Integration of Computer Assisted Learning in Teaching and Learning in Secondary Schools in Kenya

The purpose of this study was to establish factors that lead to poor integration of Information and communication technology （ICT） for teaching and learning in schools in Kenya, despite comprehensive policy, institutional, infrastructural frameworks and capacity building by the Ministry of Education. The subject of this study was administered by use of questionnaires in three categories of public schools： national school, provincial schools and district schools. The respondents were students from each level that is from one, two, three and four and teachers based on the most offered subjects in the secondary schools. The computer assisted learning facilities were classified into computers, internet and content in optical media. In national school Internet based research, optical media content provided by Kenya Institute of Curriculum Development and Cyber School program for science subjects was used in learning. In provincial school, it lacks adequate computers, reliable Internet and content in optical media. In district school, it lacks adequate computer, no internet connection and content in optical media. A learner management system which can be accessed by all learners by use of any internet access devices like mobile phone access will be an ideal tool with over 4,000,000 mobile phone subscribers currently in Kenya.

Educational Application for Special Needs Is a Learning Tool the Way Forward？

As the 21st century brings in a revolutionary change in the way students study at schools and universities, technology continues to play a crucial role in helping students achieve more conceptual and practical knowledge of topics taught in classrooms. Students with special needs too are now able to study in a general classroom setting, access relevant technologies and use them for higher cognitive development, helping them integrate with their surroundings. However, existing literature shows that though multiple learning tools exist that do enhance learning in special needs students, they either cater to specific areas of development such as Mathematics and English, or that are targeted towards a specified category of studentswith special needs such as autism and cerebral palsy. Furthermore, despite multiple laws and regulations supporting the right to education launched by the UAE （United Arab Emirates） government for special needs students, there seems to exist a need to provide classrooms across the country with educational applications that have a universal approach particularly in the UAE in order to include students with almost any special needs. This paper looks closely at the existing literature and highlights this gap, especially in the UAE and proposes to develop such a tool based on existing learning concepts.

Conducting the Task-based Approach With Context Aware Mobile Situated Learning on Learning English for College Students＇ Learning Performance

Learning English with specific purpose （ESP） initiatives domain knowledge and language ability with English learning to meet future job demands （Leroux ＆ Lafleur, 1995; Khan ＆ Khan, 2015）. In the information age, the innovative technologies of mobile devices make the dramatical changes in ways of teaching and learning （Atkinson, 2011; Bierstaker, Janvrin ＆ Lowe, 2014, Pittaway, 2012; Yang ＆ Che, 2015）. The focus of this study aims to examine ESP college students＇ English learning performance by using Context Aware Mobile Situated Learning （CAMSL） in Tourism and Hospitality Management field and other majors. The mixed research method is conducted for data collection and analysis. The quantitative data are collected by examining students＇ learning performance; the qualitative data are allowed to understand the students＇ perspective toward their role in using the CAMSL with Tourism related content. Eight-three students are randomly selected and divided into two groups： 42 students are assigned in the experimental group A （CAMSL）, and 41 students are assigned in control group. Two groups of students receive pretest and posttest to examine their English performance. Besides, twenty students from group A and B are selected for online survey. The survey is mailed directly to students＇ email account. Results represented by using CAMSL show the significant improvement on students＇ learning performance. In addition, the survey data indicate the benefits of using the CAMSL help students enhance their academic discourse, develop their learned knowledge to represent their profession, use English properly to speak themselves up, and provide the effectiveness of obtain domain knowledge in future workplace.

Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites

The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.

Teaching Chinese sign language with a smartphone

Background There is a large group of deaf-mutes worldwide, and sign language is a major communication tool in this community. It is necessary for deaf-mutes to be able to communicate with others who are capable of hearing, and hearing people also need to understand sign language, which produces a great demand for sign language tuition. Even though there have already been a large number of books written about sign language, it is inefficient to learn sign language through reading alone, and the same can be said on watching videos. To solve this problem, we developed a smartphone-based interactive Chinese sign language teaching system that facilitates sign language learning. Methods The system provides a learner with some learning modes and captures the learner's actions using the front camera of the smartphone. At present, the system provides a vocabulary set with 1000 frequently used words, and the learner can evaluate his/her sign action by subjective or objective comparison. In the mode of word recognition, the users can play any word within the vocabulary, and the system will return the top three retrieved candidates;thus, it can remind the learners what the sign is. Results This system provides interactive learning to enable a user to efficiently learn sign language. The system adopts an algorithm based on point cloud recognition to evaluate a user's sign and costs about 700ms of inference time for each sample, which meets the real-time requirements. Conclusion This interactive learning system decreases the communication barriers between deaf-mutes and hearing people.

