Giselle Nodalo
Jordan Aiko Deja
ABSTRACT
There are several machine learning suites that are readily-available. However, these applications require a basic foundation in machine learning making them appear difficult to configure. We introduce a sandbox approach with the goal of designing alternative programming interactions for machine learning tasks. A set of guidelines have been drafted and supported with user interviews to validate the proposed design framework. Ten students with novice machine learning experience participated in the study to formulate a programming pipeline that was used to draft the guidelines based on the literary review. A problem statement was formed from the analysis of interview insights using UX Research techniques. The insights suggest that a visual sandbox approach helps reduce the learning curve of programming machine learning tasks. The design guidelines we drafted focused on the three design factors namely system intent, interaction, and algorithm visualization. Considering these guidelines, a prototype was produced that will undergo future testing and validation.
- Saleema Amershi. 2011. Designing for effective enduser interaction with machine learning. Proceedings of the 24th Annual ACM Symposium Adjunct on User Interface Software and Technology - UIST 11 Adjunct: 47--50. Google Scholar
Digital Library
- Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, et al. Guidelines for Human-AI Interaction - microsoft.com. Microsoft. Retrieved 2019 from https://www.microsoft.com/en-us/research/uploads/prod/2019/01/Guidelines-for-Human-AI-Interaction-camera-ready.pdf Google ScholarDigital Library
- Francisco Bernardo, Michael Zbyszyński, Rebecca Fiebrink, and Mick Grierson. 2017. Interactive Machine Learning for End-User Innovation. 2017 AAAI Spring Symposium Series.Google Scholar
- Michelle Patrick Cook. 2006. Visual representations in science education: The influence of prior knowledge and cognitive load theory on instructional design principles. Science Education 90, 6: 1073--1091.Google ScholarCross Ref
- Jordan Aiko Deja, Patrick Arceo, Darren Goldwin David, Patrick Lawrence Gan, and Ryan Christopher Roque. 2018. MyoSL: A Framework for Measuring Usability of Two-Arm Gestural Electromyography for Sign Language. Universal Access in Human-Computer Interaction. Methods, Technologies, and Users Lecture Notes in Computer Science: 146--159.Google Scholar
- Jordan Aiko Deja, Rafael Cabredo, and Toni-Jan Keith Monserrat. 2018. On Building an Emotion-based Music Composition Companion. Proceedings of the Asian HCI Symposium 18 on Emerging Research Collection - Asian HCI Symposium 18.Google ScholarDigital Library
- Jordan Aiko Deja, Kevin Gray Chan, Migo Andres Dancel, Allen Vincent Gonzales, and John Patrick Tobias. 2018. Flow: A Musical Composition Tool Using Gesture Interactions and Musical Metacreation. HCI International 2018 -- Posters Extended Abstracts Communications in Computer and Information Science: 169--176.Google Scholar
- Steven Halim, Zi Chun Koh, Victor Bo Huai Loh, and Felix Halim. 2012. Learning Algorithms with Unified and Interactive Web-Based Visualization. Olympiads in Informatics 6: 53--68.Google Scholar
- Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. 2009. The WEKA data mining software. ACM SIGKDD Explorations Newsletter 11, 1: 10. Google ScholarDigital Library
- Jabra. 2018. Work with word2vec on rapidminer. RapidMiner Community. Retrieved 2019 from https://community.rapidminer.com/discussion/49506/work-with-word2vec-on-rapidminerGoogle Scholar
- Teng Lee, James Johnson, and Steve Cheng. 2016. An Interactive Machine Learning Framework - arXiv. Retrieved 2018 from https://arxiv.org/pdf/1610.05463.pdfGoogle Scholar
- 360 Logica. 2015. The Aggravation with Conventional Black Box Testing. Retrieved 2018 from https://www.360logica.com/blog/the-aggravation-with-conventional-black-box-testing/Google Scholar
- Jovanovic Milõs, Vukicevic Milan, Delibãsic Boris, and Suknovic Milija. 2013. Using RapidMiner for Research:Experimental Evaluation of Learners. RapidMiner: Data Mining Use Cases and Business Analytics Applications: 439.Google Scholar
- Jakob Nielsen and Rolf Molich. 1990. Heuristic evaluation of user interfaces. Proceedings of the SIGCHI Conference on Human factors in Computing Systems Empowering people - CHI 90: 249--256. Google ScholarDigital Library
- Ibrahim Ouahbi, Fatiha Kaddari, Hassane Darhmaoui, Abdelrhani Elachqar, and Soufiane Lahmine. 2015. Learning Basic Programming Concepts by Creating Games with Scratch Programming Environment. Procedia - Social and Behavioral Sciences 191: 1479--1482.Google ScholarCross Ref
- Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay. 2011. Scikitlearn: Machine Learning in Python. J. Mach. Learn. Res. 12 (November 2011), 2825--2830. Google ScholarDigital Library
- Margaret Rouse. 2005. What is sandbox (computer security)? - Definition from WhatIs.com. SearchSecurity. Retrieved 2019 from https://searchsecurity.techtarget.com/definition/sandboxGoogle Scholar
- Advait Sarkar, Mateja Jamnik, Alan F. Blackwell, and Martin Spott. 2015. Interactive visual machine learning in spreadsheets. 2015 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC).Google ScholarCross Ref
- Douglas Schuler and Aki Namioka. 2009. Participatory design: principles and practices. CRC Press., Boca Raton. Google ScholarDigital Library
- scikit-learn developers. 2007. Putting it all together. scikit. Retrieved 2018 from https://scikit-learn.org/stable/tutorial/statistical_inference/putting_together.htmlGoogle Scholar
- 2018. Scratch - About. Scratch. Retrieved 2019 from https://scratch.mit.edu/aboutGoogle Scholar
- Clifford A. Shaffer, Matthew L. Cooper, Alexander Joel D. Alon, Monika Akbar, Michael Stewart, Sean Ponce, and Stephen H. Edwards. 2010. Algorithm Visualization: The State of the Field. Trans. Comput. Educ. 10, 3, Article 9 (August 2010), 22 pages. Google ScholarDigital Library
- Daniel Smilkov and Shan Carter. 2016. Tensorflow - Neural Network Playground. A Neural Network Playground. Retrieved from https://playground.tensorflow.org/Google Scholar
- Daniel Smikov, Shan Carter, D Sculley, Fernada B Viégas, and Martin Wattenberg. 2017. Direct-Manipulation Visualization of Deep Networks. Retrieved from http://poloclub.gatech.edu/idea2016/papers/p115-smilkov.pdfGoogle Scholar
- John Edel Tamani, Jan Christian Blaise Cruz, Joshua Raphaelle Cruzada, Jolene Valenzuela, Kevin Gray Chan, and Jordan Aiko Deja. 2018. Building Guitar Strum Models for an Interactive Air Guitar Prototype. In Proceedings of the 4th International Conference on Human-Computer Interaction and User Experience in Indonesia, CHIuXiD '18 (CHIuXiD '18), Yohannes Kurniawan, Eunice Sari, and Josh Adi Tedjasaputra (Eds.). ACM, New York, NY, USA, 18--22. Google ScholarDigital Library
- tortilla. Scratch Project. (2 August, 2018) Monster- a Scrolling Platformer/Maze. Scratch. Retrieved from https://scratch.mit.edu/projects/237536987/Google Scholar
- Waikato. 2017. Json format display error. WEKA. Retrieved from http://weka.8497.n7.nabble.com/Json-format-display-error-td39863.htmlGoogle Scholar
Index Terms
- On Building Design Guidelines for An Interactive Machine Learning Sandbox Application
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