Using visualizations to learn algorithms: should students construct their own, or view an expert's?

Hundhausen, Christopher D.; Douglas, Sarah A.

doi:10.1109/vl.2000.874346

Cited by 39 publications

(49 citation statements)

References 8 publications

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“…• Constructing their own input data sets [40, Chapter 9], • Making predictions regarding future visualization states [11], • Programming the target algorithm [31], • Answering strategic questions about the visualization [23,44], • Constructing their own visualizations [26]. Given this trend, it makes sense to design further experimental studies that more closely examine the educational benefits of various forms of active engagement.…”

Section: Introductionmentioning

confidence: 99%

Exploring the role of visualization and engagement in computer science education

Naps

Rodger

Velázquez‐Iturbide

et al. 2002

Working Group Reports From ITiCSE on Innovation and Technology in Computer Science Education

196

102

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Visualization technology can be used to graphically illustrate various concepts in computer science. We argue that such technology, no matter how well it is designed, is of little educational value unless it engages learners in an active learning activity. Drawing on a review of experimental studies of visualization effectiveness, we motivate this position against the backdrop of current attitudes and best practices with respect to visualization use. We suggest a new taxonomy of learner engagement with visualization technology. Grounded in Bloom's wellrecognized taxonomy of understanding, we suggest metrics for assessing the learning outcomes to which such engagement may lead. Based on these taxonomies of engagement and effectiveness metrics, we present a framework for experimental studies of visualization effectiveness. Interested computer science educators are invited to collaborate with us by carrying out studies within this framework.

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Section: Introductionmentioning

confidence: 99%

Exploring the role of visualization and engagement in computer science education

Naps

Rodger

Velázquez‐Iturbide

et al. 2002

Working Group Reports From ITiCSE on Innovation and Technology in Computer Science Education

196

102

View full text Add to dashboard Cite

show abstract

“…Several authors have been concerned about the use of graphic interfaces that enable a way of communication between the user and the computer not restricted to a textual form (Hansen et al, 1999) (Stasko and Kehoe, 1996) (Hundhausen and Douglas, 2000).…”

Section: Animationmentioning

confidence: 99%

“…Students face different kinds of difficulties at various levels and the traditional teaching/learning methods are no longer effective resulting in a high rate of failures (Hundhausen and Douglas, 2000). Such students' main difficulties are (Proulx, 2000):…”

Section: Introductionmentioning

confidence: 99%

Computer-supported Techniques to Increase Students Engagement in Programming

Tavares¹,

Henriques

Gomes³

2016

Proceedings of the 8th International Conference on Computer Supported Education

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Abstract:One of the main reasons that justify the student's failure in (introductory) programming courses is the lack of motivation that impacts on the knowledge acquisition process, affecting learning results. As soon as students face the difficulties concerning the development of algorithms or the coding in a programming language, they give up and do not try harder to solve other problems; they think it is a demanding activity and feel frustrated.In this paper we describe in detail an experiment conducted to verify the effectiveness, in terms of the increase in motivation and in knowledge acquisition, of combining program Animation tools with the immediate feedback provided by Automatic Evaluations Systems.

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“…The effectiveness of an algorithm visualization is determined by measuring its pedagogical value [Stasko et al, 1990Lawrence, 1993;Hundhausen, 2000 2002; Gurka and Wayne, 1996; Wilson and Aiken, 1996]. By pedagogical value of an algorithm visualization we generally mean how much students learned about that algorithm by using the visualization.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

“…Measuring the effectiveness of a visualization often involves a group of students using the visualization, a group of students using textual material, and a group of students using both the visualization and the textual materials, to understand the algorithm for some amount of time [Stasko et al, 1990Lawrence, 1993;Hundhausen, 2000Hundhausen, 2002. After the students use each of these learning materials a post-test is normally taken which examines the students knowledge about the algorithm.…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

Effective features of algorithm visualizations

Saraiya

Shaffer

McCrickard

et al. 2004

Proceedings of the 35th SIGCSE Technical Symposium on Computer Science Education

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(Abstract)Current research suggests that by actively involving students, you can increase pedagogical value of algorithm visualizations. We believe that a pedagogically successful visualization, besides actively engaging participants, also requires certain other key features. We compared several existing algorithm visualizations for the purpose of identifying features that we believe increase the pedagogical value of an algorithm visualization. To identify the most important features from this list, we conducted two experiments using a variety of the heapsort algorithm visualizations.The results of these experiments indicate that the single most important feature is the ability to control the pace of the visualization. Providing a good data set that covers all the special cases is important to help students comprehend an unfamiliar algorithm. An algorithm visualization having minimum features that focuses on the logical steps of an algorithm is sufficient for procedural understanding of the algorithm. To have better conceptual understanding, additional features (like an activity guide that makes students cover the algorithm in detail and analyze what they are doing, and pseudocode display of an algorithm) may prove to be helpful, but that is a much harder effect to detect.iii

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Using visualizations to learn algorithms: should students construct their own, or view an expert's?

Cited by 39 publications

References 8 publications

Exploring the role of visualization and engagement in computer science education

Exploring the role of visualization and engagement in computer science education

Computer-supported Techniques to Increase Students Engagement in Programming

Effective features of algorithm visualizations

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