Neil Y. Yen

A sharable e-Learning platform based on Cloud computing

Over the Internet, e-Learning has speeded up the knowledge transfer without restrictions on time and space in recent years. In order to achieve the goal of knowledge sharing and reusing interactively, lots of available e-Learning standards have been adapted to complete this purpose. Normally, learning objects meeting these standards are sharable with other e-Learning platforms. Most e-Learning platforms, however, are unable to share learning objects directly on the Internet. The characteristics of Cloud computing provide a promising infrastructure to compute and store resources as services. Hence, this paper introduces Cloud computing into an e-Learning platform to allow the integration of different e-Learning standards to enhance interoperability of learning objects. By combining the characteristics of e-Learning and approach of Cloud computing, educators have not to re-construct learning objects to satisfy e-Learning environments developed from different e-Learning standards.

Courseware Authoring Tool for Achieving Interoperability among Various E-Learning Specifications Based on Web 2.0 Technologies

With the trend of the Internet applications, e- learning has shown its significance along with various learning activities in more convenient ways. Followed by this tendency, many well-known e-learning specifications were proposed to regulate those digitalized learning activities in a standardized manner. However, with respect to the interoperability among various e-learning specifications nowadays, some of them were designed to support particular domains. And this will lead to the result of poor shareability of existing learning resources. On the other hand, the courseware authoring could be considered as the fundamental process while realizing an e-learning environment. As a result, the applicable scope and the user-friendliness should be taken into account for supporting efficient learning contents development. In this paper, we will like to propose a courseware authoring tool to achieve the interpretability among different e-learning specifications based on Web 2.0 technologies in an efficient way.

Adaptive Object Re-Ranking Mechanism for Ubiquitous Learning Environment

Ubiquitous Learning (U-Learning), as an emerging learning paradigm, makes it possible for learners to carry out the learning activities at any places and at anytime. With the advantages of the devices, learners can obtain a variety of supplementary materials from the Internet. In the scope of distance learning, LOR (Learning Object Repository) stands for managing and sharing of learning related materials (known as learning objects). However, some challenges may raise while performing these activities. For instance, a huge amount of learning objects may appear while learners utilize the search service provided by LOR. Learners have to spend time on collecting relevant resources for specific purposes. This situation may discourage the reusability of learning objects especially in a ubiquitous environment. In this paper, based on systematic re-examination of reuse scenarios, an adaptive mechanism, as a resource discovery and search middleware, was proposed to assist learners in obtaining possible objects under ubiquitous environment. Achievement of the proposed mechanism can produce search results adaptive to specific situations in order of similarity degree based on the mixed information. We try to filter out some irrelevant results by using the past usage history, current geographical information and input query, so as to enhance the efficiency of learning objects retrieval in a ubiquitous environment. As a pilot test, Apple iPhone was utilized to be the major client testbed.

Weighting and Ranking the E-learning Resources

The use of learning objects is necessary in distance learning environment. Lots of repositories provide only search service for the users. Users have to sift and collect the learning objects they really need. In previous works, we have constructed the reusability tree based on CORDRA architecture for the repository users. In this paper, we further collect and record the citation numbers of learning objects and then propose a formula to calculate the weight for each learning object. The basic concept is based on the social network of Web 2.0 and takes Google Page Rank mechanism into consideration. After getting weight values, we also integrate the similarity mechanism into our formula and rank these learning objects.

Extra-value for e-learning systems: An e-learning standard transformation mechanism

There are lots of acknowledge systems involved in the e-learning environment. In order to achieve the goal of sharing and reusing the learning content generated from different educators, the common standards should be followed. More and more e- learning standards like IEEE LOM (learning object metadata), ADL SCORM (sharable content object reference model), and IMS common cartridge are developed to achieve this purpose. Besides, there are still some localized standards like UK LOM core, Can Core, TW LOM are developed according to the IEEE LOM to serve the localization needs. In this paper, we proposed a transformation mechanism to provide interoperability among these e-learning standards. Our mechanism could be regarded as a module that could be imported into any acknowledged e-learning systems. Based on this mechanism, educators will not have to re-develop the learning content to serve the systems developed from different e-learning standards.

