Public speaking performance improvement as a function of information processing in immediate and delayed feedback interventions

2000 ◽  
Vol 49 (4) ◽  
pp. 365-374 ◽  
Author(s):  
Paul E. King ◽  
Melissa J. Young ◽  
Ralph R. Behnke
2019 ◽  
Vol 34 (1) ◽  
pp. 53-67 ◽  
Author(s):  
Melissa A. Broeckelman-Post ◽  
Katherine E. Hyatt Hawkins ◽  
Joshua Murphy ◽  
Ayodeji Otusanya ◽  
George Kueppers

2019 ◽  
Vol 6 (2) ◽  
pp. 88
Author(s):  
Amiruddin Amiruddin

This study was conducted to investigate errors in oral performance among the third year English Education Department students of UIN Ar-Raniry. It was aimed at two folds of research objectives. First, it sought to investigate the most frequently-committed error of the third year English Education Department students of UINAr-Raniry. Second, it attempted to identify the causes of students’ errors in their oral performance. This study employed qualitative research methods. The participants of this study were 20 students registering in Public Speaking Course. To investigate the students’ errors, a speaking test was used as a research instrument. The test was in the form of individual speaking performance on a topic of “Do we need native speakers in our Tarbiyah Faculty?” The participants were required to speak about the issue, which lasted for 10 minutes each. 20 oral performances were transcribed to enable the analysis of the errors. To analyze the student’s oral performance errors, the content analysis was used. This process was followed by analyzing the different aspects of language: grammar, pronunciation, and categories of error causes in communication. The results revealed that puzzling vowel insertion was the most commonly committed error (316/62.7%) compared to shifts in tense (10/2.0%), word order (19/3.8%), subject verb agreement (14/2.8%), and case of referent (15/3%). These errors were identified to have been caused by interlanguage factor. In an effort to respond to these compelling issues in the students’ speaking performance, lecturers who teach English at the University are required to give their maximum attention in order to improve their students’ oral performance.


2013 ◽  
Vol 19 (4) ◽  
pp. 1501610-1501610 ◽  
Author(s):  
K. Hicke ◽  
M. A. Escalona-Moran ◽  
D. Brunner ◽  
M. C. Soriano ◽  
I. Fischer ◽  
...  

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jie Sun ◽  
Wuhao Yang ◽  
Tianyi Zheng ◽  
Xingyin Xiong ◽  
Yunfei Liu ◽  
...  

AbstractReservoir computing is a potential neuromorphic paradigm for promoting future disruptive applications in the era of the Internet of Things, owing to its well-known low training cost and compatibility with hardware. It has been successfully implemented by injecting an input signal into a spatially extended reservoir of nonlinear nodes or a temporally extended reservoir of a delayed feedback system to perform temporal information processing. Here we propose a novel nondelay-based reservoir computer using only a single micromechanical resonator with hybrid nonlinear dynamics that removes the usually required delayed feedback loop. The hybrid nonlinear dynamics of the resonator comprise a transient nonlinear response, and a Duffing nonlinear response is first used for reservoir computing. Due to the richness of this nonlinearity, the usually required delayed feedback loop can be omitted. To further simplify and improve the efficiency of reservoir computing, a self-masking process is utilized in our novel reservoir computer. Specifically, we numerically and experimentally demonstrate its excellent performance, and our system achieves a high recognition accuracy of 93% on a handwritten digit recognition benchmark and a normalized mean square error of 0.051 in a nonlinear autoregressive moving average task, which reveals its memory capacity. Furthermore, it also achieves 97.17 ± 1% accuracy on an actual human motion gesture classification task constructed from a six-axis IMU sensor. These remarkable results verify the feasibility of our system and open up a new pathway for the hardware implementation of reservoir computing.


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