Examining Effects of Technology-Assisted Learning on Learning Effectiveness and Satisfaction: A Quasi-Experimental Study

Examining students’ learning effectiveness and satisfaction is critical to the ultimate success of technology-assisted learning that has been deployed at a fast-growing pace. The accumulated results from prior research are mostly equivocal. Based on how technology-assisted learning may influence students’ learning process, we analyze technology-assisted learning and synthesize relevant prior research, and propose a factor model that explains learning effectiveness and satisfaction. We empirically test that model with a quasi-experiment that involves 212 university students, observing their learning of Adobe Photoshop. We test the hypothesized effects of technology-assisted learning and its moderating role in influencing students’ learning effectiveness and satisfaction. According to our results, the use of technology-assisted learning adversely affects student engagement. This, in turn, negatively influences their learning effectiveness and satisfaction. Student engagement in learning activities appears to mediate the impact of technology-assisted learning on learning effectiveness. Furthermore, the influence of technology-assisted learning on learning satisfaction is mediated by both student engagement and learning effectiveness. Technology-assisted learning shows no significant moderating effects on learning effectiveness or satisfaction. Our empirical results have several important implications for technology-assisted learning research and practice

Rethinking the Relationship between Ubiquitous Government and Electronic Government

The advancement of information and communications technology (ICT) has revolutionized the way governments deliver public services, thereby fostering the development of egovernment in general and attracting increasing interests in ubiquitous government. While egovernment encourages online services which can substitute or complement conventional offline services, the presence of digital divide can create a gap in such technology-enabled service provision. While different channel management strategies have been adopted to move citizens to online channels, these strategies are not effective when digital divide prevails. More importantly, the conventional view about “ubiquitous” is rather technical in nature and consequently the conceptualization or development of ubiquitous government has been led astray by this inherent nature. In this paper, we examine e-government, channel management strategy, digital divide and ubiquitous government. According to our analysis, ubiquitous government should not be considered as a subset of e-government. We redefine ubiquitous government which in effect may be viewed as a superset of e-government. Building upon our revised definition, we plan to further extend the scope of the Ubiquitous Government Development Model and illustrate our conceptualization and analysis using the experience of governments with different paces of e-government development. The case study methodology would be used

Examining the Business-Technology Alignment in Government Agencies: A Study of Electronic Record Management Systems in Taiwan

For e-government to succeed, government agencies must manage their records and archives of which the sheer volume and diversity necessitate the use of electronic record management systems (ERMS). Using an established business–technology alignment model, we analyze an agency’s strategic alignment choice and examine the outcomes and agency performance associated with that alignment. The specific research questions addressed in the study are as follows: (1) Do strategic alignment choices vary among agencies that differ in purpose or position within the overall government hierarchy? (2) Do agencies’ alignment choices lead to different outcomes? and (3) Does performance in implementing, operating, and using ERMS vary among agencies that follow different alignment choices? We conducted a large-scale survey study of 3,319 government agencies in Taiwan. Our data support the propositions tested. Based on the findings, we discuss their implications for digital government research and practice

Examining Gender Effects in Technology Acceptance by Arabian Workers: A Survey Study

While information technology is increasingly ubiquitous globally, the pace at which the technology has disseminated varies in different regions. We study technology acceptance by working individuals in the Arabian region, which has recorded substantial growths in technology infrastructure and deployments. We focus on gender because the Arabian region has a long-standing cultural tradition and entrenched social norms that distinctly define the gender roles. We develop a factor model, premised on the theory of planned behavior and the technology acceptance model, which explains the focal technology acceptance phenomenon. We test the model and the hypotheses with the responses from 1,088 Arabian workers from 56 firms that participate in our survey voluntarily. The model accounts for a significant portion of the variances in the workers’ intentions to use computer technology. We find that gender moderates the effect of subjective norms on intention (significantly stronger for males than for female workers) and the influence of perceived usefulness on attitude (significantly stronger for male than for female workers). However, the moderating role of gender appears insignificant on other relationships we hypothesized. Our findings have several important implications for both research and practice, which we will discuss in this paper

Examining the Moderating Role of Gender in Arabian Workers’ Acceptance of Computer Technology

Even as information technology becomes globally ubiquitous, the pace of its dissemination varies across regions. For example, technology acceptance by ordinary workers in the Arabian region is generally slow, though its diffusion has recently exhibited substantial improvements. The research model proposed herein, based in the theory of planned behavior and the technology acceptance model, focuses on the effects of gender, because long-standing Saudi Arabian culture traditions and entrenched social norms define distinct gender roles. The test of the model and its associated hypotheses involves voluntary responses from 1,088 Arabian workers of fifty-six firms. The results show that the model can explain a significant portion of variance in workers’ intentions to use computer technology. Perceived usefulness seems to have the strongest impact on intention, followed by perceived behavioral control and subjective norms. In addition, gender moderates the effect of subjective norms on intentions and the influence of perceived usefulness on attitude, more prominently among male than among female workers. Overall, the findings imply relative differences in the explanatory power of prevalent theoretical models across different socio-cultural contexts and point to the important role of gender in technology acceptance. They also offer implications for research and practice

