Enhancing Information Language Learning with Mobile Technology - Does it Work?

There are many theories that attempt to explain second language acquisition processes and factors determining success or failure. Despite a lack of general agreement between proponents of these theories, research has convincingly shown that the amount of exposure to target language input is one important predictor of ultimate achievement levels. ‘Time on task’ is as important in language learning as it is in many other domains (cf. Reber, 1993) and it is therefore important to identify ways in which this can be increased. An obvious possibility is to encourage learners to engage with (and in) the language outside the classroom. Informal learning, in the sense of learning outside of formal education, has been shown to be a major aspect of adult learning (Cross, 2007) and, given appropriate preparation and support, learners can greatly increase opportunities for learning if they can do so independently. Mobile technologies have obvious potential in this regard. However, is it possible to improve language skills in this way? In this article we report on an exploratory study into the use of cellphones for extensive listening practice. We used input enhancement to draw learners’ attention to not only the meaning of the materials but also the formal (grammatical) aspects of the input. We found that the use of mobile technology presented a number of challenges and in this study did not result in learners acquiring the target structures. We conclude with a number of recommendations for the use and future study of mobile technologies for (language) learning

Should I Stay or Should I Go?: East Asian international students' decision making processes about their migration intentions post-graduation

Honors (Bachelor's)SociologyUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/98882/1/ymcho.pd

Online home appliance control using EEG-Based brain-computer interfaces

Brain???computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an online BCI based on scalp electroencephalography (EEG) to control home appliances. The BCI users controlled TV channels, a digital door-lock system, and an electric light system in an unshielded environment. The BCI was designed to harness P300 andN200 components of event-related potentials (ERPs). On average, the BCI users could control TV channels with an accuracy of 83.0% ?? 17.9%, the digital door-lock with 78.7% ?? 16.2% accuracy, and the light with 80.0% ?? 15.6% accuracy, respectively. Our study demonstrates a feasibility to control multiple home appliances using EEG-based BCIs

Convergence of an iterative algorithm for systems of variational inequalities and nonexpansive mappings with applications

AbstractIn this paper, we consider the problem of convergence of an iterative algorithm for a system of generalized variational inequalities and a nonexpansive mapping. Strong convergence theorems are established in the framework of real Banach spaces

Study of electron trapping by a transversely ellipsoidal bubble in the laser wake-field acceleration

We present electron trapping in an ellipsoidal bubble which is not well explained by the spherical bubble model by [Kostyukov, Phys. Rev. Lett. 103, 175003 (2009)]. The formation of an ellipsoidal bubble, which is elongated transversely, frequently occurs when the spot size of the laser pulse is large compared to the plasma wavelength. First, we introduce the relation between the bubble size and the field slope inside the bubble in longitudinal and transverse directions. Then, we provide an ellipsoidal model of the bubble potential and investigate the electron trapping condition by numerical integration of the equations of motion. We found that the ellipsoidal model gives a significantly less restrictive trapping condition than that of the spherical bubble model. The trapping condition is compared with three-dimensional particle-in-cell simulations and the electron trajectory in test potential simulations.open1

Functional clustering methods for binary longitudinal data with temporal heterogeneity

In the analysis of binary longitudinal data, it is of interest to model a dynamic relationship between a response and covariates as a function of time, while also investigating similar patterns of time-dependent interactions. We present a novel generalized varying-coefficient model that accounts for within-subject variability and simultaneously clusters varying-coefficient functions, without restricting the number of clusters nor overfitting the data. In the analysis of a heterogeneous series of binary data, the model extracts population-level fixed effects, cluster-level varying effects, and subject-level random effects. Various simulation studies show the validity and utility of the proposed method to correctly specify cluster-specific varying-coefficients when the number of clusters is unknown. The proposed method is applied to a heterogeneous series of binary data in the German Socioeconomic Panel (GSOEP) study, where we identify three major clusters demonstrating the different varying effects of socioeconomic predictors as a function of age on the working status