Browsing by Author "Su, JM"

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A content management scheme in a SCORM compliant learning object repository
(2005-09-01)
Su, JM
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Tseng, SS
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Wang, CY
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Lei, YC
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Sung, YC
With the rapid development of the Internet, e-learning systems have become more and more popular. For sharing and reusing teaching materials in different c-learning system, the Sharable Content Object Reference Model (SCORM) has become the most popular international standard among the existing ones. In an e-learning system, teaching materials are usually stored in a database, called the Learning Object Repository (LOR). In the LOR, a huge amount of SCORM teaching materials, including associated learning objects, will result in management problems in a wired/wireless environment. Recently, the SCORM organization has focused on devising ways to efficiently maintain, search. and retrieve desired learning objects in LORs for users. This effort is referred to as the Content Object Repository Discovery and Resolution Architecture (CORDRA). In this paper, we propose a management approach, called the Level-wise Content Management Scheme (LCMS), that can be used to efficiently maintain, search, and retrieve learning contents from a SCORM compliant LOR. LCMS includes two phases: the Construction phase and Search phase. In the former, the content structure of SCORM teaching materials (Content Package) is first transformed into a tree-like structure. called a Content Tree (CT), to represent each piece of teaching material. Based on Content Trees (CTs), the proposed Level-wise Content Clustering Algorithm (LCCAIg) then creates a multistage graph showing relationships among learning objects (LOs), e.g.. a Directed Acyclic Graph (DAG), called the Level-wise Content Clustering Graph (LCCG). The LCCAIg determines the relationships among LOs in different teaching materials by clustering all of the LOs for each level from bottom to top, according to a similarity measure. Moreover, a maintenance strategy is employed to rebuild the LCCG if necessary by monitoring the condition of each node within the LCCG. The latter employs the LCCG Content Searching Algorithm (LCCG-CSAlg) to traverse the LCCG and retrieve desired learning content with both general and specific LOs, according to queries sent by users in the wire/wireless environment. Some experiments have been done conducted to test the proposed scheme, and the results are reported here.
A new approach for constructing the concept map
(2004)
Sue, PC
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Weng, JF
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Su, JM
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Tseng, SS
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資訊工程學系
For achieving the adaptive learning, a predefined concept map of a course is often used to provide adaptive learning guidance for learners. However, it is difficult and time consuming to create the concept map of a course. Thus, how to automatically create a concept map of a course becomes an interesting issue. In this paper, we propose a Two-Phase Concept Map Construction (TP-CMC) approach to automatically construct the concept map by learners' historical testing records. Phase I is used to preprocess the testing records. We apply Fuzzy Set Theory to transform the numeric testing records of learners into symbolic, apply Education Theory (Item Analysis for Norm-Referencing) to further refine it, and apply Data Mining approach to find its grade fuzzy association rules. Then, in Phase 2, based upon our observation in real learning situation, we use multiple rule types to further analyze the mined rules and then propose a heuristic algorithm to automatically construct the concept map. Finally, the Redundancy and Circularity of the concept map constructed are also evaluated.
Adaptive learning environment to meet pedagogical needs
(2004-11-01)
Su, JM
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Tseng, SS
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Chen, CT
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Tsai, WN
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資訊工程學系
With the vigorous development of the Internet, e-learning systems have become more and more popular. Currently, to solve the issue of sharing and reusing teaching materials in different e-learning systems, several standard formats, including SCORM, IMS, LOM, AICC etc., have been proposed by international organizations. Among these international standards, the Sharable Content Object Reference Model (SCORM) has become the most popular standard in recent years. In the SCORM standard, Sequencing and Navigation (SN), which is based on the concepts of learning activities, defines the course sequencing behavior, which controls the sequencing, selecting and delivering of the course, and organizes the content into a hierarchical structure, namely, an Activity Tree (AT). For a large-scale learning activity, the Activity Tree will become too complex to manage and reuse, as will its sequencing behavior rules. Moreover, the lack of inter-relations among Activity Trees also makes reusing and integrating them hard. This implies that the scalability and flexibility of an adaptive learning system will be limited. Therefore, how to create, represent and maintain an AT and associated sequencing definitions is our concern. In addition, for a personalized learning environment, how to extend the structure of an AT with Pedagogical Theory has also become an important issue. Therefore, in this paper, we extend and modularize the structure of an AT by applying Pedagogical Theory and the concept of the Object Oriented Methodology, respectively. Thus, we first propose a novel model, the Instructional Activity Model (IAM), which is composed of related AT nodes. Each AT node in IAM is modularized as a learning unit with inter-relations and specific attributes, which can be easily managed, reused, and integrated. We also propose an AT Selection algorithm with a pedagogical strategy for traversing IAM in order to generate dynamic learning content for the learner. IAM with its scalability and flexibility can apply appropriate pedagogical theories to meet specific needs by means of extension scheme. Finally, we propose a systematic approach to fast and easily construction of IAM using traditional course resources.
