This repository contains tools for studying misconceptions in visual algorithm simulation exercises implemented with JSAV and the A+ LMS. They are part of Artturi Tilanterä's Master's Thesis "Towards Automatic Advice in Visual Algorithm Simulation" for Department of Computer Science at Aalto University on years 2019-2020.
Visual algorithm simulation is a type of computerised, interactive exercise for teaching theory of programming (algorithms). The student performs the steps of a specified algorithm with given input data by interacting with a visual representation of a data structure. After completing the exercise, they receive immediate, automatic feedback on the correctness of their simulation steps.
The OpenDSA electronic textbook project contains visual algorithm simulation exercises. These exercises are implemented with the JavaScript Algorithm Visualisation library (JSAV).
Contact: artturi.dot.tilantera at aalto.dot.fi
| Software | File(s) | License |
|---|---|---|
| jQuery | inspector/css/jquery-ui.min.css | MIT |
| inspector/lib/jquery-ui.min.js | ||
| inspector/lib/jquery.min.js | ||
| inspector/lib/jquery.transit.js | ||
| JSAV | inspector/css/JSAV.css | MIT |
| inspector/lib/JSAV.js | ||
| JSAV downloader | JSAV-downloader.py | GNU GPLv3 |
| JSAV inspector | inspector/css/JSAV-inspector.css | GNU GPLv3 |
| inspector/JSAV-inspector.html | ||
| inspector/JSAV-inspector.js | ||
| JSAV matcher | matcher/* | GNU GPLv3 |
| Raphaël | inspector/lib/raphael.js | MIT |
License files:
JSAV downloader requires:
- Python 3 http://www.python.org. (Python 3.5.2 tested)
- Python libraries: requests;
pip install requests - A running A+ LMS instance https://apluslms.github.io/ and teacher's access rights to a course to download exercise submissions.
JSAV matcher requires Python 3 similar to JSAV downloader.
JSAV inspector requires a web browser with HTML5, CSS and JavaScript support. It has been tested with Mozilla Firefox 71.0.
File: JSAV-downloader.py
This tool retrieves JSAV exercise submissions from the A+ LMS.
It is a Python script where each exercise instance is specified
manually. The exercise instance is a tuple
File: inspector/JSAV-inspector.html
This tool creates slideshows of exercise submissions. It runs in a web browser as single, static web page, meaning that no server setup is needed. The application is implemented in HTML5, CSS and JavaScript and it utilises the JSAV library. The tool can open a JSON file produced with the JSAV downloader and display students' solutions to the Build-heap exercise.
File: matcher/*
This tool reads a file created by the JSAV downloader. It matches submissions against known misconceptions.
The following scientific publications are relevant to the topic.
Ville Karavirta and Clifford A. Shaffer. Jsav: The javascript algorithm visualization library. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE ’13, pages 159–164, New York, NY, USA, 2013. ACM. ISBN 978-1-4503-2078-8. doi: http://doi.acm.org/10.1145/2462476.2462487
Ville Karavirta and Clifford A. Shaffer. Creating engaging online learning material with the jsav javascript algorithm visualization library. IEEE Transac- tions on Learning Technologies, 9(2):171–183, April 2016. ISSN 1939-1382. doi: http://doi.acm.org/10.1109/TLT.2015.2490673
Ville Karavirta, Ari Korhonen, and Otto Seppälä. Misconceptions in visual algo- rithm simulation revisited: On ui’s effect on student performance, attitudes, and misconceptions. In 2013 Learning and Teaching in Computing and Engineering, pages 62–69, March 2013. doi: http://dx.doi.org/10.1109/LaTiCE.2013.35
Otto Seppälä, Lauri Malmi, and Ari Korhonen. Observations on student misconceptions—a case study of the build – heap algorithm. Computer Science Education, 16(3):241 – 255, 2006. doi: http://dx.doi.org/10.1080/08993400600913523
Ari Korhonen. Visual Algorithm Simulation. PhD thesis, Helsinki University of Technology, Department of Computer Science and Engineering, Espoo, Finland, 2003. Available via: https://learningcentre.aalto.fi/en/
Ari Korhonen, Otto Seppälä, and Juha Sorva. Automatic recognition of misconceptions in visual algorithm simulation exercises. In 2015 IEEE Frontiers in Education Conference (FIE). IEEE, October 2015. doi: http://dx.doi.org/110.1109/FIE.2015.7344046