Recent trends in computer-mediated communication (CMC) have not only led to expanded instant messaging through the use of images and videos but have also expanded traditional text messaging with richer content in the form of visual communication markers (VCMs) such as emoticons, emojis, and stickers. VCMs could prevent a potential loss of subtle emotional conversation in CMC, which is delivered by nonverbal cues that convey affective and emotional information. However, as the number of VCMs grows in the selection set, the problem of VCM entry needs to be addressed. Furthermore, conventional means of accessing VCMs continue to rely on input entry methods that are not directly and intimately tied to expressive nonverbal cues. In this work, we aim to address this issue by facilitating the use of an alternative form of VCM entry: hand gestures. To that end, we propose a user-defined hand gesture set that is highly representative of a number of VCMs and a two-stage hand gesture recognition system (trajectory-based, shape-based) that can identify these user-defined hand gestures with an accuracy of 82%. By developing such a system, we aim to allow people using low-bandwidth forms of CMCs to still enjoy their convenient and discreet properties while also allowing them to experience more of the intimacy and expressiveness of higher-bandwidth online communication.
Kanji script writing is a skill that is often introduced to novice Japanese foreign language students for achieving Japanese writing mastery, but often poses difficulties to students with primarily English fluency due to their its vast differences with written English. Instructors often introduce various pedagogical methods—such as visual structure and written techniques—to assist students in kanji study, but may lack availability providing direct feedback on students' writing outside of class. Current educational applications are also limited due to lacking richer instructor-emulated feedback. We introduce Kanji Workbook, a writing-based intelligent tutoring system for students to receive intelligent assessment that emulates human instructor feedback. Our interface not only leverages students' computing devices for allowing them to learn, practice, and review the writing of prompted characters from their course's kanji script lessons, but also provides a diverse set of writing assessment metrics—derived from instructor interviews and classroom observation insights—through intelligent scoring and visual animations. We deployed our interface onto novice- and intermediate-level university courses over an entire academic year, and observed that interface users on average achieved higher course grades than their peers and also reacted positively to our interface's various features.
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