A Petri nets-based approach to modeling SCORM sequence

[[abstract]]In order to adapt teaching in accordance with the abilities of individual students in the distance learning environment, more research emphasis is needed on constructing personalised courseware. The new version of SCORM 1.3 (scalable content object reference model) attempts to add the sequence concept into this course standard. The concept describes how the sequencing process is invoked, what occurs during the sequencing process and the potential outputs of the sequencing process. As a result, we apply the valuable features of Petri nets to decrease the complexity of the sequencing definition model in the SCORM 1.3 specification and construct a framework within various instructional strategies by piecing subnets together.[[conferencetype]]國際[[conferencedate]]20040627~20040630[[booktype]]紙

SCORM Sequencing Testing for Sequencing Control Mode

[[abstract]]In SCORM 2004 defines the sequencing information that describes how SCORM-conformant content may be sequenced to the learner through a set of learner or system-initiated navigation events. It provides users the ability to prescribe the intend learning sequencing strategy by themselves, but quit many completed definitions and lacking the testing mechanism for these authored sequencing information results in usual developers probably design the unreasonable or careless settings of SCORM sequencing. The detecting mechanism focuses on detecting improper setting of sequencing control mode elements applied to learning activities. An assistant truth table derived from the definitions of sequencing control mode elements and experiments verified with latest ADL runtime environment will be introduced.[[sponsorship]]淡江大學 Tamkang University; ADL-CO-Lab; National Central University; Institute Information Industry; Southern Taiwan University of Technology[[notice]]補正完畢[[conferencetype]]國際[[conferencetkucampus]]淡水校園[[conferencedate]]20060116~20060119[[booktype]]紙本[[conferencelocation]]臺北縣, 臺

[[alternative]]Hard SCORM(II)

計畫編號：NSC94-2524-S032-002研究期間：200505~200607研究經費：1,321,000[[sponsorship]]行政院國家科學委員

An Automated Adaptive Mobile Learning System Using Optimal Shortest Path Algorithms

Technological innovation opens the door to create a personal learning experience for any student. In this research, we discuss adaptive learning techniques and the style of learning that integrates existing learning techniques combined with new ideas. To create an effective user friendly learning environment, adaptive learning techniques should be used in order to identify the personal needs of students and reduce their individual knowledge gaps. The result will produce learning path containing relevant content that will provide a better learning direction for each student. This dissertation explores the opportunity of using adaptive learning techniques to identify the personal needs of each student by combining different learning styles, student profiles and individualized course content. By using a directed graph, we are able to represent an accurate picture of the course descriptions for online courses through computer-based implementation of various educational systems. E-learning (electronic learning) and m-learning (mobile learning) systems are modeled as a weighted directed graph where each node represents a course unit. The Learning Path Graph represents and describes the structure of the domain knowledge, including the learning goals, and all other available learning paths. In this research, we propose a system prototype that implements optimal adaptive learning path algorithms using students’ information from their profiles and their learning style. Our goal is to improve students’ learning performances through the m-learning system in order to provide suitable course contents sequenced in a dynamic form for each student