Much HCI research on prompting prosocial behaviors focuses on methods for increasing empathy. However, increased empathy may have unintended negative consequences. Our work offers an alternative solution that encourages critical reflection for nurturing compassion, which involves motivation and action to help others. In a between-subject experiment, participants (N=60) viewed a climate change documentary while receiving no prompts (CON), reflective prompts to focus on their emotions (RE) or surprises (RS). State compassion, critical reflection, and motivation to act or learn were measured at the end of the session (post-video) and two weeks later (follow-up). Despite participants’ condition not affecting compassion, critical reflection was positively correlated with post-video state compassion. RE and RS participants demonstrated deeper reflection and reported higher motivation to learn post-video, and more prosocial behavioral changes during follow-up. RS participants reported better follow-up recall than RE participants. We conclude by discussing implications on designing technology to support compassion and longer-term critical reflection.

Traditional crowdsourcing has mostly been viewed as requester-worker interaction where requesters publish tasks to solicit input from human crowdworkers. While most of this research area is catered towards the interest of requesters, we view this workflow as a teacher-learner interaction scenario where one or more human-teachers solve Human Intelligence Tasks to train machine learners. In this work, we explore how teachable machine learners can impact their human-teachers, and whether they form a trustable relation that can be relied upon for task delegation in the context of crowdsourcing. Specifically, we focus our work on teachable agents that learn to classify news articles while also guiding the teaching process through conversational interventions. In a two-part study, where several crowd workers individually teach the agent, we investigate whether this learning by teaching approach benefits human-machine collaboration, and whether it leads to trustworthy AI agents that crowd workers would delegate tasks to. Results demonstrate the benefits of the learning by teaching approach, in terms of perceived usefulness for crowdworkers, and the dynamics of trust built through the teacher-learner interaction.

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.

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.

Snow-depth retrieval from passive microwave observations without a priori information is a highly undetermined problem. Achieving accurate snow-depth retrievals requires a priori information on the snowpack properties, such as grain size, density, physical temperature, and stratigraphy. On a practical level, however, retrieval algorithms must consider prior information, while minimizing the dependence on it, as accurate ancillary data are not globally available. In this study, we build on the previously published Bayesian Algorithm for Snow Water Equivalent Estimation (BASE) to retrieve snow depth using an airborne passive microwave data set over the tundra snow in the Eureka region. The method computes the optimal estimates of snow depth, density, grain size, and other variables, given the brightness temperature observations and prior information, using Markov chain Monte Carlo (MCMC). The airborne data set includes passive microwave brightness temperature (Tb) at 18.7 and 36.5 GHz. The in situ measurements of the snow depth provide validation data for 464 sensor footprints. The microwave radiative transfer (RT) model used is the Dense Media RT-Multilayered (DMRT-ML) model. We use a two-layer wind slab and depth hoar assumption based on the local snow cover knowledge from the previous research on the study area. To improve our understanding of the results using the airborne Tbs, the inversion was also applied using the synthetic observations, where Tbs were generated from the RT model. For the case with synthetic observations, the snow-depth RMSE was 0.07 cm. When the airborne Tbs are used, the snow-depth RMSE was 21.8 cm. This discrepancy is due to the large spatial variability in the MagnaProbe snow-depth measurements and the fact that not all physical processes affecting the airborne Tbs are represented in the RT model. Our work verifies the feasibility and applicability of the proposed methodology regionally for the airborne retrievals and reinforces the tractable applicability of a physics-based RT model in the SWE retrievals.

Curiosity-the intrinsic desire for new information-can enhance learning, memory, and exploration. Therefore, understanding how to elicit curiosity can inform the design of educational technologies. In this work, we investigate how a social peer robot's verbal expression of curiosity is perceived, whether it can affect the emotional feeling and behavioural expression of curiosity in students, and how it impacts learning. In a between-subjects experiment, 30 participants played the game LinkIt!, a game we designed for teaching rock classification, with a robot verbally expressing: curiosity, curiosity plus rationale, or no curiosity. Results indicate that participants could recognize the robot's curiosity and that curious robots produced both emotional and behavioural curiosity contagion effects in participants.

