An AHP-based Computer Evaluation Model for College Students English Pronunciation Quality

2021 ◽  
Author(s):  
Yan Long ◽  
XinGuang Li ◽  
Shuai Chen ◽  
ShenBin Zhang ◽  
ShanXian Ma ◽  
...  
Keyword(s):  
College Students ◽  
Evaluation Model ◽  
Computer Evaluation ◽  
English Pronunciation

scholarly journals The Usefulness of Automatic Speech Recognition (ASR) Eyespeak Software in Improving Iraqi EFL Students’ Pronunciation

2017 ◽  
Vol 8 (1) ◽  
pp. 221 ◽  
Author(s):  
Lina Fathi Sidig Sidgi ◽  
Ahmad Jelani Shaari
Keyword(s):  
College Students ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Native Language ◽  
First Year ◽  
First Year College ◽  
Pronunciation Instruction ◽  
English Pronunciation ◽  
Efl Students ◽  
First Year College Students

The present study focuses on determining whether automatic speech recognition (ASR) technology is reliable for improving English pronunciation to Iraqi EFL students. Non-native learners of English are generally concerned about improving their pronunciation skills, and Iraqi students face difficulties in pronouncing English sounds that are not found in their native language (Arabic). This study is concerned with ASR and its effectiveness in overcoming this difficulty. The data were obtained from twenty participants randomly selected from first-year college students at Al-Turath University College from the Department of English in Baghdad-Iraq. The students had participated in a two month pronunciation instruction course using ASR Eyespeak software. At the end of the pronunciation instruction course using ASR Eyespeak software, the students completed a questionnaire to get their opinions about the usefulness of the ASR Eyespeak in improving their pronunciation. The findings of the study revealed that the students found ASR Eyespeak software very useful in improving their pronunciation and helping them realise their pronunciation mistakes. They also reported that learning pronunciation with ASR Eyespeak enjoyable.  

scholarly journals Training Strategies for Practical Ability of College Students Majoring in Computer-Aided Design

2020 ◽  
Vol 15 (16) ◽  
pp. 134
Author(s):  
Xingde Liu
Keyword(s):  
College Students ◽  
Computer Aided Design ◽  
Evaluation Model ◽  
Engineering Application ◽  
Curriculum Planning ◽  
Promoting Effect ◽  
Effective Training ◽  
Computer Aided ◽  
Training Strategies ◽  
Aided Design

Currently, the college students majoring in computer-aided design (CAD), a discipline under the department of mechanical engineering, generally have a poor ability to solve engineering problems. Therefore, this paper attempts to design effective training strategies to improve the practical ability of the CAD majors. Firstly, the authors highlighted the promoting effect of the CAD on the practical ability of college students, and summed up the prob-lems of college students in the engineering application of the CAD. On this basis, several strategies were put forward to improve the practical ability of the CAD majors from three aspects: CAD curriculum planning, CAD practi-cal training and CAD platform construction. To verify the effectiveness of the strategies, an index system was established based on information entro-py, and organized into an evaluation model through expert scoring. The strategies and evaluation model provide strong supports to the training of practical ability for the CAD majors.

scholarly journals DEUS computer evaluation model. Volume 1. Program descriptive manual. Final report

1982 ◽  
Author(s):  
A. Anand ◽  
E. Hall ◽  
J. Oplinger ◽  
R. Priestley ◽  
J. Sweeney, III
Keyword(s):  
Evaluation Model ◽  
Final Report ◽  
Computer Evaluation

scholarly journals An Evaluation Model for the Innovation and Entrepreneurship Thinking Ability of College Students Based on Neural Network

2021 ◽  
Vol 16 (02) ◽  
pp. 188
Author(s):  
Feng Zhang ◽  
Limin Xi
Keyword(s):  
Neural Network ◽  
College Students ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Analytic Hierarchy ◽  
Thinking Ability ◽  
Fuzzy Neural ◽  
Knowledge Rules ◽  
Innovation And Entrepreneurship

Mass innovation and entrepreneurship (I&E) is a national campaign in China. In this context, it is important to encourage college students to engage in I&E activities, and this calls for accurate and comprehensive evaluation of their I&E thinking ability. Therefore, this paper proposes an evaluation model for the I&E thinking ability of college students based on neural network (NN). Firstly, a reasonable evaluation index system was created for the I&E thinking ability of college students, and the evaluation indices were preprocessed through fuzzy analytic hierarchy process (AHP). Then, a fuzzy neural network (FNN) was constructed based on GA rule optimization and the specific steps of the algorithm were given. Moreover, a few representative rules were selected by GA based on uncertain fuzzy knowledge rules, a 4-layer NN model with fuzzy inputs and outputs was established, and the evaluation flow of the I&E thinking ability of college students was proposed. Finally, the effectiveness of the proposed model was verified through experiments. The research results of this paper provide a reference for the application of NN in the field of ability evaluation.

scholarly journals Determination of the minimum distance between vibration source and fibre under existing optical vibration signals: a study

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Haitao Li ◽  
Guoqing Zhang ◽  
Nawaf Alhebaishi ◽  
Hamdy Abdelhameed
Keyword(s):  
College Students ◽  
Minimum Distance ◽  
Evaluation Model ◽  
Status Quo ◽  
Vibration Source ◽  
Vibration Signals ◽  
The Status ◽  
Innovation And Entrepreneurship ◽  
Optical Vibration

Abstract The article analyses why colleges and universities should strengthen innovation and entrepreneurship education based on ‘mass entrepreneurship and innovation.’ First, we conduct a questionnaire survey on the status quo of college students’ innovation and entrepreneurship and use nonlinear methods to construct an evaluation model of innovation and entrepreneurship capabilities to evaluate students’ innovation and entrepreneurship capabilities quantitatively. Finally, we verify the effectiveness of the combined evaluation model through the data on the innovation and entrepreneurship of college students. The research results can provide a new idea for appraisal of college students’ innovation and entrepreneurship ability.

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