Adaptive watermarking using relationships between wavelet coefficients
(2005)
Chen, YH
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Su, JM
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Fu, HC
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Huang, HC
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Pao, HT
In this paper, we propose an approach that hides watermarks in relationships between wavelet coefficients. This approach would minimize the perceptual distortion of watermarked images, measured with PSNR or just noticeable distortion (JND). Therefore, the strength of watermarks can be enlarged to increase the robustness of the watermarks, while keeping the quality of watermarked images visually accepted. Experimental results show that the watermark is still detectable after common image processing operations such as JPEG compression, Gaussian filtering and sharpening.
An object based authoring tool for creating SCORM compliant course
(2005)
Su, JM
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Tseng, SS
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Weng, JF
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Chen, KT
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Liu, YL
In recent years, how to reuse and share the teaching materials and instructional experiences among e-learning systems become more and more important. Sharable Content Object Reference Model (SCORM), the most popular international teaching materials standard, uses the metadata to specify the structure of every teaming object and proposes the content aggregation scheme to package these objects with XML language format. In 2004, the Sequencing and Navigation (SN) in SCORM 2004 also define the sequencing rules which can be used to control the sequencing, selecting and delivering of teaming activity for the desired, sequencing behavior. Learning contents in SN is organized into a hierarchical structure, namely Activity Tree (AT). However, for teachers/instructional designers, it is hard to understand the complicated sequencing rules in SN much less using it to construct a SCORM course with desired teaming guidance. Therefore, how to provide a user-friendly authoring tool to efficiently construct SCORM compliant course becomes an important issue. Therefore, in this paper, based upon the concept of Object Oriented Methodology (OOM), the complicated sequencing rules can be modularized into several basic components with corresponding sequencing rules in SN, called Sequencing Object (SO). Each SO is like a middleware, which represents a basic sequencing building block as a cluster in activity tree (AT) with some corresponding sequencing rules on the SCORM view and represents a basic sequencing graph on the users' view. By appropriately combining these SOs, users can easily construct a complex structure of course with desired teaming guidance. Then, a transformation algorithm is also proposed to firstly transform the created graph based course structure into Activity Tree (AT) with related sequencing rules, and then package the transformed activity tree and related physical teaming resources into a SCORM compliant course file. Moreover, the authoring tool can be easily upgraded by importing a new SO instead of redesigning the tool only for new SCORM version. Finally, a SCORM compliant course built by the object based authoring tool is executed on the SCORM RTE system. The results show that our approach is workable and efficient.
Automated information. mining on multimedia TV news archives
(2005)
Lai, PS
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Cheng, SS
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Sun, SY
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Huang, TY
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Su, JM
This paper addresses an integrated information mining techniques for multimedia TV-news archive. The utilizes techniques from the fields of acoustic, image, and video analysis, for information retrieval on news story title, newsman and scene identification. The goal is to construct a compact yet meaningful abstraction of broadcast news video, allowing users to browse through large amounts of data in a non-linear fashion with flexibility and efficiency. By using acoustic analysis, the system can classify video into news versus commercials, with 90% accuracy on a data set of 400 hours TV-news recorded off the air from July 2003 to August of 2004. By applying speaker identification and/or image detection techniques, each news stories can be segmented with an accuracy of 96%. On screen captions or subtitles are recognized by OCR techniques to produce the text title of each news stories. The extracted title words can be used to link or to navigate more related News contents on the WWW. In cooperation with facial and scene analysis and recognition techniques, OCR results can provide users with multimodality query for specific news stories. Some experimental results are presented and discussed for the system reliability and performance evaluation and comparison.