Mobile Assisted Chinese Learning as a Foreign Language:An Overview of Publications between 2007 and 2019

This paper presents a critical overview of studies on mobile assisted language learning(MALL)in teaching Chinese as a foreign language(CFL)during the period 2007−2019.In the review,keyword and reference searches were conducted to identify and select empirical studies during the review period.Thematic and frequency analyses were employed on the data.This identified methodological trends and research outcomes in the reviewed studies.As shown in the results,most of the reviewed studies used qualitative methods to examine the effect of mobile CFL learning on formal learning in higher education settings.These studies document the positive impact that mobile technology has on CFL learning.Their attention is primarily on the use of mobile learning in Chinese vocabulary acquisition,language skill development and mobile seamless learning.Suggestions are provided for further research to support continuous mobile assisted CFL teaching and learning.

REPORT ON THE 2006 INTERNATIONAL SYMPOSIUM OF COMPUTER ASSISTED LANGUAGE LEARNING: RESEARCH AND DEVELOPMENT OF CALL IN CHINA

This paper reviews (CALL 2006) held on June 2-4, 2006 in Beijing, China. It also describes the research and development of CALL in China and reflects on the problems as a result of this symposium.

A new formant feature and its application in Mandarin vowel pronunciation quality assessment

In order to improve the Mandarin vowel pronunciation quality assessment, a nox/el formant feature was proposed and applied to formant classification for Chinese Mandarin vowel pronunciation quality evaluation. Formant candidates of each frame were plotted on the time-frequency plane to form a bitmap, and its Gabor feature was extracted to represent the formant trajectory. The feature was then classified by using GMM model and the classification posterior probability was mapped to pronunciation quality grade. The experiments of comparing the Gabor transformation based formant trajectory feature with several other kinds of traditionally used features show that with this method, a human-machine scoring correlation coefficient （CC） of 0.842 can be achieved, which is better than the result of 0.832 by traditional speech recognition techniques. At the same time, considering that the long-term information of formant classification and the short-term information of speech recognition technique are complementary to each other, it is investigated to combine their results with linear or nonlinear methods to further improve the evaluation performance. As a result, experiments on PSK show that the best CC of 0.913, which is very close to the correlation of inter-human rating of 0.94, is gotten by using neural network.

COMPUTER ASSISTED INSTRUCTION AND ITS APPLICATION IN ENGLISH LEARNING

This paper briefly describes the development of computer assisted instruction(CAI) abroad and in China, lists the advantages of CAI and deals with its application in English learning. Some suggestions about how to make better use of CAI in ELT are also given.

Self-powered piezoelectric player-interactive patch for guitar learning assistance

Device-assisted practice for instrument learning has been widely used by professional and amateur musicians to improve their learning efficiency.This study fabricates a novel self-powered and flexible player-interactive patch for guitar-learning assistance based on a piezoelectric T-ZnO/PVDF film.The system consists primarily of three parts:a flexible piezoelectric T-ZnO/PVDF film for pressure sensing,a signal processing module for analyzing the sensed signal,and light-emitting diode(LED)indicators for visualizing guitar performance.The sensing film can be conformably fixed on a guitar and can convert the mechanical energy generated by pressing a finger on a string into a piezoelectric signal without any external power supply.The output voltage of the film can act as a sensing signal for guitar performance,and both the response and recovery times are short.As fingers press on different strings,a series of piezoelectric signals are generated and transferred to the signal processing module,subsequently lighting up LEDs of different colors.The actions of the fingers during guitar playing are reflected by the corresponding LED indicators.The proposed system can help players adjust their posture and rhythm in real time,thus improving their playing technique.This study demonstrates the potential application of self-powered sensing systems in musical instrument learning assistance.