A Study on Reusability of Adaptive Learning Object

• Main Authors: Hsiau-Wen Lin, 林曉雯
• Other Authors: 許輝煌
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/23917351650623746551

博士 === 淡江大學 === 資訊工程學系博士班 === 95 === One stumbling block to the establishment of E-Learning is on the time consuming tasks to create high quality digital contents that can be used in instruction. In the literature, reusability of learning objects is addressed as a potential solution toward the sharing of high quality contents. SCORM provides a generic and standard mechanism to enable learning objects changeable, reusable. SCORM defines metadata of learning objects, which is used as the elementary reusable components in E-Learning; SCORM defines sequence of learning objects, which is used for setting adaptive E-Learning; even provides a common architecture for the resolution, discovery, and sharing of learning objects in new proposed CORDRA. However, there is another big stumbling block for most course designers (e.g. teachers) who are not SCORM specialists to create a SCORM compliant learning objects. Thus, our investigation focuses on how to help course designers to create reusable learning objects facilely. First, we propose a mechanism called the MINE Metadata Wizard, by means of using user profiles, system extracted information, interactive questions, and influence rules to provide an automatic metadata generation mechanism to reduce the load of course designers. Second, the first technique for automatic sequencing testing is proposed to decrease the complexity of sequencing definition model in SCORM 2004. Petri Net is extended to make the course sequencing visible and avoid any irregular setting interrupting learning activity in advance. In addition, when course designers want to reuse exist learning objects, our extending CORDRA model provides systematic query results from huge repository. The evolution of reusing on a particular learning object in CORDRA can be tracked in a structure. We call the structure a Reusability Tree. As a consequence, reusable learning objects can be discovered and reused by many course designers.