Using taxonomic indexing trees to efficiently retrieve SCORM-compliant documents in e-learning grids

[[abstract]]With the flourishing development of e-Learning, more and more SCORM-compliant teaching materials are developed by institutes and individuals in different sites. In addition, the e-Learning grid is emerging as an infrastructure to enhance traditional e-Learning systems. Therefore, information retrieval schemes supporting SCORM-compliant documents on grid environments are gaining its importance. To minimize the query processing time and content transmission time, our idea is to use a bottom-up approach to reorganize documents in these sites based on their metadata, and to manage these contents in a centralized manner. In this paper, we design an indexing structure named Taxonomic Indexing Trees (TI-trees). A TI-tree is a taxonomic structure and has two novel features: 1) reorganizing documents according to the Classification metadata such that queries by classes can be processed efficiently and 2) indexing dispersedly stored documents in a centralized manner which is suitable for common grid middleware. This approach is composed of a Construction phase and a Search phase. In the former, a local TI-tree is built from each Learning Object Repository. Then, all local TI-trees are merged into a global TI-tree. In the latter, a Grid Portal processes queries and presents results with estimated transmission time to users. Experimental results show that the proposed approach can efficiently retrieve SCORM-compliant documents with good scalability

Wiki-based rapid prototyping for teaching-material design in e-Learning grids

[[abstract]]Grid computing environments with abundant resources can support innovative e-Learning applications, and are promising platforms for e-Learning. To support individualized and adaptive learning, teachers are encouraged to develop various teaching materials according to different requirements. However, traditional methodologies for designing teaching materials are time-consuming. To speed up the development process of teaching materials, our idea is to use a rapid prototyping approach which is based on automatic draft generation and Wiki-based revision. This paper presents the approach named WARP (Wiki-based Authoring by Rapid Prototyping), which is composed of five phases: (1) requirement verification, (2) query expansion, (3) teaching-material retrieval, (4) draft generation and (5) Wiki-based revision. A prototype system was implemented in grid environments. The evaluation was conducted using a two-group t-test design. Experimental results indicate that teaching materials can be rapidly generated with the proposed approach. © 2007 Elsevier Ltd. All rights reserved

Using a performance-based skeleton to implement divisible load applications on grid computing environments

[[abstract]]Applications with divisible loads have such a rich source of parallelism that their parallelization can significantly reduce their total completion time on grid computing environments. However, it is a challenge for grid users, probably scientists and engineers, to develop their applications which can exploit the computing power of the grid. We propose a performance-based skeleton algorithm for implementing divisible load applications on grids. Following this skeleton, novice grid programmers can easily develop a high performance grid application. To examine the performance of programs developed by this approach, we apply this skeleton to implement three kinds of applications and conduct experiments on our grid test-bed. Experimental results show that programs implemented by this approach run more rapidly than those using conventional scheduling schemes

A Knowledge-based Approach to Retrieving Teaching Materials for Context-aware Learning

[[abstract]]With the rapid development of wireless communication and sensor technologies, ubiquitous learning has become a promising solution to educational problems. In context-aware ubiquitous learning environments, it is required that learning content is retrieved according to environmental contexts, such as learners' location. Also, a learning content retrieval scheme should be able to work with various instructional strategies for different learning activities. To solve the context-aware learning content retrieval problem, we propose a strategy-driven approach to derive content retrieval strategies from instructional strategies. Moreover, we construct a knowledge-based system to expand query keywords based on the derived strategies and then select relevant keywords according to geographical distance between entities of concept and learners. This system is composed of four components: knowledge transformation, query expansion, content retrieval and user interface. Besides, ontology construction algorithms, designed for teachers to easily build up ontology from course outlines, are applied to generate the rules of query expansion and the taxonomic index of learning object repository. The experimental results indicate that the proposed approach can increase the learning performance of students

Ontology-based content organization and retrieval for SCORM-compliant teaching materials in data grids

[[abstract]]With the rapid growth of e-Learning, a tremendous amount of learning content has been developed by numerous providers. Recently, the Sharable Content Object Reference Model (SCORM) has been widely accepted as a standard of e-Learning for users to share and reuse various teaching materials. Data grids, characterized by their goal to manage large-scale dataset, are promising platforms to support sharing of geographically dispersed learning content. However, current data grid standards have not provided complete solutions to content-based information retrieval. To increase the precision of content retrieval on data grids, our idea is to propose an ontology-based approach to organize and retrieve learning content in geographically dispersed repositories. We designed a layered architecture to enable learning content organization and retrieval on data grids, implemented in a metropolitan-scale grid environment. Experimental results show that the proposed approach can precisely retrieve SCORM-compliant learning content. © 2009 Elsevier B.V. All rights reserved

Web-based computer adaptive assessment of individual perceptions of job satisfaction for hospital workplace employees

<p>Abstract</p> <p>Background</p> <p>To develop a web-based computer adaptive testing (CAT) application for efficiently collecting data regarding workers' perceptions of job satisfaction, we examined whether a 37-item Job Content Questionnaire (JCQ-37) could evaluate the job satisfaction of individual employees as a single construct.</p> <p>Methods</p> <p>The JCQ-37 makes data collection via CAT on the internet easy, viable and fast. A Rasch rating scale model was applied to analyze data from 300 randomly selected hospital employees who participated in job-satisfaction surveys in 2008 and 2009 via non-adaptive and computer-adaptive testing, respectively.</p> <p>Results</p> <p>Of the 37 items on the questionnaire, 24 items fit the model fairly well. Person-separation reliability for the 2008 surveys was 0.88. Measures from both years and item-8 job satisfaction for groups were successfully evaluated through item-by-item analyses by using <it>t</it>-test. Workers aged 26 - 35 felt that job satisfaction was significantly worse in 2009 than in 2008.</p> <p>Conclusions</p> <p>A Web-CAT developed in the present paper was shown to be more efficient than traditional computer-based or pen-and-paper assessments at collecting data regarding workers' perceptions of job content.</p