Visualizations have been used to explain algorithms to learners, in order to help them understand complex processes. These ‘explanatory visualizations’ can help learners understand computer algorithms and data-structures. But most are created by an educator and merely watched by the learner. In this paper, we explain how we get learners to plan and develop their own explanatory visualizations of algorithms. By actively developing their own visualizations learners

gain a deeper insight of the algorithms that they are explaining. These depictions can also help other learners understand the algorithm

Headleand, Chris

Jackson, J. R.

Ritsos, Panagiotis D.

Roberts, Jonathan C.

University of Lincoln Institutional Repository

Creating Explanatory Visualizations of Algorithms for Active LearningJonathan C. Roberts∗Bangor University, UKJames Jackson†Bangor University, UKChristopher Headleand‡University of Lincoln, UKPanagiotis D. Ritsos§University of Chester, UKABSTRACTVisualizations have been used to explain algorithms to learners, inorder to help them understand complex processes. These ‘explana-tory visualizations’ can help learners understand computer algo-rithms and data-structures. But most are created by an educator andmerely watched by the learner. In this paper, we explain how we getlearners to plan and develop their own explanatory visualizations ofalgorithms. By actively developing their own visualizations learn-ers gain a deeper insight of the algorithms that they are explaining.These depictions can also help other learners understand the algo-rithm.Keywords: Explanatory visualization, Information Visualization,Teaching visualization, Learning SupportIndex Terms: H.5.2 [Interfaces and Presentation]: UserInterfaces—Graphical User interfaces (GUI). K.3.2 [ComputingMilieux]: Computers & Education—Computer Science Education1 INTRODUCTIONLearners often find computer graphics and scientific visualizationalgorithms complex and difficult to understand. Explanatory vi-sualizations can help tell the story of how a process occurs or analgorithm works on a dataset. For instance, displaying the differentcases of the Marching Cubes algorithm [4] is demonstrated moreeasily by animating the cases with a test dataset and an explanatoryvisualization presentation.Computing education is an area where explanatory visualizationshave been successfully used. For example, searching the internetfor “visualization of sorting algorithms” produces a large numberof informative examples. Most of the search results are anima-tion videos and while they do explain the algorithms, they havebeen hand-crafted by the educator and are merely ‘viewed’ by thelearner. There are some examples where the learner can interactwith a tool to change parameters and tryout different algorithm con-figurations, but still the workload – and especially the design andcreative imagination – resides with the educator.We propose a different strategy, where the learner is actively in-volved in making their own explanatory visualization. This expe-riential learning approach enables the learner to engage with thedesign and makes their own decisions as to how the data is visual-ized, taking ownership of their learning. While this active learningprocess may take longer it gives the learner a much deeper under-standing of the underlying principles of the algorithms. Studentscomprehend better the important steps and can explain the conceptsin a discerning way.Our methodology is to get students to individually think, plan,and develop their own explanatory visualizations. The students cri-tique their work throughout the process and so develop their critical∗e-mail: j.c.roberts@bangor.ac.uk†e-mail: j.jackson@bangor.ac.uk‡e-mail: cheadleand@lincoln.ac.uk§e-mail: p.ritsos@chester.ac.ukthinking, in addition to their application skills. Fig. 1 shows our Ex-planatory Visualization active learning cycle, broken down into sixstages. We demonstrate the work, with the case study of a studentwho visualized the Marching Squares algorithm.2 BACKGROUND & RELATED WORKWhile different styles of visualization exist, our approach focuseson explanatory techniques. In this paper we focus on a third yearComputer Graphics & Visualization course for Computer Scienceundergraduate-major students. These students are studying for theirthree-year BSc honors degree in the UK. In Semester 1, learnersperform all the research and planning stages, and in Semester 2they start coding, reflect on their code and demonstrate their so-lutions. The advantage of using active learning [5] techniques isthat learners more proactively interact with the material engagingmulti-sensory learning, rather than merely listening passively to theteacher. Students can become motivated by the task, if the task issomething interesting and they see it being worthwhile. Grissom etal. [1] write “The true value of using visualizations may lie not intheir content but rather in their serving as a motivational factor tomake students work harder”.Active learning techniques are not new, e.g., Stasko [7] used an-imation as a learning aid, and Hundhasuen and Douglass [2] eval-uate the benefits of using visualizations to help students learn al-gorithms, whereas tools such as Greenfoot (greenfoot.org) use ani-mation to teach programming skills. But, most prior work only fo-cuses on the (i) design and (ii) implementation of these algorithms.We, however, have developed a formal structure, that draws uponKolb’s [3] experiential learning cycle. We integrate thinking, withmodifying (through improvements from formative feedback). Inte-grating, experiencing and practicing the ideas through sketching-by-design, building the tool, and reflecting on the work. Further-more, our approach takes the student through the entirety of theBlooms taxonomy, from knowledge to synthesis and evaluation.3 METHODOLOGY & CASE STUDYThe 20 credit computer graphics & visualization course is splitover two teaching Semesters (before and after Christmas). InSemester 1, the students are first given traditional lectures on allthe algorithms, choose their topic and started planning their visu-alization. Forty students chose from a list of 50 topics, includingMarching Squares/Cubes, Volume