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ACOMS+ 및 학술지 리포지터리 설명회

  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

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A Structure of Personalized e-Learning System Using On/Off-line Mixed Estimations Based on Multiple-Choice Items

INTERNATIONAL JOURNAL OF CONTENTS / INTERNATIONAL JOURNAL OF CONTENTS, (P)1738-6764; (E)2093-7504
2009, v.5 no.1, pp.51-55
오용선 (목원대학교)

Abstract

In this paper, we present a structure of personalized e-Learning system to study for a test formalized by uniform multiple-choice using on/off-line mixed estimations as is the case of Driver’s License Test in Korea. Using the system a candidate can study toward the license through the Internet (and/or mobile instruments) within the personalized concept based on IRT(item response theory). The system accurately estimates user’s ability parameter and dynamically offers optimal evaluation problems and learning contents according to the estimated ability so that the user can take possession of the license in shorter time. In order to establish the personalized e-Learning concepts, we build up 3 databases and 2 agents in this system. Content DB maintains learning contents for studying toward the license as the shape of objects separated by concept-unit. Item-bank DB manages items with their parameters such as difficulties, discriminations, and guessing factors, which are firmly related to the learning contents in Content DB through the concept of object parameters. User profile DB maintains users’ status information, item responses, and ability parameters. With these DB formations, Interface agent processes user ID, password, status information, and various queries generated by learners. In addition, it hooks up user’s item response with Selection & Feedback agent. On the other hand, Selection & Feedback agent offers problems and content objects according to the corresponding user’s ability parameter, and re-estimates the ability parameter to activate dynamic personalized learning situation and so forth.

keywords
personalized e-Learning, on/off-line mixed estimation, IRT(item response theory), user ability parameter, item parameters

INTERNATIONAL JOURNAL OF CONTENTS