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Identifying Students’ Group Work Problems: Design and Field Studies of a Supportive Peer Assessment

Abstract Group projects are part of the core educational experience in higher education, but many students report bad experiences with them, characterized by negative emotions and group problems. Group problems may undermine learning and cause stress and frustration. This may be prevented by monitoring and supporting groups, but this is often not feasible for teachers, who lack time and resources. This research aims to find a method for early identification of group work problems via computer-supported assessment. First, interviews and focus groups provided insights into the most common group problems and which visual features students preferred in a peer assessment. Next, two assessment versions were created: a simple, time-efficient version, and a more engaging, interactive one. We also created an initial version of E-Mate, a virtual agent that provides feedback on the assessment. These were tested in a field study. Most students reported a positive experience with the peer assessment, regardless of the version used. Teachers were also positive about its usefulness. Based on the feedback received, new features were added to the peer assessment survey and the E-Mate was redesigned; this new version was tested in a second field study. The results are in line with the previous field study and confirm the positive reception and usefulness of the survey, supporting the use of five attributes to evaluate group collaboration and the usage of a peer assessment survey to assess group work.

Exploring the Extent of Usability for Augmented Profile Interfaces in Enhancing Conversation Experiences

Abstract In this study, we investigated how to design a usable augmented reality (AR) profile conversation assistant focusing on how and which information leads to enhanced conversation experience and satisfaction. We drew on usability practices including user need interviews, information disposition sessions and an experiment comparing the conversation experiences between AR profile usage and non-usage. We provide insights into how to design a user interface that can enhance users' conversation experience and satisfaction compared to existing interfaces, especially in terms of type, quantity and placement of information on the AR profile. The three main design insights are to (1) limit the topics to personal information, recent events, preferences and hobbies; (2) use a text-based card format with emojis and make a clear distinction between preferred and not preferred topics through font size and placement difference; and (3) limit the number of information provision pages to less than four pages; however, we were not able to resolve the problem of the guilt and artificiality users feel in acquiring information about others from the AR profile. Thus, to resolve this problem, we suggest shifting our paradigm from a techno-solutionist perspective to breaking the illusion of the omnipotence of technology.

Talk or Text? The Role of Communication Modalities in the Adoption of a Non-directive, Goal-Attainment Coaching Chatbot

Abstract Despite the proliferation of chatbots (conversational agents) in increasingly varied contexts, user satisfaction with chatbot interactions remains a challenge. We do not yet fully understand chatbot usability and adoption factors or how to customize chatbots based on users' personality traits. One important and under researched aspect of chatbot design is users' perceptions of different communication modalities such as voice and text. In this between-group study (n = 393 participants), we asked participants to rate an equivalent text-based (n = 189) and voice-based (n = 204) non-directive, goal-attainment coaching chatbot in terms of usability, performance expectancy and risk perception. We also considered participants' personality in terms of extraversion. For usability across all participants, there was no difference between the chatbots for all participants; however, a higher rating of the voicebot was observed in the group classified as introverts and no difference was found for participants classified as extroverts. For performance expectancy all participants, extroverts and introverts rated the textbot higher. Risk ratings showed no difference between bots for all participants, extroverts and introverts. The results suggest that the voicebot was considered slightly easier to use for some participants while the textbot was considered to perform better by all participants. Creators of chatbots should consider using voice as a modality to attract users and text as a mode to accomplish complex tasks. Extraversion did not play a significant part in chatbot communication modality choice. These results may assist in designing context and audience-specific chatbots for increased efficacy and user satisfaction.

Open Access