Suggestive answers strategy in human-chatbot interaction: a route to engaged critical decision making

In this study, we proposed a novel chatbot interaction strategy based on the suggestive ending of answers. This strategy is inspired by the cliffhanger ending narrative technique, which ends a story without specifying conclusions to spark readers' curiosity as to what will happen next and is often used in television series. Common chatbots provide relevant and comprehensive answers to users' questions. In contrast, chatbots with our proposed strategy end their answers with hints potentially interest-triggering users. The suggestive ending strategy aims to stimulate users' inquisition for critical decision-making, relating to a psychological phenomenon where humans are often urged to finish the uncompleted tasks they have initiated. We demonstrated the implication of our strategy by conducting an online user study involving 300 participants, where they used chatbots to perform three decision-making tasks. We adopted a between-subjects factorial experimental design and compared between the following UIs: (1) plain chatbot-it provides a generated answer when participants issue a question; (2) expositive chatbot-it provides a generated answer for a question, adding short summaries of a positive and negative person's opinion for the answer; (3) suggestive chatbot-it provides a generated answer for a question, which ends with a suggestion of a positive and negative person for the answer. We found that users of the suggestive chatbot were inclined to ask more questions to the bot, engage in prolonged decision-making and information-seeking actions, and formulate their opinions from various perspectives. These findings vary with the users' experience with plain and expositive chatbots.

The effects of topic familiarity on college students' learning search process

Purpose

This study aims to explore the influence of topic familiarity on the four stages of college students' learning search process.

Design/methodology/approach

This study clarified the effects of topic familiarity on students' learning search process by conducting a simulation experiment based on query formulation, information item selection, information sources and learning output.

Findings

The results characterized users' interaction behaviors in increasing topic familiarity through their use of more task descriptions as queries, increased reformulation of queries, construction of more purposeful query formulation, reduced attention to a topic's basic concept content and increased exploration of academic platform contents.

Originality/value

This study proposed three innovative indicators which were proposed to evaluate the effects of topic familiarity on college students' learning search process, and the adopted metrics were useful for observing differences in college students' learning output as their topic familiarity increased. It contributes to the understanding of a user's search process and learning output to support the optimization function of learning-related information search systems and improve their effect on the user's search process for learning.

Process patterns and conceptual changes in knowledge representations during information seeking and sensemaking: A qualitative user study

The construction of knowledge representations during sensemaking resembles meaningful learning in which conceptual changes to knowledge structure take place in various forms. Guided by a cognitive process model of sensemaking expanding prior models with ideas from learning and cognitive psychology, we conducted a qualitative user study of 15 participants with news writing and business analysis tasks to investigate the evolvement of their knowledge structures. We collected and analysed think-aloud protocols along with recorded screen activities, intermediate work products including notes and concept maps, and the final reports. Findings suggested that: (a) the sensemaking process can be viewed as composed of several iterations that fall into nine slightly varied common patterns, which make up the components of sensemaking; (b) conceptual changes fall into three broad classes – accretion, tuning and restructuring; and (c) changes in forms of representation seem to assist in sensemaking. These findings provide insights for system design that assists in sensemaking and intelligent use of information.

Searching as learning: A systematization based on literature

The paper surveys empirical studies on the relations between information searching and learning, and presents some reflections about learning in a search process based on the findings. First, the meaning of the concepts ‘learning’ and ‘searching’ is briefly defined. Learning is conceptualized as changes in one’s knowledge structures. Then it is described more in detail how learning occurs in the search process. The point of departure is to focus on tasks that require the restructuring of knowledge structures and to analyse how gradual stabilization of those structures is related to accessing and interacting with information sources. After that, empirical studies on searching and learning are categorized by identifying independent and dependent variables in those studies. In conclusion, some general remarks on the topic are presented.

Towards searching as a learning process: A review of current perspectives and future directions

We critically review literature on the association between searching and learning and contribute to the formulation of a research agenda for searching as learning. The paper begins by reviewing current literature that tends to characterize search systems as tools for learning. We then present a perspective on searching as learning that focuses on the learning that occurs during the search process, as well as search outputs and learning outcomes. The concept of ‘comprehensive search’ is proposed to describe iterative, reflective and integrative search sessions that facilitate critical and creative learning beyond receptive learning. We also discuss how search interaction data can provide a rich source of implicit and explicit features through which to assess search-related learning. In conclusion, we summarize opportunities and challenges for future research with respect to four agendas: developing a search system that supports sense-making and enhances learning; supporting effective user interaction for searching as learning; providing an inquiry-based literacy tool within a search system; and assessing learning from online searching behaviour.