Abstract
Everyday devices are becoming smarter. Machine Learning (ML) is increasingly more utilized in technology to augment the user experience. Furthermore, it is applied to improve the overall performance of systems. People constantly use intelligent agents in different forms through online shopping, social media, and entertainment. However, they do not understand the intrinsic functionality of the technology. Brain-Computer Interfaces (BCI) are more prominent in non-medical fields. Advances in hardware enable better ways to implement and use BCIs in more ubiquitous ways. There are also software advances that allow devices to connect and achieve signal processing in lower-end environments like the web. There is a need for the public to understand the social and ethical implications of Machine Learning. This research project aims to provide novices and non-CS majors with an explainable educational experience. Machine Teacher (MTeacher) is a web-based Machine Learning educational system that aims to implement less typical types of data (e.g. EEG) and gamified elements. Players can explore the concepts of Machine Learning through interactive activities, and our objective is to help increase their engagement, general ML knowledge, and self-efficacy. Initial results of a study reveal that physiological signals are more appealing to the users than traditional types of data, though they would prefer collecting the data themselves. Our future work aims to explore the addition of real-time physiological data acquisition and improve the application of gamification elements to study their effects on the learner’s experience.
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References
Chancellor, S., Baumer, E.P., De Choudhury, M.: Who is the “human” in human-centered machine learning: the case of predicting mental health from social media. In: Proceedings of the ACM on Human-Computer Interaction (CSCW), vol. 3, pp. 1–32 (2019)
Crawford, C.S., Gilbert, J.E.: Neuroblock: A block-based programming approach to neurofeedback application development. In: 2017 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), pp. 303–307. IEEE (2017)
Gresse, C., Jean, V.W., Pacheco, F.S., Bertonceli, M.F.: Visual tools for teaching machine learning in K-12: a ten-year systematic mapping. No. 0123456789. Springer, US (2021). https://doi.org/10.1007/s10639-021-10570-8. https://doi.org/10.1007/s10639-021-10570-8
Hernandez-Cuevas, B., Egbert, W., Denham, A., Mehul, A., Crawford, C.S.: Changing minds: exploring brain-computer interface experiences with high school students. In: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–10 (2020)
Long, D., Magerko, B.: What is AI Literacy? competencies and design considerations, pp. 1–16. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3313831.3376727
Lotte, F.: A tutorial on eeg signal-processing techniques for mental-state recognition in brain-computer interfaces In: Guide to brain-computer music interfacing. pp. 133–161 (2014)
Lytle, N., et al.: Use, modify, create: Comparing computational thinking lesson progressions for stem classes. In: Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education, pp. 395–401 (2019)
Ma, L., Sun, B.: Machine learning and AI in marketing-connecting computing power to human insights. Int. J. Res. Mark. 37(3), 481–504 (2020)
Michaeli, T., et al.: Looking Beyond Supervised Classification and Image Recognition-Unsupervised Learning with Snap! (May 2020)
Portugal, I., Alencar, P., Cowan, D.: The use of machine learning algorithms in recommender systems: A systematic review. Expert Syst. Appl.Expert Syst. Appl.=Expert Syst. Appl. 97, 205–227 (2018)
Stegman, P.: Bci.js. Software (February 2020), (Retrieved Feb. 11, 2022). https://bci.js.org/
Sulmont, E., Patitsas, E., Cooperstock, J.R.: Can You Teach Me To Machine Learn? In: Proceedings of the 50th ACM Technical Symposium on Computer Science Education - SIGCSE 2019 (2019). https://doi.org/10.1145/3287324.3287392
Szafir, D., Mutlu, B.: Pay attention! designing adaptive agents that monitor and improve user engagement. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp. 11–20 (2012)
Tangermann, M., et al.: Review of the BCI competition IV. Front. Neurosci. 6, 55 (2012)
Vangipuram, S.k., Appusamy, R.: A survey on similarity measures and machine learning algorithms for classification and prediction. In: International Conference on Data Science, E-learning and Information Systems 2021, pp. 198–204 (2021)
Wan, X., Zhou, X., Ye, Z., Mortensen, C.K., Bai, Z.: SmileyCluster: supporting accessible machine learning in K-12 scientific discovery. In: Proceedings of the Interaction Design and Children Conference, IDC, pp. 23–35. (2020). https://doi.org/10.1145/3392063.3394440
Wang, Z.J., et al.: CNN 101: interactive visual learning for convolutional neural networks, pp. 1–7 (2020). https://doi.org/10.1145/3334480.3382899
Yi, S., Liu, X.: Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review. Complex Intell. Syst. 6(3), 621–634 (2020)
Yuksel, B.F., et al.: Human-in-the-loop machine learning to increase video accessibility for visually impaired and blind users In: Proceedings of the 2020 ACM Designing Interactive Systems Conference, pp. 47–60 (2020)
Zhou, X., Van Brummelen, J., Lin, P.: Designing AI learning experiences for K-12: emerging works, future opportunities and a design framework (September 2020). http://arxiv.org/abs/2009.10228
Zimmermann-Niefield, A., Polson, S., Moreno, C., Shapiro, R.B.: Youth making machine learning models for gesture-controlled interactive media. In: Proceedings of the Interaction Design and Children Conference, IDC, pp. 63–74 (2020). https://doi.org/10.1145/3392063.3394438
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Hernández-Cuevas, B.Y., Crawford, C.S. (2022). MTeacher: A Gamified and Physiological-Based Web Application Designed for Machine Learning Education. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. Novel Design Approaches and Technologies. HCII 2022. Lecture Notes in Computer Science, vol 13308. Springer, Cham. https://doi.org/10.1007/978-3-031-05028-2_29
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