Ruimin Shen

The impact of mobile learning on students' learning behaviours and performance: Report from a large blended classroom

Chinese classrooms, whether on school grounds or online, have long suffered from a lack of interactivity. Many online classes simply provide recorded instructor lectures, which only reinforces the negative effects of passive nonparticipatory learning. At Shanghai Jiaotong University, researchers and developers actively seek technologic interventions that can greatly increase

A scalable P2P recommender system based on distributed collaborative filtering

Collaborative Filtering (CF) technique has been proved to be one of the most successful techniques in recommender systems in recent years. However, most existing CF based recommender systems worked in a centralized way and suffered from its shortage in scalability as their calculation complexity increased quickly both in time and space when the record in user database increases. In this article, we first propose a distributed CF algorithm called PipeCF together with two novel approaches: significance refinement and unanimous amplification, to further improve the scalability and prediction accuracy. We then show how to implement this algorithm on a Peer-to-Peer (P2P) structure through distributed hash table method, which is the most popular and efficient P2P routing algorithm, to construct a scalable distributed recommender system. The experimental data show that the distributed CF-based recommender system has much better scalability than traditional centralized ones with comparable prediction efficiency and accuracy. q 2004 Elsevier Ltd. All rights reserved.

Message design for mobile learning: Learning theories, human cognition and design principles

The demands of an increasingly knowledge-based society and the dramatic advances in mobilephonetechnologyarecombiningtospurthegrowthof mobilelearning(mLearning). However, for mLearning to attain its full potential, it is essential to develop pedagogyandinstructionaldesigntailoredtotheneedsof thisnewlearningenvironment.At present, there is a lack of research on message design for mLearning. Towards these ends, this paper explores the principles and processes of message design for mLearning, including the influence of learning and cognitive theories, human‐computer interaction principles, devices and methodologies. And it presents a number of practical guidelines for designing instructional messages for mLearning.

Priority-based target coverage in directional sensor networks using a genetic algorithm

Sensor networks have been applied in a wide variety of situations. Recently directional sensor networks consisting of directional sensors have gained attention. As for the traditional target coverage problem, the limited sensing angle of directional sensors makes it even more challenging. Moreover, individual targets may also be associated with differentiated priorities. Considering the distance between the directional sensors and targets influences sensing quality, this paper proposes the priority-based target coverage problem and strives to choose a minimum subset of directional sensors that can monitor all targets, satisfying their prescribed priorities. Due to the NP-Complete complexity, the minimum subset of directional sensors is approximated by using a genetic algorithm. Simulation results reveal the effects of multiple factors on the size of the resulting subset.

A collaborative filtering framework based on both local user similarity and global user similarity

Collaborative filtering as a classical method of information retrieval has been widely used in helping people to deal with information overload. In this paper, we introduce the concept of local user similarity and global user similarity, based on surprisal-based vector similarity and the application of the concept of maximin distance in graph theory. Surprisal-based vector similarity expresses the relationship between any two users based on the quantities of information (called surprisal) contained in their ratings. Global user similarity defines two users being similar if they can be connected through their locally similar neighbors. Based on both of Local User Similarity and Global User Similarity, we develop a collaborative filtering framework called LS&GS. An empirical study using the MovieLens dataset shows that our proposed framework outperforms other state-of-the-art collaborative filtering algorithms.

Increasing Interactivity in Blended Classrooms through a Cutting-Edge Mobile Learning System.

