtained with a system Algovision developed at Charles University, Prague. The teaching of the paper is that lear...
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ISBN:
(纸本)9789898111821
The paper deals with learning of algorithms and protocols using visual media and it presents experience obtained with a system Algovision developed at Charles University, Prague. The teaching of the paper is that learning objects and courses should attempt explaining why an algorithm or protocol achieves its goals rather than merely showing what is going on during the computation and/or communication and how the data change in time. This means visualising abstract topics like algorithm invariant, mathematical proof, researcher intuition, and a collection of paradigms used to achieve such task is presented, as it appeared during development of Algovision.
Traditional algorithm animation attempts to provide visualizations of the execution of a program on concrete data. In recent years a different approach has been proposed which attempts to visualize an "abstract e...
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ISBN:
(纸本)9781605581125
Traditional algorithm animation attempts to provide visualizations of the execution of a program on concrete data. In recent years a different approach has been proposed which attempts to visualize an "abstract execution" on "abstract data." This is based on Cousots' notion of abstract interpretations, in particular in the case of programs manipulating pointer structures this is based on so-called shape analysis. Shape analysis maps all possible heap configurations that can arise during a program's executions (a potentially unbounded set) to a finite number of "shape graphs" and it maps steps of the program to transitions between shape graphs. Every concrete execution of the program then corresponds to a sequence of transitions among shape graphs, the "abstract execution." Visualizing such an abstract execution is desirable since sets of shape graphs in a very strong sense encode invariants of the program and understanding the effect of a program usually benefits more from grasping what stays invariant than from seeing what changes. In this paper we combine the two approaches and argue for simultaneously visualizing a concrete and the corresponding abstract execution of a program. We have built a Visualizer for programs manipulating pointer structures that realizes this combined abstract/concrete visualization. It uses TVLA to automatically perform the shape analysis of a program. It allows to present abstract and concrete views in an extremely customizable way. This paper however does not present our Visualizer, but focuses on the technique to combine concrete and abstract visualizations.
Visual algorithm Simulation (VAS) exercise is an interactive application, which teaches an algorithm or a data structure. The exercise shows the student a visual representation of a data structure with initial data. T...
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Visual algorithm Simulation (VAS) exercise is an interactive application, which teaches an algorithm or a data structure. The exercise shows the student a visual representation of a data structure with initial data. The student imitates the execution of the algorithm by interacting with the visual representation. The student’s solution is graded automatically. A misconception about the algorithm being learned can manifest itself as systematic errors, which can be modelled as a new algorithm. It is assumed that a VAS exercise, which could detect automatically a misconception and give corrective feedback would further support learning.
This thesis includes a literature review on algorithm misconceptions and an empirical study. Four VAS exercises of OpenDSA e-textbook are reviewed by their program code: Evaluating a postfix expression, Build heap, Quicksort, and Dijkstra’s algorithm. A dataset of 1430 Build heap VAS submissions is analysed manually with ad-hoc software. The submissions are then automatically classified based on the misconceptions found.
The main result extends the set of known misconceptions of the Build-heap VAS exercise. 52 percent of the submissions were correct, 17 percent were misconceptions and the rest 31 percent had a lo0gical explanation. 95 per cent of submissions classified as misconception have multiple explanations with heap size of 10. The thesis presents a Python software, which can automatically classify the known misconceptions. Theory on how to generate VAS inputs, which support detection of misconceptions is discussed. The theory is applied by improving the input generation of Dijkstra’s algorithm exercise.
The thesis concludes that studying misconceptions in the VAS exercises of OpenDSA currently requires exercise-dependent work. Not all OpenDSA VAS exercises record enough data for later analysis. Moreover, the player and analysis software must be written separately for each exercise. There is need to develop the OpenDSA relat
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