Abstract
Onboarding of new employees is a common process in all companies. Many hours of qualified employees’ time need to be invested to teach new employees how to use the company’s internal systems. This process can be significantly eased by onboarding solutions leveraging application guides. However, if not personalized, the guides can quickly become annoying to users. This can be overcome by employing emotion detection in real-time, but the solutions face several major challenges, such as scalability, detection time, or model retraining. In this paper, we describe how we tackled these challenges and implemented an emotion detection-based personalization module in the onboarding solution YesElf. The module leverages the mouse interaction data of users to detect their confusion. We show the scalability of our solution in the production environment which has been deployed to three customers with more than 200 concurrent users.
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Notes
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Throughout the rest of the paper we use the word customer to denote the owner of the web application who decided to integrate the YesElf. Under the term user we understand the user of the customer’s application.
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Under the term context we understand the URL for which the context help is displayed, as each guide is situated at a specific URL.
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References
Ashwin, T., Jose, J., Raghu, G., Reddy, G.R.M.: An e-learning system with multifacial emotion recognition using supervised machine learning. In: 2015 IEEE 7th International Conference on Technology for Education (T4E), pp. 23–26. IEEE (2015)
Atterer, R., Wnuk, M., Schmidt, A.: Knowing the user’s every move: user activity tracking for website usability evaluation and implicit interaction. In: Proceedings of the 15th International Conference on World Wide Web, WWW 2006, New York, NY, USA, pp. 203–212. Association for Computing Machinery (2006). https://doi.org/10.1145/1135777.1135811
Bahreini, K., Nadolski, R., Westera, W.: Towards multimodal emotion recognition in e-learning environments. Interact. Learn. Environ. 24(3), 590–605 (2016). https://doi.org/10.1080/10494820.2014.908927
Bielikova, M., et al.: Eye-tracking en masse: Group user studies, lab infrastructure, and practices. J. Eye Mov. Res. 11(3) (2018). https://doi.org/10.16910/jemr.11.3.6
Chudá, D., Krátky, P., Burda, K.: Biometric properties of mouse interaction features on the Web. Interact. Comput. 30(5), 359–377 (2018). https://doi.org/10.1093/iwc/iwy015
Funk, M., Bächler, A., Bächler, L., Kosch, T., Heidenreich, T., Schmidt, A.: Working with augmented reality?: A long-term analysis of in-situ instructions at the assembly workplace. In: Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 222–229, PETRA 2017. ACM, New York (2017). https://doi.org/10.1145/3056540.3056548
Göschlberger, B., Bruck, P.A.: Gamification in mobile and workplace integrated microlearning. In: Proceedings of the 19th International Conference on Information Integration and Web-based Applications & Services, iiWAS 2017, pp. 545–552. ACM, New York (2017). https://doi.org/10.1145/3151759.3151795
Hewett, S., Becker, K., Bish, A.: Blended workplace learning: the value of human interaction. Educ.+ Train. 61(1), 2–16 (2019)
Hucko, M., et al.: YesELF: personalized onboarding for web applications. In: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, pp. 39–44. ACM (2019)
Kung-Keat, T., Ng, J.: Confused, bored, excited? An emotion based approach to the design of online learning systems. In: Fook, C.Y., Sidhu, G.K., Narasuman, S., Fong, L.L., Abdul Rahman, S.B. (eds.) 7th International Conference on University Learning and Teaching (InCULT 2014) Proceedings, pp. 221–233. Springer, Singapore (2016). https://doi.org/10.1007/978-981-287-664-5_19
Lin, F.R., Kao, C.M.: Mental effort detection using EEG data in e-learning contexts. Comput. Educ. 122, 63–79 (2018). https://doi.org/10.1016/j.compedu.2018.03.020
Minsky, M.: The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. Simon and Schuster (2007)
Nederveld, A., Berge, Z.L.: Flipped learning in the workplace. J. Workplace Learn. 27(2), 162–172 (2015)
Paxiuba, C.M., Calado, J., Lima, C.P., Sarraipa, J.: CADAP: a student’s emotion monitoring solution for e-learning performance analysis. In: 2018 International Conference on Intelligent Systems (IS), pp. 776–783, September 2018. https://doi.org/10.1109/IS.2018.8710542
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)
Pentel, A.: Employing think-aloud protocol to connect user emotions and mouse movements. In: 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–5. IEEE (2015)
Pentel, A.: Patterns of confusion: using mouse logs to predict user’s emotional state. In: UMAP Workshops (2015)
Picard, R.W.: Affective Computing. MIT Press (2000)
Qian, Y., Zhang, Y., Ma, X., Yu, H., Peng, L.: Ears: emotion-aware recommender system based on hybrid information fusion. Inf. Fusion 46, 141–146 (2019)
Santos, O.C.: Emotions and personality in adaptive e-Learning systems: an affective computing perspective. In: Tkalčič, M., De De Carolis, B., de de Gemmis, M., Odić, A., Košir, A. (eds.) Emotions and Personality in Personalized Services. HIS, pp. 263–285. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31413-6_13
Stefancova, E., Moro, R., Bielikova, M.: Towards detection of usability issues by measuring emotions. In: Benczúr, A., et al. (eds.) New Trends in Databases and Information Systems, pp. 63–70. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00063-9_8
Thai, N.T.T., De Wever, B., Valcke, M.: The impact of a flipped classroom design on learning performance in higher education: looking for the best “blend” of lectures and guiding questions with feedback. Comput. Educ. 107, 113–126 (2017)
Vigo, M., Harper, S.: Real-time detection of navigation problems on the world ‘wild’ web. Int. J. Hum.-Comput. Stud. 101, 1–9 (2017). https://doi.org/10.1016/j.ijhcs.2016.12.002
Acknowledgements
This work was partially supported by the Slovak Research and Development Agency under the contracts No. APVV-15-0508, APVV-17-0267 and by the Scientific Grant Agency of the Slovak Republic, grants No. VG 1/0667/18 and VG 1/0725/19. The authors would also like to thank the company Brainware for supporting their research.
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Hucko, M., Moro, R., Bielikova, M. (2020). Scalable Real-Time Confusion Detection for Personalized Onboarding Guides. In: Bielikova, M., Mikkonen, T., Pautasso, C. (eds) Web Engineering. ICWE 2020. Lecture Notes in Computer Science(), vol 12128. Springer, Cham. https://doi.org/10.1007/978-3-030-50578-3_18
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