SORTING OUT SORTING: A CASE STUDY OF SOFTWARE VISUALIZATION FOR TEACHING

COMPUTER SCIENCE

Ronald Baecker University of Toronto

Presented by David Burlinson

2/25/2015

OUTLINE

Introduction and Background

Sorting out Sorting Problems Solutions Analysis

Discussion

INTRODUCTION AND BACKGROUND Visualization in Computer Science Education

Drawings and diagrams to represent concepts Professors covering blackboards (and themselves) with chalk Students sketching hurried representations Data structures, control flow, hierarchies, pointer chains, nesting of procedures, scope of variables, memory allocation..

Drawbacks?

INTRODUCTION AND BACKGROUND

Program behavior is dynamic! Difficult to represent on paper

We (Instructors and students) are prone to errors and inaccuracies! Bad Timing Skipping or rearranging steps Big picture may not be clear

INTRODUCTION AND BACKGROUND

Motivation for animated visualization of computer science concepts Faithful representation Illustration of temporal nature Efficient display of repetitive computations Perceptually salient groups, structures, relationships, and causality

We can see how the program is running

SORTING OUT SORTING

SORTING OUT SORTING: EARLY WORK

Effective algorithm visualization must: Abstract or highlight only the essential aspects Carefully consider text and data representation and when to update them Suppress extraneous detail and enhance relevant features Choose appropriate timing and pacing

Baecker and his students spent seven years experimenting with animations of algorithms and programs

SORTING OUT SORTING

• The film focuses on nine sorting methods and their complexities:

• Insertion Sorts• Linear Insertion Sort• Binary Insertion Sort• Shellsort

• Exchange Sorts• Bubblesort• Shakesort• Quicksort

• A student who has watched the animation carefully could program some of the methods herself

• Selection Sorts• Straight Selection Sort• Tree Selection Sort• Heapsort

SORTING OUT SORTING

The film has been used with computer science students at various levels Generally shown in introductory CS courses at high school and university levels

As a motivational aid

Also shown in first or second courses on programming To introduce the concept of efficiency of different solutions to the same problem

SORTING OUT SORTING: PROBLEMS Early drafts lacked consistent visual conventions (confusing!) Color to denote ‘sorted’ or ‘still to consider’ Visual encoding strategies for elements

Timing is an issue Steps must be presented slowly enough for a narrator to give supplemental explanations

Once an algorithm is understood, later steps can be boring Some algorithms appear to slow down as they go along whereas others appear to speed up at the end

SORTING OUT SORTING: PROBLEMS Larger sets are problematic too New representations to compress desired info onto the screen are necessary

Consistency is required so as not to allow false interpretation of a cue

Being literal and going step-by-step aids initial understanding

BUT: have to go beyond this to add visual and dramatic interest Especially with more advanced material

“Totally literal and consistent presentations can be boring.”

SORTING OUT SORTING: SOLUTIONS The film is broken up into three groups of three

Algorithms in each group are shown in increasing order of efficiency (And therefore order of complexity of explanation)

Visual encoding strategies changed for each group Color for ‘yet to be considered’ and ‘sorted’ Color boundary for items being compared Items fade when they are not currently being considered by the algorithm Vertical bars, horizontal bars, or numbers to represent items

SORTING OUT SORTING: SOLUTIONS No pointers, no labels, no gimmicks Attributes of data and processes affecting them are shown by the aforementioned visual cues, motion, and accompanying narrative

Titles of each algorithm were scrambled, then ordered by each respective algorithm to show a preview of sorts

Each method is then demonstrated on a small data set Pace is sometimes increased after a few initial passes

SORTING OUT SORTING: SOLUTIONS Each group’s efficiency is then presented Line graphs show number of comparisons, number of data movements, and actual execution time

All three techniques race on the same data set

After all three groups, a grand race compares the efficiency across all nine algorithms 2500 data items Quicksort and Treesort: ~20 seconds Other n log(n) algorithms: ~40 seconds n^2 algorithms: … ~55 minutes

SORTING OUT SORTING: SOLUTIONS https://www.youtube.com/watch?v=SJwEwA5gOkM

9-way race 25:37

SORTING OUT SORTING: ANALYSIS Efficacy and influence of the 30 minute film were rated highly Interviews with students Informal, unpublished experiment

With no marketing, it sold more than 600 copies

Baecker received lots of praise and many letters “I am profoundly deaf... I was not able to understand ANY of the voice-over “I am profoundly deaf... I was not able

to understand ANY of the voice-over commentary on the film. I could hear someone talking; I could hear music, and beeps; but I could not understand WHAT was being said. Interestingly enough, this was not any particular disadvantage. I felt that I could understand most of the film... without hearing any of the commentary.” (Bev Biderman, 12 March 1982)commentary on the film. I could hear someone talking; I could hear music, and beeps; but I could not understand WHAT was being said. Interestingly enough, this was not any particular disadvantage. I felt that I could understand most of the film... without hearing any of the commentary.” (Bev Biderman, 12 March 1982)

Most importantly, it stimulated a significant amount of further work in algorithm analysis

SORTING OUT SORTING: ANALYSIS

Weaknesses of the film: ‘atrocious’ typography ‘mediocre’ color Sub-optimal timing (diverse audience) Heavy focus on efficiency, ignores best/worst case analyses Omits merge and distribution sorts

SORTING OUT SORTING: ANALYSIS

Lessons learned: Effective symbolism depends on size, scale, and context of items With careful illustration and timing, significant insight into algorithm behavior is possible

Control over motion dynamics must be powerful and flexible to communicate effectively

Timing is absolutely critical

DISCUSSION

Periodic surveys of instructor interest in Algorithm Visualization show: Over 90% indicate a strong positive view in favor of AV use Only about 10% indicate frequent use of AV in their courses 50-75% indicated occasional usage Most agree that AV can help students learn computer science

Why do you think this discrepancy exists?

Where do you stand on the usage of AV and similar visualization methods in the classroom? What roles should they play?

DISCUSSION

Roughly half of surveyed instructors’ problems with AV usage stem from locating suitable examples

The other half relate to issues integrating AVs into their courses and lectures

What can be done to combat these issues?

Do you have any anecdotal evidence or experiences to support usage of AVs?

DISCUSSION

Resources:

http://www.cs.usfca.edu/~galles/visualization/Algorithms.html

http://algoviz.org/avcatalog

http://people.cs.vt.edu/~shaffer/CS6604/Papers/AVpedagogypost.pdf

THANK YOU!

I appreciate your attention and involvement!