Computer codes are widely used to describe physical processes in lieu of physical observations. In some cases, more than one computer simulator, each with different degrees of fidelity, can be used to explore the physical system. In this work, we combine field observations and model runs from deterministic multi-fidelity computer simulators to build a predictive model for the real process. The resulting model can be used to perform sensitivity analysis for the system, solve inverse problems and make predictions. Our approach is Bayesian and will be illustrated through a simple example, as well as a real application in predictive science at the Center for Radiative Shock Hydrodynamics at the University of Michigan.KEY WORDS: Computer Experiment; Gaussian process; Markov Chain Monte Carlo. arXiv:1208.2716v1 [stat.AP] 13 Aug 2012 occur, for example, because of the presence of reduced order physics in lower fidelity models, different levels of accuracy specified for numerical solvers or solutions obtained on finer grids. In these cases, a higher fidelity model is thought to better represent the physical process than a lower fidelity model, but also takes more computer time to produce an output than a lower fidelity model. So, combining relatively cheap lower fidelity model runs with more costly high fidelity runs to emulate the high fidelity model has been an significant problem of interest (Kennedy and O'Hagan, 2000;Qian et al., 2006 and.Another important application of computer models is that of calibration (e.g., Kennedy and O'Hagan, 2001;Higdon et al., 2004) where the aim is to combine simulator outputs with physical observations to build a predictive model and also estimate unknown parameters that govern the behaviour of the computer model. The latter endeavour amounts to solving a sort of inverse problem, while the former activity is a type of regression problem.Motivated by applications at the Center for Radiative Shock Hydrodynamics (CRASH) at the University of Michigan, the aim of this work is to develop new methodology to combine outputs from simulators with different levels of fidelity and field observations to make predictions of the physical system with associated measurements of uncertainty. In the spirit similar to Kennedy and O'Hagan (2000 and2001) and Higdon et al. (2004), we propose a predictive model that incorporates computer model outputs and field data, while attempting to find optimal values for some input parameters (i.e. calibration parameters). Different models are specified for each source of data (Kennedy and O'Hagan, 2000;Qian et al., 2006 and. The approach calibrates each computer model to the next highest level of fidelity model, and the simulator of the highest fidelity is then calibrated to the field measurements. All the response surfaces are Gaussian process (GP) models and the various sources of information that inform predictions of the physical system are combined with a Bayesian hierarchical model. The paper is organized as follows: In section 2, we will introduce the prop...

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 the personal characteristics of the worker and the nature of the task.

No abstract

Purpose The purpose of this paper is to report on research findings from a teaching and learning intervention that explored whether undergraduate university students can be taught to articulate their employability skills effectively to prospective employers and to retain this ability post-course. Design/methodology/approach The study included 3,400 students in 44 courses at a large Canadian university. Stage 1 involved a course-level teaching and learning intervention with the experimental student group, which received employability skills articulation instruction. Stage 2 involved an online survey administered six months post-course to the experimental group and the control group. Both groups responded to two randomly generated questions using the Situation/Task, Actions, Result (STAR) format, a format that employers commonly rely on to assess job candidates’ employability skills. The researchers compared the survey responses from the experimental and control groups. Findings Survey results demonstrate that previous exposure to the STAR format was the only significant factor affecting students’ skills articulation ability. Year of study and program (co-operative or non-co-operative) did not influence articulation. Practical implications The findings suggest that universities should integrate institution-wide, course-level employability skills articulation assignments for students in all years of study and programs (co-op and non-co-op). Originality/value This research is novel because its study design combines practical, instructional design with empirical research of significant scope (institution-wide) and participant size (3,400 students), contributing quantitative evidence to the employability skills articulation discussion. By surveying students six months post-course, the study captures whether articulation instruction can be recalled, an ability of particular relevance for career preparedness.

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. CCS CONCEPTS• Human-centered computing → Empirical studies in interaction design.

No abstract

No abstract

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.