Constructing SCORM compliant course based on high-level Petri nets
(2006-01-01)
Su, JM
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Tseng, SS
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Chen, CY
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Weng, JF
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Tsai, WN
With rapid development of the Internet, e-learning system has become more and more popular. Currently, to solve the issue of sharing and reusing of teaching materials in different e-learning system, Sharable Content Object Reference Model (SCORM) is the most popular standard among existing international standards. In SCORM standard, the Sequencing and Navigation (SN) defines the course sequencing behavior, which controls the sequencing, selecting and delivering of a course, and organizes the content into a hierarchical structure, namely Activity Tree (AT). However, the structures with complicated sequencing rules of Activity Tree (AT) in SCORM make the design and creation of course sequences hard. Therefore, how to provide a user-friendly authoring tool to efficiently construct SCORM compliant course becomes an important issue. However, before developing the authoring tool, how to provide a systematic approach to analyze the sequencing rules and to transform the created course into SCORM compliant are our concerns. Therefore, in this paper, based upon the concept of Object Oriented Methodology (OOM), we propose a systematic approach, called Object Oriented Course Modeling (OOCM), to construct the SCORM compliant course. High-Level Petri Nets (HLPN), which is a powerful language for system modeling and validation, are applied to model the basic sequencing components, called Object-Oriented Activity Tree (OOAT), for constructing the SCORM course with complex sequencing behaviors. Every OOAT as a middleware represents a specific sequencing behavior in teaming activity and corresponding structure with associated sequencing rules of AT in SCORM. Thus, these OOATs can be efficiently used to model and construct the course with complex sequencing behaviors for different teaming guidance. Moreover, two algorithms, called PN2AT and AT2CP, are also proposed to transform HLPN modeled by OOATs into a tree-like structure with related sequencing rules in Activity Tree (AT) and package the AT and related physical teaming resources into a SCORM compliant course file described by XML language, respectively. Finally, based upon the OOCM scheme, a prototypical authoring tool with graphical user interface (GUI) is developed. For evaluating the efficiency of the OOCM approach compared with existing authoring tools, an experiment has been done. The experimental results show that the OOCM approach is workable and beneficial for teachers/ instructional designers. (c) 2005 Elsevier B.V. All rights reserved.
Learning portfolio analysis and mining for SCORM compliant environment
(2006)
Su, JM
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Tseng, SS
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Wang, W
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Weng, JF
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Yang, JTD
With vigorous development of the Internet, e-learning system has become more and more popular. Sharable Content Object Reference Model (SCORM) 2004 provides the Sequencing and Navigation (SN) Specification to define the course sequencing behavior, control the sequencing, selecting and delivering of course, and organize the content into a hierarchical structure, namely Activity Tree. Therefore, how to provide customized course according to individual learning characteristics and capabilities, and how to create, represent and maintain the activity tree with appropriate associated sequencing definition for different learners become two important issues. However, it is almost impossible to design personalized learning activities trees for each learner manually. The information of learning behavior, called learning portfolio, can help teacher understand the reason why a learner got high or low grade. Thus, in this paper, we propose a Learning Portfolio Mining (LPM) Approach including four phases: 1. User Model Definition Phase: define the learner profile based upon existing articles and pedagogical theory. 2. Learning Pattern Extraction Phase: apply sequential pattern mining technique to extract the maximal frequent learning patterns from the learning sequence, transform original learning sequence into a bit vector, and then use distance based clustering approach to group learners with good learning performance into several clusters. 3. Decision Tree Construction Phase: use two third of the learner profiles with corresponding cluster labels as training data to create a decision tree, and the remainings are the testing data. 4. Activity Tree Generation Phase: use each created cluster including several learning patterns as sequencing rules to generate personalized activity tree with associated sequencing rules of SN. Finally, for evaluating our proposed approach of learning portfolio analysis, an experiment has been done and the results show that generated personalized activity trees with sequencing rules are workable and beneficial for learners.