Rendering, Ray-tracing, linearinterpolation and use of transfer functions. Because each studentinvestigates their own algorithm, they can readily work together andexchange design ideas without problems of plagiarism. Students, inSemester 2, develop their code and complete the assessment.Our formal structure follows six parts (see Fig. 1 and explainedbelow). At each stage, their work is evaluated formatively (withthe teacher giving individual verbal feedback in a classroom set-ting), learners improve this version and submit for summative as-sessment. We include examples from a student (Tom) who chosethe Marching Squares 2D piecewise linear contour algorithm.Research. Students look at books, papers from IEEE and ACMdigital libraries and other resources online to understand the algo-rithm or technique. Tom looked at the original Lorensen & Cline [4]algorithm and other papers.Summary-document. A two-page summary of the algorithm iscreated. Every student has the same structure of: History, PseudoFigure 1: Our Explanatory Visualization active learning cycle. Students choose an algorithm, perform research, plan through sketching, gainformative and summative marks and demonstrate their tool that demonstrates a computer graphics or visualization algorithm.code, Maths, Diagram, Application, Similar techniques, Refer-ences. Several indicators of quality exist here, from written de-scriptions to the quality of the diagram. One diagram from Tom’stwo-page summary report is shown in Fig. 2 clearly articulating themain parts of the algorithm.Design-by-sketching. Students use the Five Design-Sheetmethodology [6] to sketch different visualization concepts (sheet1), three alternative but potentially implementable designs (sheets2,3,4) and sketch the realization design (sheet5). Formative feed-back is given to the students on their ideas. Tom explored 23 ideas(on sheet 1), including: applying a threshold, matching the casein the look-up table, interpolating the piecewise segments to theirexact position after retrieving them from the lookup-table, the actof marching from one voxel to another, resolving ambiguities, togenerating statistical analysis over different cases from given data.Storyboard. The learners create a storyboard to describe themain key stages of their explanatory visualization. This further con-firms their knowledge of the main aspects of the algorithm.Code. The students had to use OpenGL or WebGL due to pre-requisite course requirements. However, other graphics librariescould be readily used instead. Over a 3 month development pe-riod, students met weekly with the academic tutor to gain feedbackon their progression. Tom chose to develop an interactive, WebGLapplication. Users can generate test data to visualize (he spent ef-fort to make it random, but to filter out very small unconnectedscalar points), and then animate the index and contour generationper voxel.Technical report. Finally, students wrote a technical report ontheir work, which included a critical evaluation of their achieve-ments and explanatory visualization, and they gave a demonstrationof their visualization to the tutor.111111221213214122131111100000011010110101101000001 10 01100=12d ca b a b c dthreshold with iso valueFigure 2: Marching Squares figure from Tom’s two-page summarysheet, that explains the main stages.4 DISCUSSION & CONCLUSIONSWe have made a preliminary evaluation of the process. Studentscompleted an anonymous questionnaire of 10 questions. We re-ceived positive and encouraging feedback. One student wrote “itbroke down a project into its logical steps (design – implementation– evaluation)”, another “it is a good way to guide students from un-derstanding, thinking and then implementing”. The two-page sum-mary sheet gets the students thinking about the algorithm, and thetutor can ascertain if a student does actually understand the algo-rithm! Extra help can be given, if a student is confused. Sketchingusing the Five Design-Sheet [6] method encourages the students tobe creative and imaginative. While students were given the opportu-nity to re-submit each FdS only two students decided to re-submit.On reflection, we believe that students would benefit from moredesign teaching in their computer science program, and using theFdS multiple times would benefit the students’ learning. Previousyears, we have used a more ad hoc method of design and buildingin this module. Our 6-part structure developed better solutions, andthe grades were (on average) better than previous years. No plagia-rism was found and students benefited from being able to discusstheir work with others: “you have the opportunity to . . . explain thealgorithm you are creating with students in the same course”. Werecommend other educators to use a similar active learning strategyand to use explanatory visualization in learning. because studentsperform better, and understand the work more deeply.REFERENCES[1] S. Grissom, M. F. McNally, and T. Naps. Algorithm visualization inCS education: Comparing levels of student engagement. In Proc. ACMSymp. Soft. Vis., pages 87–94. ACM, 2003.[2] C. Hundhausen and S. Douglas. Using visualizations to learn algo-rithms: should students construct their own, or view an expert’s? InIEEE Symp. on Visual Languages, pages 21–28, 2000.[3] A. Y. Kolb and D. A. Kolb. Learning styles and learning spaces: En-hancing experiential learning in higher education. Academy of manage-ment learning & education, 4(2):193–212, 2005.[4] W. E. Lorensen and H. E. Cline. Marching cubes: A high resolution 3dsurface construction algorithm. In SIGGRAPH, pages 163–169, NewYork, NY, USA, 1987. ACM.[5] C. Meyers and T. B. Jones. Promoting Active Learning. Strategies forthe College Classroom. ERIC, 1993.[6] J. C. Roberts, C. Headleand, and P. D. Ritsos. Sketching designs usingthe five design-sheet methodology. IEEE Trans. on Vis. and Comp.Graph., 22(1):419–428, Jan 2016.[7] J. T. Stasko. Using student-built algorithm animations as learning aids.SIGCSE Bull., 29(1):25–29, Mar. 1997.

Creating explanatory visualizations of algorithms for active learning

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Creating explanatory visualizations of algorithms for active learning

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