Data-Seeking Behaviour in the Social Sciences

Purpose: Publishing research data for reuse has become good practice in recent years. However, not much is known on how researchers actually find said data. In this exploratory study, we observe the information-seeking behaviour of social scientists searching for research data to reveal impediments and identify opportunities for data search infrastructure. Methods: We asked 12 participants to search for research data and observed them in their natural environment. The sessions were recorded. Afterwards, we conducted semi-structured interviews to get a thorough understanding of their way of searching. From the recordings, we extracted the interaction behaviour of the participants and analysed the spoken words both during the search task and the interview by creating affinity diagrams. Results: We found that literature search is more closely intertwined with dataset search than previous literature suggests. Both the search itself and the relevance assessment are very complex, and many different strategies are employed, including the creatively "misuse" of existing tools, since no appropriate tools exist or are unknown to the participants. Conclusion: Many of the issues we found relate directly or indirectly to the application of the FAIR principles, but some, like a greater need for dataset search literacy, go beyond that. Both infrastructure and tools offered for dataset search could be tailored more tightly to the observed work processes, particularly by offering more interconnectivity between datasets, literature, and other relevant materials

Know What Not To Know: Users' Perception of Abstaining Classifiers

Machine learning systems can help humans to make decisions by providing decision suggestions (i.e., a label for a datapoint). However, individual datapoints do not always provide enough clear evidence to make confident suggestions. Although methods exist that enable systems to identify those datapoints and subsequently abstain from suggesting a label, it remains unclear how users would react to such system behavior. This paper presents first findings from a user study on systems that do or do not abstain from labeling ambiguous datapoints. Our results show that label suggestions on ambiguous datapoints bear a high risk of unconsciously influencing the users' decisions, even toward incorrect ones. Furthermore, participants perceived a system that abstains from labeling uncertain datapoints as equally competent and trustworthy as a system that delivers label suggestions for all datapoints. Consequently, if abstaining does not impair a system's credibility, it can be a useful mechanism to increase decision quality

Ah, Alright, Okay! Communicating Understanding in Conversational Product Search

When talking about products, people often express their needs in vague terms with vocabulary that does not necessarily overlap with product descriptions written by retailers. This poses a problem for chatbots in online shops, as the vagueness and vocabulary mismatch can lead to misunderstandings. In human-human communication, people intuitively build a common understanding throughout a conversation, e.g., via feedback loops. To inform the design of conversational product search systems, we investigated the effect of different feedback behaviors on users’ perception of a chatbot’s competence and conversational engagement. Our results show that rephrasing the user’s input to express what was understood increases conversational engagement and gives the impression of a competent chatbot. Using a generic feedback acknowledgment (e.g., “right” or “okay”), however, does not increase engagement or perceived competence. Auto-feedback for conversational product search systems therefore needs to be designed with care

“Mhm...” – Conversational Strategies For Product Search Assistants

Online retail has become a popular alternative to in-store shopping. However, unlike in traditional stores, users of online shops need to find the right product on their own without support from expert salespersons. Conversational search could provide a means to compensate for the shortcomings of traditional product search engines. To establish design guidelines for such virtual product search assistants, we studied conversations in a user study (N = 24) where experts supported users in finding the right product for their needs. We annotated the conversations concerning their content and conversational structure and identified recurring conversational strategies. Our findings show that experts actively elicit the users’ information needs using funneling techniques. They also use dialogue-structuring elements and frequently confirm having understood what the client was saying by using discourse markers, e.g., “mhm”. With this work, we contribute insights and design implications for conversational product search assistants

Design challenges for long-term interaction with a robot in a science classroom

This paper aims to present the main challenges that emerged during the process of the research design of a longitudinal study on child-robot interaction for science education and to discuss relevant suggestions in the context. The theoretical rationale is based on aspects of the theory of social constructivism and we use the collaborative inquiry as a framework to examine children's learning process who interact with a robotic learning companion. We identify two main challenges; (i) the development of robust on-demand systems for long-term interaction; and (ii) the design of developmentally appropriate scaffolding in embodied, semi-structured learning tasks. To address these challenges, we suggest (i) the development of a system for the detection of child's intention for interaction in the context of a classroom and (ii) the design of sensorized learning materials for the support of developmentally appropriate embodied learning experience