A Combination with Knowledge Acquisition Petri Net for Location-Based Service

Since the ubiquitous computing was proposed and developed for many years and the matured develop of location based research, personal user information and e-travel, our research provides a personal and useful tour guide mechanism and adaptive learning content when user traveling in anywhere they interesting. This research hopes user not only have fun during tour but also provides several knowledge they may interests about the tour places. This research extends former experiences on the development of location based research, and integrate the Petri Net methods to produce the task based procedure during the travel for user, we called it MOPN (Mission oriented Petri-Net)Besides, this research also proposes a user portfolio synchronize mechanism to provide user more adaptive and personal content based on former user background and using experience. With the results of this research, most user indicated that they have more fun with proposed system during the travel and also got lots of useful knowledge from the mission.

Intelligent Optimization Scheduling Algorithm for Professional Sports Games

The world financial crisis has caused a great impact to human beings¡¦ daily life. The significant evidence is that the oil price has hit, more than, 90 U.S. according to the report of ministry of economic affairs. The price reflects the difficulty not only to transportation but finance status. In this paper, an optimization algorithm concerning the scheduling issues was proposed. The algorithm was revised in accordance with Tabu Search algorithm and Genetics algorithm. The purpose of this work is to reduce the travelling cost, simply achieve the least expenditures. The information of famous sports league (e.g. NBA) was adopted as preliminary experiment data. The travelling distances, according to the matchups of 2009 NBA games, were collected and obtained the straight-line distances from Google Map API. As a result, our method could reduce, at least, around 14,000 kilometers under 45,000 times mutation with the rate at 0.1%.

Cognitive assessment evaluation based on Blue-Red trees Inference of Rule-Space Model and social network learning

It is getting important that learning reciprocally from social network to intelligent e-learning system. In this paper, we used the Rule-Space Model to infer reasonable Blue-Red trees of learning performance and their definitions through analyzing learning objects of courses within system. We can derive social network learning grouping algorithms from previous definitions. They include one-to-one group collaborative learning algorithms. Further, we use these algorithms to build a social network learning system and apply to the Online Learning and Test System based on Analysis of Learning Performance and Social Network Learning Feedback. Besides learners are able to self-learning through Recommended Table to the Remedial Learning Objects for the Course, they can find out reasonable mutual learning targets by the suggestion of Blue-Red Trees Database of Learning Performance from the Social Network Learning System. There are the same results to improve the self-learning direction of learners and remedy the weaker learning objects for the course by internal cyclic feedback, and can also improve learning materials and items of the ITE Licenses Course by external cyclic feedback.

ACM international workshop on multimedia technologies for distance learning (MTDL 2009)

The MTDL 2009 workshop aims to discuss the impact of multimedia technologies to e-Learning. This workshop is held in conjunction with the ACM Multimedia 2009 Conference in Beijing. As a cover paper of this workshop, we briefly summarize important technologies used in e-learning in the first section, followed by a discussion of important issues proposed in the 11 papers accepted to the workshop (among the 24 submissions).

Intelligent Evaluation Rules for Learning Objects

With the popularity of social media, sharing and retrieving information on website, especially those user-generated content, all of this information have become an emerging phenomenon. At the same time, it brought an open issue of information management and discovery. In this research, a common service framework for learning resource retrieval was proposed. Two major contributions, ranking and recommendation, are included in this framework. This ranking process reveals the value of a specific object by taking its implicit information, such as authority, citation, and time series issue, into consideration. In addition, the personalized attributes are also concerned. The above mentioned attributes will be recorded as a profile and will be categorized into several groups for further use. And we can utilize the personalization factor to produce recommendation results.