An extendible and ubiquitious e-learning software for foreigners to learn Chinese on iOS-based devices

The larger screen size, mobility, sensing and multi-touch input features of Android or iOS based devices like the iPad or Android tablet PC offer many exciting opportunities for real-life edutainment applications. An example is the learning of Chinese, especially to write Chinese characters in correct stroke sequences, that is typically considered as a difficult task to most foreigners. In this work, we propose an extendible, easy-to-use and ubiquitous e-learning software that was integrated with mini-games to facilitate the learning of Chinese characters on iOS-based devices such as the iPad. To demonstrate the feasibility of our proposal, a prototype of our proposed e-learning software was built on the iOS-based devices so that foreign students can learn to speak and write Chinese anytime and anywhere. Among the various commercially available iOS-based app for learning Chinese, our proposal represents the first attempt to reduce the complexity of learning to write Chinese characters through mini-games while increasing the extendibility of the e-learning software through component-based design. More importantly, it opens up numerous opportunities for further investigations. © 2012 IEEE.published_or_final_versio

An extendible and ubiquitious e-learning software for foreigners to learn Chinese on iOS-based devices

The larger screen size, mobility, sensing and multi-touch input features of Android or iOS based devices like the iPad or Android tablet PC offer many exciting opportunities for real-life edutainment applications. An example is the learning of Chinese, especially to write Chinese characters in correct stroke sequences, that is typically considered as a difficult task to most foreigners. In this work, we propose an extendible, easy-to-use and ubiquitous e-learning software that was integrated with mini-games to facilitate the learning of Chinese characters on iOS-based devices such as the iPad. To demonstrate the feasibility of our proposal, a prototype of our proposed e-learning software was built on the iOS-based devices so that foreign students can learn to speak and write Chinese anytime and anywhere. Among the various commercially available iOS-based app for learning Chinese, our proposal represents the first attempt to reduce the complexity of learning to write Chinese characters through mini-games while increasing the extendibility of the e-learning software through component-based design. More importantly, it opens up numerous opportunities for further investigations. © 2012 IEEE.published_or_final_versio

Exploring Chinese through learning objects and interactive interface on mobile devices

Session H3CWith its unprecedented economic growth, China has gradually developed its significant influence on the global stage in recent years. As a result, there are increasing interests to learn Chinese all over the world. Intrinsically, learning Chinese is challenging to most foreigners and Chinese students as well due to the complex structures of Chinese Characters, the writing of characters in correct stroke sequences, and their appropriate usage and pronunciation, etc. Even with the guidance of an experienced Chinese teacher, there is often insufficient time to practise the writing or pronunciation during classes. However, mobile devices such as the iPads or iPhones may open up numerous opportunities facilitated by the latest interface and sensing technologies for students to learn anytime and anywhere. Therefore in this project, we propose an extendible application based on learning objects which can fully utilized these features including the GPS, touch screen and camera of mobile devices to facilitate foreigners or Chinese students to learn Chinese more effectively. More importantly, we have designed an intelligent algorithm to help students in writing Chinese characters with correct stroke sequences. To demonstrate the feasibility of our proposal, a prototype of our proposed e-learning software is built on the iOS platform, and will be evaluated with a thorough plan. Furthermore, there are many interesting directions for further investigation of our proposal. © 2012 IEEE.published_or_final_versio

An extendible software for learning to write Chinese characters in correct stroke sequences on smartphones

With the fast economic development in China, learning to understand Chinese becomes very crucial and popular worldwide. To most foreigners and even native Chinese students, one of the major challenges in learning Chinese is to write Chinese characters in correct stroke sequences since the correct stroke sequences of writing any Chinese character is regarded as crucial in the Chinese culture. Intrinsically, there were very few available character recognition techniques that can tackle the complexity of structures of Chinese characters together with their stroke sequences. In this paper, we propose an extendible and intelligent e-learning software based on learning objects to facilitate the learning of writing Chinese characters in correct stroke sequences. To demonstrate the feasibility of our proposal, a prototype of our proposed e-learning software was built on smartphones. Our proposal represents the first attempt to reduce the complexity while increasing the extendibility of the e-learning software to learn Chinese through learning objects. More importantly, it opens up numerous opportunities for further investigations. © 2011 IEEE.published_or_final_versionThe 11th IEEE International Conference on Advanced Learning Technologies (ICALT 2011), Athens, GA., 6-8 July 2011. In Proceedings of the IEEE International Conference on Advanced Learning Technologies, 2011, p. 118-11

