• Knowledge Management Research
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• v.14 no.1
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• pp.121-132
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• 2013

In education, innovative ways of teaching and learning are always under development and keep being proposed with advanced concepts since the ancient times. Student-centered learning, problem-based learning and cooperative learning have been three major trends under development in secondary education research and practice more than a decade or so. Combined with advanced information and communication technologies, these trends will greatly transform the way we teach and learn in classroom environment and may change the classroom environment itself, into a more interactive and self-centered coaching type environment. In this study, a smart environment that utilizes advanced information technology devices and network is conceptualized, accommodating requirements contained and proposed in the recent trendy pedagogies. Pedagogical cases discussed in these trends are analyzed in detail, producing requirements for such a learning and coaching environment. These requirements are modeled using unified modeling language, leading to a proposal of a basic architecture for an information system supporting this environment.

• International Commerce and Information Review
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• v.10 no.4
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• pp.81-96
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• 2008

uEFL is an open source solution to integrate general business/learning activities and processes. uEFL is originally developed to adopt LD (Learning Design) specification, which represents learning as various combination of learning activities with learning conditions and outcomes. Learning activities are described with participant's role, learning environment, and contextual sequence. This viewpoint resembles BPM (Business Process Modeling). uEFL can convert LD to BPM description. uEFL engine can run converted LD activity with other business activities. This paper presents 4 templates and 2 sample models for uEFL. The templates and models will show how learning activities can be integrated with business activities efficiently.

• International Commerce and Information Review
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• v.9 no.4
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• pp.3-19
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• 2007

The purpose of this paper is to study the pattern of interactive learning in strategic networks. Interactive learning is defined as the exchange and sharing of knowledge resources conducive to innovation between an innovator firm, its suppliers, and/or its customers. The strength of internal knowledge resources can either hamper or facilitate levels of interactive learning. We assume that more complex innovative activities urge firms to co-ordinate and exchange information between users and producers, which implies a higher level of interactive learning. To test our theoretical claims, we estimated the level of interactive learning of firms in strategic networks with: (1) their customers, (2) their suppliers. Theses analyses allow a comparison of the antecedents of interactive learning of firms participating in strategic networks. Our findings suggest that interactive learning with customers is positively affected by company's capabilities and value-created activities, and with supplies is positively affected by value-created activities and technology innovation centers.

• The Journal of Information Systems
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• v.15 no.4
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• pp.171-188
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• 2006

Information technology and the Internet have had a dramatic effect on education method and individual life. Universities and companies we making large investments in e-Learning applications but are hard to pressed to evaluate the success of their e-Learning systems. e-Learning can be seen as not only one of Internet based information systems which can provide education services but also one of teaching-teaming methods which can implement self-directed teaming. This paper tests the updated model of information system success proposed by Delone and McLean using a field study of a e-Learning. The five dimensions - information quality, system quality, service quality, user satisfaction, net benefit - of the updated model are parsimonious framework for organizing the e-learning success metrics identified in the literature. Questionaires are collected from 107 students who are enrolling a e-learning class using online survey. The model is tested using SPSS and LISREL. The results show that information quality and service quality are significant predictors of user satisfaction with the e-Learning system but system quality is not. Also user satisfaction is found to be a strong predictor of the learning performance. This strong association between user satisfaction and teaming performance suggests that user satisfaction may serve as a valid surrogate for teaming performance. Empirical testing of the updated DeLone & McLean model should therefore be extended to cover a wider variety of systems.

• Journal of information and communication convergence engineering
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• v.14 no.2
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• pp.97-105
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• 2016

With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1738-1764
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• 2019

With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.

• Asia pacific journal of information systems
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• v.31 no.1
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• pp.43-75
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• 2021

Clearly, the smartphone is increasingly playing a greater role in everyday life, thus providing opportunities to evaluate how well the use of the smartphone meets the requirements of undergraduate students in independent learning outside of a classroom setting. This study used the task-technology fit (TTF) model to explore the effectiveness of smartphone usage to interact with learning materials in independent learning outside of classrooms, the need for smartphone support, and the fit of devices to tasks as well as performance. First, the study used interviews, observation, and survey data to identify what are the most important constructs of smartphones that stimulate students to interact with learning materials in independent learning outside of classrooms. Based on the findings from the exploratory study and Task Technology Fit theory, we postulated the Navigation design, Ergonomic design, Content support, and Capacity as the essential dimension of the smartphone construct. Then, we proposed a research model and empirically tested hypotheses with the structural model analysis. The results reveal a significant positive impact of task and technology on TTF for smartphone usage to interact with learning materials in independent learning outside of classrooms; it also confirmed the TTF and performance have a direct effect on actual use.

• Asia pacific journal of information systems
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• v.17 no.1
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• pp.33-58
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• 2007

Quiet often, an organization tries to grapple with inconsistent and partial information to generate relevant information to support decision making and action. As such, an organization scans the environment interprets scanned data, executes actions, and learns from feedback of actions, which boils down to computational interpretations and learning in terms of machine learning, statistics, and database. The ExOM proposed in this paper is geared to facilitate such knowledge discovery found in large databases in a most flexible manner. It supports a broad range of learning and classification styles and integrates them with traditional database functions. The learning and classification components of the ExOM are tightly integrated so that learning and classification of objects is less burdensome to ordinary users. A brief sketch of a strategy as to the expressiveness of terminological language is followed by a description of prototype implementation of the learning and classification components of the ExOM.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.737-767
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• 2023

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.742-756
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• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.