Lina Zhou

Can e-learning replace classroom learning?

In an e-learning environment that emphasizes learner-centered activity and system interactivity, remote learners can outperform traditional classroom students.

Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness

Interactive video in an e-learning system allows proactive and random access to video content. Our empirical study examined the influence of interactive video on learning outcome and learner satisfaction in e-learning environments. Four different settings were studied: three were e-learning environments--with interactive video, with non-interactive video, and without video. The fourth was the traditional classroom environment. Results of the experiment showed that the value of video for learning effectiveness was contingent upon the provision of interactivity. Students in the e-learning environment that provided interactive video achieved significantly better learning performance and a higher level of learner satisfaction than those in other settings. However, students who used the e-learning environment that provided non-interactive video did not improve either. The findings suggest that it may be important to integrate interactive instructional video into e-learning systems.

Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches

Web content mining is intended to help people discover valuable information from large amount of unstructured data on the web. Movie review mining classifies movie reviews into two polarities: positive and negative. As a type of sentiment-based classification, movie review mining is different from other topic-based classifications. Few empirical studies have been conducted in this domain. This paper investigates movie review mining using two approaches: machine learning and semantic orientation. The approaches are adapted to movie review domain for comparison. The results show that our results are comparable to or even better than previous findings. We also find that movie review mining is a more challenging application than many other types of review mining. The challenges of movie review mining lie in that factual information is always mixed with real-life review data and ironic words are used in writing movie reviews. Future work for improving existing approaches is also suggested.

A Comparison of Classification Methods for Predicting Deception in Computer-Mediated Communication

The increased chance of deception in computer-mediated communication and the potential risk of taking action based on deceptive information calls for automatic detection of deception. To achieve the ultimate goal of automatic prediction of deception, we selected four common classification methods and empirically compared their performance in predicting deception. The deception and truth data were collected during two experimental studies. The results suggest that all of the four methods were promising for predicting deception with cues to deception. Among them, neural networks exhibited consistent performance and were robust across test settings. The comparisons also highlighted the importance of selecting important input variables and removing noise in an attempt to enhance the performance of classification methods. The selected cues offer both methodological and theoretical contributions to the body of deception and information systems research.

Ontology learning: state of the art and open issues

Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.

Discovering golden nuggets: data mining in financial application

With the increase of economic globalization and evolution of information technology, financial data are being generated and accumulated at an unprecedented pace. As a result, there has been a critical need for automated approaches to effective and efficient utilization of massive amount of financial data to support companies and individuals in strategic planning and investment decision-making. Data mining techniques have been used to uncover hidden patterns and predict future trends and behaviors in financial markets. The competitive advantages achieved by data mining include increased revenue, reduced cost, and much improved marketplace responsiveness and awareness. There has been a large body of research and practice focusing on exploring data mining techniques to solve financial problems. In this paper, we describe data mining in the context of financial application from both technical and application perspectives. In addition, we compare different data mining techniques and discuss important data mining issues involved in specific financial applications. Finally, we highlight a number of challenges and trends for future research in this area.

How the Semantic Web is Being Used: An Analysis of FOAF Documents

Semantic Web researchers have initially focused on the representation, development and use of ontologies but paid less attention to the social and structural relationships involved. The past year has seen a dramatic increase in the amount of published RDF documents using the Friend of a Friend (FOAF) vocabulary, providing a valuable resource for investigating how early Semantic Web adopters use this technology as well as build social networks. We describe an approach to identify, discover, and analyze FOAF documents. Over 1.5 million of FOAF documents are collected to show the variety and scalability of the web of FOAF documents. We analyzed the empirical usage of namespace and properties in the FOAF community, which helps the FOAF project in standardizing vocabularies. We also analyzed the social networks induced by those FOAF documents and revealed interesting patterns which can become powerful resource for outsourcing and justification of scientific knowledge.

The Impact of Individualism-Collectivism, Social Presence, and Group Diversity on Group Decision Making Under Majority Influence

Majority influence is the attempt by a majority of group members to impose their common position on group dissenters during group decision making. Because of globalization, the use of cross-cultural teams in group tasks is becoming increasingly common. The objective of this study was to investigate how national culture, social presence, and group diversity may affect majority influence in a group decision-making context. A total of 183 groups participated in a large-scale empirical experiment at multiple sites. The results show that the national culture of group minorities has a significant impact on majority influence and that the use of computer-mediated communication can reduce majority influence. The findings have both theoretical and practical implications for improving the outcome and the effectiveness of group decision making in cross-cultural environments.

Empowering collaborative commerce with Web services enabled business process management systems

Collaborative commerce (C-Commerce) is a set of technologies and business practices that allows companies to build stronger relationships with their trading partners through integrating complex and cross-enterprise processes governed by business logic and rules, as well as workflows. Business Process Management (BPM) is a key element of C-Commerce solutions for complex process coordination. It provides a mechanism to support e-businesses in modeling, deploying, and managing business processes that involve various applications with greater flexibility. Traditional BPM solutions often lack the capability to integrate external applications in that they have very limited support for interoperability. In recent years, Web services have emerged as a promising enabling technology for BPM in support of C-Commerce. Web services offer effective and standard-based means to improve interoperability among different software applications over Internet protocols. This paper aims to give an in-depth analysis of BPM and Web services in the context of C-Commerce. We propose an architecture for Web services enabled BPM in C-Commerce and provide technical insights into why Web services can enhance business process coordination. Finally, an implementation of a dynamic e-procurement application based on the proposed architecture is presented. With the advent of Web service standards and business process integration tools that support them, BPM systems enabled by Web services are empowering the development of more flexible and dynamic C-Commerce.

Social commerce research: An integrated view

Social commerce has quickly emerged as a new area of inquiry for both practitioners and researchers, suggesting the potential impacts of social media and social networking technologies and services in shaping commercial channels on and off the Internet. This essay starts by providing a brief overview of social commerce research and practice in light of the wide attention it has drawn in the industry. Then, we propose a research framework with an integrated view of social commerce that consists of four key components: business, technology, people, and information. The framework helps us understand the development of social commerce research and practice to date. Subsequently, we report some preliminary findings from a bibliometric study of academic and industry publications in social commerce to reveal recent trends and research topics, as well as some verification of the research framework. Finally, we discuss five articles in this special issue and categorize them in terms of the proposed social commerce research framework.