Abstract
Deep Learning has revolutionized Machine Learning, enhancing our ability to solve complex computational problems. From image classification to speech recognition, the technology can be beneficial in a broad range of scenarios. However, the barrier to entry is quite high, especially when programming skills are missing. In this paper, we present the development of a learning application that is easy to use, yet powerful enough to solve practical Deep Learning problems. We followed the human-centered design approach and conducted a technical evaluation to identify solvable classification problems. Afterwards, we conducted an online user evaluation to gain insights on users’ experience with the app, and to understand positive as well as negative aspects of our implemented concept. Our results show that participants liked using the app and found it useful, especially for beginners. Nonetheless, future iterations of the learning app should step-wise include more features to support advancing users.
About the authors
Sven Schultze is a Master Student for Computer Science at the University of Oldenburg. He conducts research in the field of Machine Learning and Human-Computer Interaction. He is especially interested in Deep Learning, Computer Vision, and User Experience.
Dr. Uwe Gruenefeld is a Postdoc Researcher in Human-Computer Interaction at the University of Duisburg-Essen, Germany. He is fascinated by Augmented and Virtual Reality, with a strong interest in Intelligent User Interfaces. His research has mainly focused on investigating Peripheral Visualization, Attention Guidance, and Multimodal Interfaces.
Prof. Dr. Susanne Boll is Professor of Media Informatics and Multimedia Systems in the Department of Computing Science at the University of Oldenburg in Germany. She serves on the executive board of the OFFIS Institute for Information Technology in Oldenburg and heads the competence cluster Human Machine Collaboration. Her research area lies at the intersection of human computer interaction and interactive multimedia. She is developing novel interaction technology that is shaped towards a respectful and beneficial cooperation of human and technology in a future more and more automated world.
Acknowledgment
We would like to thank our Deep Learning experts from the field of HCI Abdallah El Ali, Niels Henze, and Sven Mayer.
References
[1] Peter Brusilovsky. Explanatory visualization in an educational programming environment: Connecting examples with general knowledge. In Brad Blumenthal, Juri Gornostaev, and Claus Unger, editors, Human-Computer Interaction, pages 202–212, Berlin, Heidelberg, 1994. Springer Berlin Heidelberg.10.1007/3-540-58648-2_38Search in Google Scholar
[2] Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pages 1721–1730, 2015.10.1145/2783258.2788613Search in Google Scholar
[3] Wei Chen, Haifeng Wei, Suting Peng, Jiawei Sun, Xu Qiao, and Boqiang Liu. Hsn: Hybrid segmentation network for small cell lung cancer segmentation. IEEE Access, 7:75591–75603, 2019.10.1109/ACCESS.2019.2921434Search in Google Scholar
[4] Shuchi Grover and Satabdi Basu. Measuring student learning in introductory block-based programming: Examining misconceptions of loops, variables, and boolean logic. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, pages 267–272. ACM, 2017.10.1145/3017680.3017723Search in Google Scholar
[5] Gunnar Harboe and Elaine M. Huang. Real-world affinity diagramming practices: Bridging the paper-digital gap. In Proc. 33rd Annual ACM Conf. Human Factors in Computing Systems, pages 95–104. ACM, 2015.10.1145/2702123.2702561Search in Google Scholar
[6] Fred Hohman, Nathan Hodas, and Duen Horng Chau. Shapeshop: Towards understanding deep learning representations via interactive experimentation. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 2017.10.1145/3027063.3053103Search in Google Scholar PubMed PubMed Central
[7] Fred Hohman, Minsuk Kahng, Robert Pienta, and Duen Horng Chau. Visual analytics in deep learning: An interrogative survey for the next frontiers. IEEE Transactions on Visualization and Computer Graphics, 2018.10.1109/TVCG.2018.2843369Search in Google Scholar PubMed PubMed Central
[8] Fred Hohman, Haekyu Park, Caleb Robinson, and Duen Horng Chau. Summit: Scaling deep learning interpretability by visualizing activation and attribution summarizations. IEEE Transactions on Visualization and Computer Graphics (TVCG), 2020.10.1109/TVCG.2019.2934659Search in Google Scholar PubMed
[9] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132–7141, 2018.10.1109/CVPR.2018.00745Search in Google Scholar
[10] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.Search in Google Scholar
[11] ISO. Ergonomics of human-system interaction – part 210: Human-centred design for interactive systems. Standard ISO-9241-210:2019, International Organization for Standardization, Geneva, CH, 2019.Search in Google Scholar
[12] R. Ji, K. Li, Y. Wang, X. Sun, F. Guo, X. Guo, Y. Wu, F. Huang, and J. Luo. Semi-supervised adversarial monocular depth estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1, 2019.10.1109/TPAMI.2019.2936024Search in Google Scholar PubMed
[13] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.Search in Google Scholar
[14] C. Lewis. Using the “thinking Aloud” Method in Cognitive Interface Design. Research report. IBM T.J. Watson Research Center, 1982.Search in Google Scholar
[15] Bettina Laugwitz, Theo Held, and Martin Schrepp. Construction and evaluation of a user experience questionnaire. In Andreas Holzinger, editor, HCI and Usability for Education and Work, pages 63–76, Berlin, Heidelberg, 2008. Springer Berlin Heidelberg.10.1007/978-3-540-89350-9_6Search in Google Scholar
[16] Zachary C Lipton. The mythos of model interpretability. Queue, 16(3):31–57, 2018.10.1145/3236386.3241340Search in Google Scholar
[17] Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, and Jeff Clune. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In Advances in neural information processing systems, pages 3387–3395, 2016.Search in Google Scholar
[18] Jakob Nielsen and Rolf Molich. Heuristic evaluation of user interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’90, page 249–256, New York, NY, USA, 1990. Association for Computing Machinery.10.1145/97243.97281Search in Google Scholar
[19] Daniel Smilkov, Shan Carter, D Sculley, Fernanda B Viégas, and Martin Wattenberg. Direct-manipulation visualization of deep networks. arXiv preprint arXiv:1708.03788, 2017.Search in Google Scholar
[20] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929–1958, 2014.Search in Google Scholar
[21] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9, 2015.10.1109/CVPR.2015.7298594Search in Google Scholar
[22] Ben Shneiderman, Catherine Plaisant, Maxine Cohen, Steven Jacobs, Niklas Elmqvist, and Nicholas Diakopoulos. Designing the User Interface: Strategies for Effective Human-Computer Interaction. Pearson, 6th edition, 2016.Search in Google Scholar
[23] Bret Victor. Explorable explanations, Mar 2011.Search in Google Scholar
[24] Mathukumalli Vidyasagar. A theory of learning and generalization. Springer-Verlag, 2002.Search in Google Scholar
[25] Jason Wang and Luis Perez. The effectiveness of data augmentation in image classification using deep learning. in Convolutional Neural Networks Vis. Recognit, 2017.Search in Google Scholar
[26] David Weintrop and Uri Wilensky. Comparing block-based and text-based programming in high school computer science classrooms. ACM Transactions on Computing Education (TOCE), 18(1):3, 2017.10.1145/3089799Search in Google Scholar
[27] Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. How transferable are features in deep neural networks? In Advances in neural information processing systems, pages 3320–3328, 2014.Search in Google Scholar
[28] Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson. Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579, 2015.Search in Google Scholar
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