Development of an Interactive Educational Tool to Experience Machine Learning with Image Classification

Sasaki, Yuji; Fukui, Masanori; Hagikura, Jo; Moriyama, Jun; Hirashima, Tsukasa

doi:10.1109/gcce50665.2020.9291944

Cited by 1 publication

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“…These are works that propose educational tools using machine learning, for example [26]. In addition, the study by [27], calculates this number in linear time to find a number that summarizes all interactions between an object and a user.…”

Section: F Eligibilitymentioning

confidence: 99%

User Experience Design Using Machine Learning: A Systematic Review

2022

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User experience (UX) is the key to increased productivity by enhancing the usability and interactivity of the product. Machine learning (ML) solutions have raised user and academic awareness of technical innovation. As a result, ML is becoming increasingly popular to improve the quality of UX. Several investigations have highlighted a potential lack of studies on the overall challenges and recommendations for UX using ML. Therefore, more attention should be paid to ML's existence and potential applications across various applications to get the most out of ML techniques to improve the UX design process. To this objective, a systematic review of the literature was performed as to determine the challenges faced by UX designers when incorporating ML in their design process. Recommendations that help UX designers incorporate ML into UX design will be highlighted. Furthermore, the PRISMA approach is used (a process that has been established in the literature), to restrict the chance of bias at the selection stage. Relevant articles in the following four databases were searched: IEEE Xplore, Scopus, Web of Science, and ACM. The findings revealed that the number of publications on issues linked to UX with ML had advanced exponentially. This review highlights the challenges, recommendations, tools, algorithms, techniques and datasets used in different studies. In addition, suggestions are given for future investigations.

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Section: F Eligibilitymentioning

confidence: 99%