Collaborative crowdsourcing tasks allow crowd workers to solve problems that they could not handle alone, but worker motivation in these tasks is not well understood. In this paper, we study how to motivate groups of workers by paying them equitably. To this end, we characterize existing collaborative tasks based on the types of information available to crowd workers. Then, we apply concepts from equity theory to show how fair payments relate to worker motivation, and we propose two theoretically grounded classes of fair payments. Finally, we run two experiments using an audio transcription task on Amazon Mechanical Turk to understand how workers perceive these payments. Our results show that workers recognize fair and unfair payment divisions, but are biased toward payments that reward them more. Additionally, our data suggests that fair payments could lead to a small increase in worker effort. These results inform the design of future collaborative crowdsourcing tasks.

Listening to heart sounds is an important first step in evaluating the cardiovascular system and is important in the early detection of cardiovascular disease. We present and evaluate a framework for combining machine learning algorithms, crowd workers, and experts in the classification of heart sound recordings. The development of a hybrid human-machine framework is motivated by the past success in utilizing human computation to solve problems in medicine and the use of human-machine frameworks in other domains. We describe the methods that decide when and how to escalate the analysis of heart sounds to different resources and incorporate their decision into a final classification. Our framework was tested with a combination of machine classifiers and crowd workers from Amazon's Mechanical Turk. The results indicate a hybrid approach achieves greater performance than a baseline classifier alone, utilizing less expert resources while achieving similar performance, compared to a framework without the crowd.

Crowdsourced classification of data typically assumes that objects can be unambiguously classified into categories. In practice, many classification tasks are ambiguous due to various forms of disagreement. Prior work shows that exchanging verbal justifications can significantly improve answer accuracy over aggregation techniques. In this work, we study how worker deliberation affects resolvability and accuracy using case studies with both an objective and a subjective task. Results show that case resolvability depends on various factors, including the level and reasons for the initial disagreement, as well as the amount and quality of deliberation activities. Our work reinforces the finding that deliberation can increase answer accuracy and the importance of verbal discussion in this process. We contribute a new public data set on worker deliberation for text classification tasks, and discuss considerations for the design of deliberation workflows for classification.

Consistency is a practical metric that evaluates an instrument's reliability based on its ability to yield the same output when repeatedly given a particular input. Despite its broad usage, little is understood about the feasibility of using consistency as a measure of worker reliability in crowdwork. In this paper, we explore the viability of measuring a worker's reliability by their ability to conform to themselves. We introduce and describe Deja Vu, a mechanism for dynamically generating task queues with consistency probes to measure the consistency of workers who repeat the same task twice. We present a study that utilizes Deja Vu to examine how generic characteristics of the duplicate task - such as placement, difficulty, and transformation - affect a worker’s task consistency in the context of two unique object detection tasks. Our findings provide insight into the design and use of consistency-based reliability metrics.

Service robots are becoming a widespread tool for assisting humans in scientific, industrial and even domestic settings. Yet, our understanding of how to motivate and sustain interactions between human users and robots remains limited. In this work, we conducted a study to investigate how surprising robot behaviour evokes curiosity and influences trust and engagement in the context of participants interacting with Recyclo, a service robot for providing recycling recommendations. In a Wizard-of-Oz experiment, 36 participants were asked to interact with Recyclo to recognize and sort a variety of objects, and were given object recognition responses that were either unsurprising or surprising. Results show that surprise gave rise to information seeking behavior indicative of curiosity, while having a positive influence on engagement and negative influence on trust.

Crowdsourcing systems are designed to elicit help from humans to accomplish tasks that are still difficult for computers. How to motivate workers to stay longer and/or perform better in crowdsourcing systems is a critical question for designers. Previous work have explored different motivational frameworks, both extrinsic and intrinsic. In this work, we examine the potential for curiosity as a new type of intrinsic motivational driver to incentivize crowd workers. We design crowdsourcing task interfaces that explicitly incorporate mechanisms to induce curiosity and conduct a set of experiments on Amazon's Mechanical Turk. Our experiment results show that curiosity interventions improve worker retention without degrading performance, and the magnitude of the effects are influenced by both personal characteristics of the worker and the nature of the task.