Designing algorithm visualization on mobile platform: The proposed guidelines

Supli, Ahmad Affandi; Shiratuddin, Norshuhada

doi:10.1063/1.5005943

Cited by 3 publications

(2 citation statements)

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“…According to [28], due to the task complexity, there is a diverse range of misconceptions in learning data structures. For example, there are some misconceptions that students commit when trying to understand how different algorithms work [29] misconceptions on how to use such algorithms to manipulate data structures within the program [30] misconceptions in planning for a solution, and problems in debugging and tracing [28]. There are also misconceptions in understanding some of the fundamental programming concepts, such as nested loops, recursion, and linked lists, which are sources of difficulty [1].…”

Section: Causes Of the Misconceptions In Learning Dsa Coursementioning

confidence: 99%

“…The difficulty of understanding the dynamic nature of algorithm states is another cause of a high misconception [30], [34] Such difficulty relies on the algorithm itself which is derived from the dynamic step-by-step processes [34]. A dynamic view of a program brings together program codes, the state of the program, and the process that changes it, as well as the computer on which the program runs [if not the actual hardware, at least a notional machine [35].…”

Section: Causes Of the Misconceptions In Learning Dsa Coursementioning

confidence: 99%

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Effects of using problem-solving guide and explanatory support in program visualization tool on reducing students’ misconceptions in learning data structure concepts

Mtaho

2023

Bull.Socinf.The.App

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The tendency of novice programmers to hold misconceptions when learning data structures is one of the challenges that novice programmers face in computer science education. Holding misconceptions can result in students’ demotivation and high failure rates in learning the subject. This article presents the findings of an experimental study that was conducted to determine whether using the CeliotM Program visualization tool could help students learn CS 122 (data structures) at the College of Informatics and Virtual Education of the University of Dodoma with fewer misconceptions. The design chosen was a single factor between the experiment designs, with the number of errors committed by the students when writing programs as a dependent variable. The experimental group received instruction using the teaching technique employed by CeliotM, while the control group received instruction using the conventional lecture method. Results show that the use of CeliotM significantly reduced students misconceptions compared to the conventional lecture method. The study's important findings suggest that employing the CeliotM framework can help students understand data structures more effectively by minimizing their misconceptions and hence enhance their programming competencies.

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