Managing items and knowledge components: domain modeling in practice

PELÁNEK, Radek. Managing items and knowledge components: domain modeling in practice. Educational Technology Research and Development. 2020, vol. 68, No 1, p. 529-550. ISSN 1042-1629. Available from: https://dx.doi.org/10.1007/s11423-019-09716-w.

Basic information

• Original name: Managing items and knowledge components: domain modeling in practice
• Authors: PELÁNEK, Radek (203 Czech Republic, guarantor, belonging to the institution).
• Edition: Educational Technology Research and Development, 2020, 1042-1629.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 3.565
• RIV identification code: RIV/00216224:14330/20:00115525
• Organization unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1007/s11423-019-09716-w
• UT WoS: 000513352400024
• Keywords in English: domain modeling; student modeling; adaptivity; scalability; knowledge component; system developoment
• Tags: International impact, Reviewed

Abstract

Adaptive learning systems need large pools of examples for practice—thousands of items that need to be organized into hundreds of knowledge components within a domain model. Domain modeling and closely related student modeling are intensively studied in research studies. However, there is a gap between research studies and practical issues faced by developers of scalable educational technologies. The aim of this paper is to bridge this gap by connecting techniques and notions used in research papers to practical problems in development. We put specific emphasis on scalability—on techniques that enable relatively cheap and fast development of adaptive learning systems. We summarize conceptual questions in domain modeling, provide an overview of approaches in the research literature, and discuss insights based on the development and analysis of a widely used system. We conclude with recommendations for both developers and researchers in the area of adaptive learning systems.