Joslin Goh

Jessy Ceha, Ken Jen Lee, Elizabeth Nilsen et al.

Previous studies have highlighted the benefits of pedagogical conversational agents using socially-oriented conversation with students. In this work, we examine the effects of a conversational agent's use of affiliative and self-defeating humour -- considered conducive to social well-being and enhancing interpersonal relationships -- on learners' perception of the agent and attitudes towards the task. Using a between-subjects protocol, 58 participants taught a conversational agent about rock classification using a learning-by-teaching platform, the Curiosity Notebook. While all agents were curious and enthusiastic, the style of humour was manipulated such that the agent either expressed an affiliative style, a self-defeating style, or no humour. Results demonstrate that affiliative humour can significantly increase motivation and effort, while self-defeating humour, although enhancing effort, negatively impacts enjoyment. Findings further highlight the importance of understanding learner characteristics when using humour.

Ken Jen Lee, Apoorva Chauhan, Joslin Goh et al.

While learning by teaching is a popular pedagogical technique, it is a learning phenomenon that is difficult to study due to variability in the tutor-tutee pairings and learning environments. In this paper, we introduce the Curiosity Notebook, a web-based research infrastructure for studying learning by teaching via the use of a teachable agent. We describe and provide rationale for the set of features that are essential for such a research infrastructure, outline how these features have evolved over two design iterations of the Curiosity Notebook and through two studies -- a 4-week field study with 12 elementary school students interacting with a NAO robot and an hour-long online observational study with 41 university students interacting with an agent -- demonstrate the utility of our platform for making observations of learning-by-teaching phenomena in diverse learning environments. Based on these findings, we conclude the paper by reflecting on our design evolution and envisioning future iterations of the Curiosity Notebook.

Nalin Chhibber, Rohail Syed, Mengqiu Teng et al.

Understanding how to engage users is a critical question in many applications. Previous research has shown that unexpected or astonishing events can attract user attention, leading to positive outcomes such as engagement and learning. In this work, we investigate the similarity and differences in how people and algorithms rank the surprisingness of facts. Our crowdsourcing study, involving 106 participants, shows that computational models of surprise can be used to artificially induce surprise in humans.