Research on Calligraphy Evaluation Technology Based on Deep Learning

Today, when computer-assisted instruction (CAI) is booming, related research in the field of calligraphy education still hasn’t much progress. This main research for the calligraphy beginners to evaluate their works anytime and anywhere. Author uses the literature research and interview to understand the common writing problems of beginners. Then conducts discussion on these problems, design of solutions, research on algorithms, and experimental verification. Based on the ResNet-50 model, through WeChat applet implements for beginners. The main research contents are as follows: (1) In order to achieve good results in calligraphy judgment, this article uses the ResNet-50 model to judge calligraphy. First, adjust the area of the handwritten calligraphy image as the input of the network to a small block suitable for the network. While training the network, adjust the learning rate, the number of image layers and the number of training samples to achieve the optimal. The research results show that ResNet has certain practicality and reference value in the field of calligraphy judgment. Regarding the possible over-fitting problem, this article proposes to improve the accuracy of the judgment by collecting more data and optimizing the data washing process. (2) Combining the rise of WeChat applets, in view of the current WeChat applet learning platform development process and the problem of fewer functional modules, this paper uses cloud development functions to develop a calligraphy learning platform based on WeChat applets. While simplifying the development process, it ensures that the functional modules of the platform meet the needs of teachers and beginners, it has certain practicality and commercial value. After the development of the calligraphy learning applet is completed, it will be submitted for official

Apps-based Machine Translation on Smart Media Devices - A Review

Machine Translation Systems are part of Natural Language Processing (NLP) that makes communication possible among people using their own native language through computer and smart media devices. This paper describes recent progress in language dictionaries and machine translation commonly used for communications and social interaction among people or Internet users worldwide who speak different languages. Problems of accuracy and quality related to computer translation systems encountered in web & Apps-based translation are described and discussed. Possible programming solutions to the problems are also put forward to create software tools that are able to analyze and synthesize language intelligently based on semantic representation of sentences and phrases. Challenges and problems on Apps-based machine translation on smart devices towards AI, NLP, smart learning and understanding still remain until now, and need to be addressed and solved through collaboration between computational linguists and computer scientists

Recognition of Japanese handwritten characters with Machine learning techniques

The recognition of Japanese handwritten characters has always been a challenge for researchers. A large number of classes, their graphic complexity, and the existence of three different writing systems make this problem particularly difficult compared to Western writing. For decades, attempts have been made to address the problem using traditional OCR (Optical Character Recognition) techniques, with mixed results. With the recent popularization of machine learning techniques through neural networks, this research has been revitalized, bringing new approaches to the problem. These new results achieve performance levels comparable to human recognition. Furthermore, these new techniques have allowed collaboration with very different disciplines, such as the Humanities or East Asian studies, achieving advances in them that would not have been possible without this interdisciplinary work. In this thesis, these techniques are explored until reaching a sufficient level of understanding that allows us to carry out our own experiments, training neural network models with public datasets of Japanese characters. However, the scarcity of public datasets makes the task of researchers remarkably difficult. Our proposal to minimize this problem is the development of a web application that allows researchers to easily collect samples of Japanese characters through the collaboration of any user. Once the application is fully operational, the examples collected until that point will be used to create a new dataset in a specific format. Finally, we can use the new data to carry out comparative experiments with the previous neural network models

Improving Students' Language Performance Through Consistent Use of E-Learning: An Empirical Study in Japanese, Korean, Hindi and Sanskrit

