Will mobile video become the killer application for 3G mobile Internet? a model of media convergence acceptance

We propose and empirically test a model of user acceptance of digital media convergence. Integrating information system research, particularly the paradigm of task–technology fit and the platform–application dichotomy, and theories from media psychology and human–computer interaction, we theorize the fit between the content applications and the media platform as the key determinant of user evaluation and acceptance of digital media convergence. We empirically tested our theory in the context of mobile video entertainment services. Specifically, we hypothesize that both the duration of the video content (i.e., the characteristic of the content) and the attentional constraint of the mobile media platform (i.e., the characteristic of the media platform) jointly determine users’ attentional involvement (i.e., reflecting the fit) in the video content. Such involvement in turn influences users’ emotional enjoyment and satisfaction with the mobile video entertainment service. The empirical results support most of our hypotheses. Two key findings are: (1) in the case of media convergence, the attentional constraint of the target media platform significantly influences user experience during content delivery that may lead to the rejection of media convergence; and (2) the duration of the content interacts with both the attentional constraint of the media platform and the type of interruptions from the environment in determining attentional involvement and emotional enjoyment. We discuss the theoretical contributions and practical implications from our empirical findings.     

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighborhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue.     

Dynamic testing: Can a robot as tutor be of help in assessing children's potential for learning?

This study examined whether computerized dynamic testing by utilizing a robot would lead to different patterns in children's (aged 6–9 years) potential for learning and strategy use when solving series‐completion tasks. The robot, in a “Wizard of Oz” setting, provided instructions and prompts during dynamic testing. It was found that a dynamic training resulted in greater accuracy and more correctly placed pieces at the post‐test than repeated testing only. Moreover, children who were dynamically trained appeared to use more heuristic strategies at the post‐test than their peers who were not trained. In general, observations showed that children were excited to work with the robot. All in all, the study revealed that computerized dynamic testing by means of a robot has much potential in tapping into children's potential for learning and strategy use. The implications of using a robot in educational assessment were stressed further in the discussion.     

Integration of Usability Evaluation Studies via a Novel Meta-Analytic Approach: What are Significant Attributes for Effective Evaluation?

The overall discovery rates, which are the ratios of sum of unique usability problems detected by all experiment participants against the number of usability problems existed in the evaluated systems, were investigated to find significant factors of usability evaluation through a meta-analytic approach with the n-corrected effect sizes newly defined in this study. Since many studies of usability evaluation have been conducted under specific contexts showing some mixed findings, usability practitioners need holistic and more generalized conclusions. Due to the limited applicability of the traditional meta-analysis to usability evaluation studies, a new meta-analytic approach was established and applied to 38 experiments that reported overall discovery rates of usability problems as a criterion measure. Through the meta-analytic approach with the n-corrected effect sizes, we successfully combined 38 experiments and found evaluator's expertise, report type, and interaction between usability evaluation method and time constraint as significant factors. We suggest that in order to increase overall discovery rates of usability problems, (a) free-style written reports are better than structured written reports; (b) when heuristic evaluation or cognitive walkthrough is used, the usability evaluation experiments should be conducted without time constraint, but when think-aloud is used, time constraint is not an important experimental condition; (c) usability practitioners do not need to be concerned about unit of evaluation, fidelity of evaluated systems, and task type; and (d) HCI experts are better than novice users or evaluators. Our conclusions can guide usability practitioners when determining evaluation contexts, and the meta-analytic approach of this study provides an alternative way to combine the empirical results of usability evaluation besides the traditional meta-analysis.