Over the past decade, the use of emojis in social media has seen a rapid increase.Despite their popularity and image-grounded nature, previous studies have found that people interpret emojis inconsistently when presented in context and in isolation.In this work, we explore whether emoji semantics differ across languages and how semantics interacts with sentiment in emoji use across languages.To do so, we developed a corpus containing the literal meanings for a set of emojis, as defined by L1 speakers in English, Portuguese and Chinese.We then use these definitions to assess whether speakers of different languages agree on whether an emoji is being used literally or figuratively in the context where they are grounded in, as well as whether this literal and figurative use correlates with the sentiment of the context itself.We found that there were varying levels of disagreement on the definition for each emoji but that these stayed fairly consistent across languages.We also demonstrated a correlation between the sentiment of a tweet and the figurative use of an emoji, providing theoretical underpinnings for empirical results in NLP tasks, particularly offering insights that can benefit sentiment analysis models.

This full research paper examines the effect of scaffolding on students” engagement with engineering judgment while solving an open-ended modeling problem (OEMP) during an introductory statics course. While traditional engineering coursework involves well-structured homework problems, research shows that open-ended design problems offer opportunities for students to develop fluency in problem solving and practice the use of engineering judgment. However, the transition from traditional, well-structured problems to OEMPs can be difficult for students, highlighting the critical role of scaffolding in introductory OEMP design [1]. In this paper, we interviewed the professor who created and assigned the OEMP to understand the goals of the scaffolding. Next, we analyzed the OEMP written assignment for scaffolding that elicits the use of engineering judgment and statics concepts. Using both data sources, the professors goals and assignment analysis, we coded a video transcript of collaborative work sessions from a group of students as they worked through the OEMP for engineering judgment and identified if students used the correct static concepts in their discussion. The implications of the paper revolve around identifying the level and type of scaffolding that initiates engagement in engineering judgment and statics course material, as well as strategies for improving scaffolding of open-ended problems. These insights aim to assist educators by highlighting effective scaffolding techniques that better align professors goals with the student takeaways.