Chinese classrooms, whether on school grounds or online, have long suffered from a lack of interactivity. Many online classes simply provide recorded lectures to which students listen after downloading. This format only reinforces the negative effects of passive non-participatory learning. At the e-Learning Lab of Shanghai Jiaotong University researchers and developers actively seek technologic interventions that can greatly increase interactivity in blended classes. They developed a cutting-edge mobile learning system that can deliver live broadcast of real-time classroom teaching to online students with mobile devices. Their system allows students to customise means of content-reception, based on when and where the students are tuning in to the broadcast. The system also supports short text messaging and instant polls. Through these venues, students can ask questions and make suggestions in real time, and the instructor can address them immediately. Here we describe this system in detail, and also report results from a test implementation of the system with a blended classroom of 1000 students (250 campus and 750 online). [ABSTRACT FROM AUTHOR]

A novel image watermarking scheme based on support vector regression

In this paper, a novel support vector regression based color image watermarking scheme is proposed. Using the information provided by the reference positions, the support vector regression can be trained at the embedding procedure, and the watermark is adaptively embedded into the blue channel of the host image by considering the human visual system. Thanks to the good learning ability of support vector machine, the watermark can be correctly extracted under several different attacks. Experimental results show that the proposed scheme outperform the Kutters method and Yus method against different attacks including noise addition, shearing, luminance and contrast enhancement, distortion, etc. Especially when the watermarked image is enhanced in luminance and contrast at rate 70%, our method can extract the watermark with few bit errors.

Mobile Learning in a Large Blended Computer Science Classroom: System Function, Pedagogies, and Their Impact on Learning

The computer science classes in Chinas institutions of higher education often have large numbers of students. In addition, many institutions offer ldquoblendedrdquo classes that include both on-campus and online students. These large blended classrooms have long suffered from a lack of interactivity. Many online classes simply provide recorded instructor lectures to which distance students listen after downloading. This format only reinforces the negative effects of passive nonparticipatory learning. At a major university in Shanghai, researchers and developers actively seek technological interventions that can greatly increase interactivity in blended classes. They have developed a cutting-edge mobile learning system that can deliver live broadcasts of real-time classroom teaching to online students with mobile devices. Their system allows students to customize their means of content-reception, based on when and where the students are tuning into the broadcast. The system also supports short text-messaging and instant polls. Through these features, students can ask questions and make suggestions in real time, and the instructor can respond immediately. This paper describes this system in detail and also reports results from a formal implementation of the system with a blended classroom of 562 students (of whom 90% were online).

Learning Content Recommendation Service Based-on Simple Sequencing Specification

A new era of e-learning is on the horizon, hundreds of Learning Contents are created and more and more people begin to acquire acknowledge thru e-learning. The traditional teaching method is already showing its limitations that students from different backgrounds are still given the same contents at the same time, and they may only interest in part of a whole learning content. In this paper, we propose a novel way to organize learning contents into small ”atomic” units called Learning Objects so that they could be used and reused effectively. The Learning Objects together with their ontology are systemized into knowledge base. An intelligent recommendation mechanism based on sequencing rules is then introduced with detail, where the rules are formed from the knowledge base and competency gap analysis. Finally we establish a test knowledge base system, using and extending the ontology editor Protege-2000 and its Protege Axiom Language.

DCFLA: A distributed collaborative-filtering neighbor-locating algorithm

Although collaborative filtering (CF) has proved to be one of the most successful techniques in recommendation systems, it suffers from a lack of scalability as the time complexity rapidly increases when the number of the records in the user database increases. As a result, distributed collaborative filtering (DCF) is attracting increasing attention as an alternative implementation scheme for CF-based recommendation systems. In this paper, we first propose a distributed user-profile management scheme using distributed hash table (DHT)-based routing algorithms, which is one of the most popular and effective approaches in peer-to-peer (P2P) overlay networks. In this DCF scheme, an efficient DCF neighbor-locating algorithm (DCFLA) is proposed, together with two improvements, most same opinion (MSO) and average rating normalization (ARN), to reduce the network traffic and time cost. Finally, we analyze the performance of one baseline and three novel CF algorithms are being proposed: (1) a traditional memory-based CF (baseline); (2) a basic DHT-based CF; (3) a DHT-based CF with MSO; and (4) a DHT-based CF with MSO and ARN. The experimental results show that the scalability of our proposed DCFLA is much better than the traditional centralized CF algorithm and the prediction accuracies of these two systems are comparable.