This paper describes the backing theories, methodology, and results of a two-semester long case study of the application of technology in teaching four Asian languages (Japanese, Korean, Hindi, and Sanskrit) to Croatian students. We have developed e-learning materials to follow the curriculum in Croatia and deployed them in Asian language classrooms. Students who agreed to participate in the study were tested before using the materials, and after each semester, and their progress was surveyed. In the case of Japanese students (N=53), we have thoroughly monitored their usage and compared the progress of students who have diligently studied vocabulary and grammar using our materials on Memrise, and those who have neglected their studies. This was measured through their scores on the Memrise, which shows the user's activity. Also, their progress was measured using standardized tests that were designed in such a manner to resemble Japanese Language Proficiency Test. We have found that frequent users progressed averagely 20,3% after each semester, while non-frequent users have progressed only 11,6%, proving this method to be related to stable and constant use of e-materials

The Effectiveness of Using Cloud-Based Cross-Device IRS to Support Classical Chinese Learning

[[abstract]]The purpose of the present study was to examine the effects of integrating a cloud-based cross-device interactive response system (CCIRS) on enhancing students¡¦ classical Chinese learning. The system is a cloud-based IRS system which provides instructors and learners with an environment in which to achieve immediate interactive learning and discussion in the classroom. A quasi-experimental design was employed in which the experimental group (E.G.) learned classical Chinese with the system, while the control group (C.G.) followed their original learning method. The results revealed that the novice and medium-achievement learners in the E.G. performed significantly better than other E.G. students, and most students as well as the instructor gave positive feedback regarding the use of the system for course learning. In sum, CCIRS is an easy-to-use learning trigger that encourages students to participate in activities, arouses course discussion, and helps to achieve students¡¦ social and self-directed learning. The study concludes that the idea of ¡¥bring your own device¡¦ could be implemented with this system, while integrating educational factors such as game-based elements and competitive activities into the response system could reinforce flipped classroom learning.[[notice]]補正完

Scalable Teaching and Learning via Intelligent User Interfaces

The increasing demand for higher education and the educational budget cuts lead to large class sizes. Learning at scale is also the norm in Massive Open Online Courses (MOOCs). While it seems cost-effective, the massive scale of class challenges the adoption of proven pedagogical approaches and practices that work well in small classes, especially those that emphasize interactivity, active learning, and personalized learning. As a result, the standard teaching approach in today’s large classes is still lectured-based and teacher-centric, with limited active learning activities, and with relatively low teaching and learning effectiveness. This dissertation explores the usage of Intelligent User Interfaces (IUIs) to facilitate the efficient and effective adoption of the tried-and-true pedagogies at scale. The first system is MindMiner, an instructor-side data exploration and visualization system for peer review understanding. MindMiner helps instructors externalize and quantify their subjective domain knowledge, interactively make sense of student peer review data, and improve data exploration efficiency via distance metric learning. MindMiner also helps instructors generate customized feedback to students at scale. We then present BayesHeart, a probabilistic approach for implicit heart rate monitoring on smartphones. When integrated with MOOC mobile clients, BayesHeart can capture learners’ heart rates implicitly when they watch videos. Such information is the foundation of learner attention/affect modeling, which enables a ‘sensorless’ and scalable feedback channel from students to instructors. We then present CourseMIRROR, an intelligent mobile system integrated with Natural Language Processing (NLP) techniques that enables scalable reflection prompts in large classrooms. CourseMIRROR 1) automatically reminds and collects students’ in-situ written reflections after each lecture; 2) continuously monitors the quality of a student’s reflection at composition time and generates helpful feedback to scaffold reflection writing; 3) summarizes the reflections and presents the most significant ones to both instructors and students. Last, we present ToneWars, an educational game connecting Chinese as a Second Language (CSL) learners with native speakers via collaborative mobile gameplay. We present a scalable approach to enable authentic competition and skill comparison with native speakers by modeling their interaction patterns and language skills asynchronously. We also prove the effectiveness of such modeling in a longitudinal study