As emojis are increasingly used in everyday online communication such as messaging, email, and social networks, various techniques have attempted to improve the user experience in communicating emotions and information through emojis. Emoji recommendation is one such example in which machine learning is applied to predict which emojis the user is about to select, based on the user’s current input message. Although emoji suggestion helps users identify and select the right emoji among a plethora of emojis, analyzing only a single sentence for this purpose has several limitations. First, various emotions, information, and contexts that emerge in a flow of conversation could be missed by simply looking at the most recent sentence. Second, it cannot suggest emojis for emoji-only messages, where the users use only emojis without any text. To overcome these issues, we present Reeboc (Recommending emojis based on context), which combines machine learning and k -means clustering to analyze the conversation of a chat, extract different emotions or topics of the conversation, and recommend emojis that represent various contexts to the user. To evaluate the effectiveness of our proposed emoji recommendation system and understand its effects on user experience, we performed a user study with 17 participants in eight groups in a realistic mobile chat environment with three different modes: (i) a default static layout without emoji recommendations, (ii) emoji recommendation based on the current single sentence, and (iii) our emoji recommendation model that considers the conversation. Participants spent the least amount of time in identifying and selecting the emojis of their choice with Reeboc (38% faster than the baseline). They also chose emojis that were more highly ranked with Reeboc than with current-sentence-only recommendations. Moreover, participants appreciated emoji recommendations for emoji-only messages, which contributed to 36.2% of all sentences containing emojis.
One-to-many coaching is a common, yet difficult, coaching technique used in environments with many novices learning to solve ill-defined problems. Intelligent systems might be designed to support 1-to-many coaching but designing such systems requires a 1-to-many coaching model that details novices' challenges, coaches' strategies, and coaches' goals. To build such a model, we conducted interaction analysis on 24 1-to-many coaching sessions with novices developing new products in a university incubator and conducted retrospective analyses with 3 coaches and 30 novices. We contribute a model that demonstrates that coaches in a 1-to-many setting not only need to help novices develop metacognitive skills (just as in 1-to-1 coaching), but also need to utilize the presence and expertise of a group of novices to learn from each other, to mitigate their fear of failures, and provide them accountability. Our model informs design implications for future intelligent coaching systems to (1) assist coaches in monitoring and comparing many novices' progress, learning, and expertise; (2) provide novices with checklists, templates, and scaffolds to help them self-evaluate, seek-help, and summarize learning; (3) showcase failures and growth; and (4) publicize planning and progress to provide accountability.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.