Impacts of the Problem-based Learning Pedagogy on English Learners’ Reading Comprehension, Strategy Use, and Active Learning Attitudes

This study investigated whether an English reading course integrated with the problem-based learning approach could foster foreign language learners’ reading comprehension ability, strategy use, and their active learning attitudes. The pedagogy was featured with the small group scaffolding. Two intact English classes in a Taiwanese university were randomly assigned to the experimental and control treatment conditions. The experimental group received problem-based learning instruction, whereas the control group did not. The reading comprehension pre- and post-tests and the English active learning questionnaire were employed to collect the data. The Independent Samples t test yielded a significant difference between the 2 groups in the total score of the reading comprehension post-test, indicating that the PBL approach significantly improved the participants’ reading comprehension ability, and the PBL participants’ strategy use for identifying the subject matter and supportive details was better than that of their counterparts. The t-test results of the questionnaire showed that the PBL participants exhibited a significantly higher degree of active English learning attitude than the non-PBL participants in terms of motivation intensity and desire to learn English. There was a significantly positive correlation between reading comprehension ability and English learning attitude. Finally, instructional recommendations are presented

Renewable Composite Quantile Method and Algorithm for Nonparametric Models with Streaming Data

We are interested in renewable estimations and algorithms for nonparametric models with streaming data. In our method, the nonparametric function of interest is expressed through a functional depending on a weight function and a conditional distribution function (CDF). The CDF is estimated by renewable kernel estimations combined with function interpolations, based on which we propose the method of renewable weighted composite quantile regression (WCQR). Then we fully use the model structure and obtain new selectors for the weight function, such that the WCQR can achieve asymptotic unbiasness when estimating specific functions in the model. We also propose practical bandwidth selectors for streaming data and find the optimal weight function minimizing the asymptotic variance. The asymptotical results show that our estimator is almost equivalent to the oracle estimator obtained from the entire data together. Besides, our method also enjoys adaptiveness to error distributions, robustness to outliers, and efficiency in both estimation and computation. Simulation studies and real data analyses further confirm our theoretical findings.Comment: 24 pages, 0 figure

Optical fiber multiplexing interferometer system with a single laser diode and its application to online displacement measurement

A multiplexed optical fiber Michelson interferometer system that is self-referenced with a stabilizing feedback loop is presented. This system employs fiber Bragg gratings and wavelength division multiplexing technique to combine two optical fiber interferometers that share the same optical path in the main part of the optical system. When one Michelson interferometer, which uses the fiber Bragg gratings as reflective mirrors and is used as reference interferometer, is stabilized by an electric feedback loop, the other interferometer, which is used for the measurement, is also stabilized. This system is therefore suitable for online precision measurement. An active phase-tracking technique is applied for signal processing to achieve high resolution

High reward enhances perceptual learning.

Studies of perceptual learning have revealed a great deal of plasticity in adult humans. In this study, we systematically investigated the effects and mechanisms of several forms (trial-by-trial, block, and session rewards) and levels (no, low, high, subliminal) of monetary reward on the rate, magnitude, and generalizability of perceptual learning. We found that high monetary reward can greatly promote the rate and boost the magnitude of learning and enhance performance in untrained spatial frequencies and eye without changing interocular, interlocation, and interdirection transfer indices. High reward per se made unique contributions to the enhanced learning through improved internal noise reduction. Furthermore, the effects of high reward on perceptual learning occurred in a range of perceptual tasks. The results may have major implications for the understanding of the nature of the learning rule in perceptual learning and for the use of reward to enhance perceptual learning in practical applications