iLOG: A Framework for Automatic Annotation of Learning Objects with Empirical Usage Metadata

Autor/inn/en	Miller, L. D.; Soh, Leen-Kiat; Samal, Ashok; Nugent, Gwen
Titel	iLOG: A Framework for Automatic Annotation of Learning Objects with Empirical Usage Metadata
Quelle	In: International Journal of Artificial Intelligence in Education, 21 (2012) 3, S.215-236 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.3233/JAI-2012-024
Schlagwörter	Computer Science Education; Metadata; Internet; Artificial Intelligence; Synchronous Communication; Programming; Computer Software; Computer Software Evaluation; Computer System Design; Online Searching; Integrated Learning Systems; Use Studies; Educational Resources; Information Retrieval; Access to Information; Validity; Automation; Standards; College Instruction; Federal Aid + Suchen Sie Ihr Suchwort? Computer science lessons; Informatikunterricht; Metadaten; Künstliche Intelligenz; Programmierung; Softwareanalyse; Online-Recherche; Benutzerschulung; Bildungsmittel; Gültigkeit; Standard; Hochschullehre
Abstract	Learning objects (LOs) are digital or non-digital entities used for learning, education or training commonly stored in repositories searchable by their associated metadata. Unfortunately, based on the current standards, such metadata is often missing or incorrectly entered making search difficult or impossible. In this paper, we investigate automating metadata generation for SCORM-complaint LOs based on user interactions with the LO and static information about LOs and users. Our framework, called the Intelligent Learning Object Guide (iLOG), involves real-time tracking of each user sessions (an LO Wrapper), offline data mining to identify key attributes or patterns on how the LOs have been used as well as characteristics of the users (MetaGen), and the selection of these findings as metadata. Mechanisms used in the data mining include data imputation via clustering, association rule mining, and feature selection ensemble. This paper describes the methodology of automatic annotation, presents the results on the evaluation and validation of the algorithms, and discusses the resulting metadata. We have deployed our iLOG implementation for five LOs in introductory computer science topics and collected data for over 1400 sessions. We demonstrate that iLOG successfully tracks user interactions that can be used to automate the generation of meaningful empirical usage metadata for different stakeholder groups including learners and instructors, LO developers, and researchers. (Contains 2 figures and 5 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10