Preserving User Preferences in Document-Category Management: An Ontology-based Evolution Approach

Preserving the user’s preference in document-category management is essential because it affects his/her search efficiency, cognitive processing load, and satisfaction. Prior research has investigated automated document category evolution by using lexicon-based documentcategory evolution techniques which take into account the document categories previously created by the user. However, comparing documents at the lexical level cannot solve word mismatch or ambiguity problems effectively. To address such problems inherent to the lexicon-based approach, we propose an ONtology-based Category Evolution (ONCE) technique, which uses an appropriate ontology to support document-category evolution at the conceptual level rather than at the lexical level. Specifically, we develop an Ontology Enrichment (OE) technique for automatic leaning of concept descriptors in the adopted ontology. We empirically evaluate the effectiveness of the proposed ONCE technique, using a lexicon-based document-category evolution technique (i.e., CE2) and the hierarchical agglomerative clustering (HAC) technique for benchmark purposes. According to our empirical results, ONCE appears more effective than CE2 and HAC, and achieves higher clustering recall and precision

The Impact of Service System Design and Flow Experience on Customer Satisfaction in Online Financial Services

Prior research examines customer satisfaction in retailing and e-commerce settings, yet online financial services have received little research attention. To understand customer satisfaction with this fast-growing service, this study investigates the role of flow experience, a sensation that occurs as a result of significant cognitive involvement. The study examines how service system characteristics affect the cognitive states of the flow experience, which determines customer satisfaction. The flow construct and total experience design suggest a structural model that is empirically tested using responses from a large sample of online investors. In support of the model and most of the hypotheses it suggests, the empirical results clarify the important antecedents and consequence of flow experience in online financial services and suggest the viability of using a dual-layer experience construct to investigate customer satisfaction. These findings can help researchers and service providers understand when, where, and how flow experience is formulated in online financial services

Technology-assisted learning and learning style: A longitudinal field experiment

From a student\u27s perspective, technology-assisted learning provides convenient access to interactive contents in a hyperlinked multimedia environment that allows increased control over the pace and timing of the presented material. Previous research examining different aspects of technology-assisted learning has found equivocal results concerning its effectiveness and outcomes. We extend prior studies by conducting a longitudinal field experiment to compare technology-assisted with face-to-face learning for students\u27 learning of English. Our comparative investigation focuses on learning effectiveness, perceived course learnability, learning-community support, and learning satisfaction. In addition, we analyze the effects of different learning styles in moderating the effectiveness of and satisfaction with technology-assisted learning. Overall, our results show significantly greater learning effectiveness with technology-assisted learning than with conventional face-to-face learning. Learning style has noticeable influences on the effectiveness and outcomes of technology-assisted learning. We also observe an apparently important interaction effect with the medium for delivery, which may partially explain the equivocal results of previous research. © 2007 IEEE

THE STRATEGIC CO-ALIGNMENT FOR IMPLEMENTING INFORMATION SYSTEMS IN E-GOVERNMENT

Regulating agency in government, i.e. regulator, must co-align its information systems (IS) planning strategy with executing agencies, i.e. executors, for better e-government performance. Using an established strategic co-alignment model, we analyze the mutual participating strategies between regulator and executors and examine the outcomes and performance associated with that co-alignment choice. After conducting a large-scale survey study of government agencies in Taiwan, the co-alignment relationship between e-government IS policy regulator and executor is examined. Based on the findings, we discuss their implications for e-government research and practice

Positive Example Learning for Content-Based Recommendations: A Cost-Sensitive Learning-Based Approach

Existing supervised learning techniques can support product recommendations but are ineffective in scenarios characterized by single-class learning; i.e., training samples consisted of some positive examples and a much greater number of unlabeled examples. To address the limitations inherent in existing single-class learning techniques, we develop COst-sensitive Learning-based Positive Example Learning (COLPEL), which constructs an automated classifier from a singleclass training sample. Our method employs cost-proportionate rejection sampling to derive, from unlabeled examples, a subset likely to feature negative examples, according to the respective misclassification costs. COLPEL follows a committee machine strategy, thereby constructing a set of automated classifiers used together to reduce probable biases common to a single classifier. We use customers’ book ratings from the Amazon.com Web site to evaluate COLPEL, with PNB and PEBL as benchmarks. Our results show that COLPEL outperforms both PNB and PEBL, as measured by its accuracy, positive F1 score, and negative F1 score