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Galina Kofman

dblp:70/5009 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 77% Medical and health informatics · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics
distributed learning
0.012006
MAGICAL demonstration: system for automated metadata generation for instructional content · ACM Multimedia 2006

Methods — techniques the papers use, named apart from their topics

video analysis · 0.1metadata extraction · 0.1document analysis · 0.1
YearPublicationVenuePosition
2006 MAGICAL demonstration: system for automated metadata generation for instructional content
abstract
The "Tools for Automatic Generation of Learning Object Metadata" project addresses the requirement of developing advanced distributed learning delivery architecture and services for a large US government agency. We have developed a Webbased system called MAGIC (Metadata Automated Generation for Instructional Content) to assist content authors and course developers in generating metadata for learning objects and information assets to enable wider reuse of these objects across departments and organizations. Using the MAGIC system, content authors review and edit automatically-generated metadata sufficient to register and describe their assets for use and discovery in current and future distributed learning applications complying with the ADL SCORM standard. Course developers can use the system to assist in the conversion of existing courses to SCORM format or in developing new SCORM courses. The MAGIC system includes software tools to analyze and extract descriptive metadata from instructional videos, training documents, and other information assets. The tools generate some of the most critical SCORM metadata completely automatically. Benefits of MAGIC include easier reuse and repurposing, improved interoperability, and more timely registration of content for use by course developers. In this paper, we describe the system architecture, analysis tools developed, and services supported. A live demonstration of the system illustrating several use cases of the system will be presented at the conference, with a discussion of results from user studies and evaluation of the system.
Chitra Dorai, Robert G. Farrell, Amy Katriel, Galina Kofman, Ying Li 0121, Youngja Park
ACM Multimedia4