Peer Review

Designing a Resource Evolution Support System for Open Knowledge Communities

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Reviewed:: Mar 8, 2017 by Computer Science

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Ratings

Overall Rating:

Content Quality:

Effectiveness:

Ease of Use:

Overview:

The continuous generation and evolution of digital learning resources is important for promoting open learning and meeting the personalized needs of learners. In the Web 2.0 era, open and collaborative authoring is becoming a popular method by which to create vast personalized learning resources in open knowledge communities (OKCs). However, the essence of openness of OKCs also gives rise to concerns regarding the knowledge quality and non-orderliness of resource evolution. In this study, we design a resource evolution support system (RESS) called learning cell system (LCS) in one OKC. Two key issues, namely, the intelligent control of content evolution and the dynamic semantic associations between resources, are addressed by combining technologies of semantics, trust evaluation, rule-based reasoning, and association rule mining. One typical case is taken to illustrate the actual evolution process of learning resources assisted by RESS in LCS. The operating effect of this system shows that RESS can control content evolution and effectively build semantic associations among resources. Finally, the academic contribution to the OKCs, implications for educational practice, limitations, and future research plans are presented.

Type of Material:

Open Journal-Article

Recommended Uses:

This article can be used in formal teaching (in-class, online, hybrid) or for self-paced learning.

Technical Requirements:

A PDF viewer or standard browser

Identify Major Learning Goals:

Users will be able to learn about some new concepts, such as personalized learning resources in open knowledge communities (OKCs), a resource evolution support system (RESS) and learning cell system (LCS). The learning cell system is invented at Beijing Normal University, institution where the second author is from.

Target Student Population:

College and postgraduate students; researchers and practitioners in the field of computer science and learning technology.

Prerequisite Knowledge or Skills:

Familiarity with some main concepts: open educational resources, personalized learning resources, knowledge communities, etc.

Content Quality

Rating:
Strengths:: The article is well-written and describes new and original ideas, mainly how to create personalized learning resources in open knowledge communities. It also presents a new model of Content evolution of learning resources, and the architecture of the learning cell system.
Concerns:: It's a PDF article without much interactivity. Some of the charts and graphs are presented in Chinese. It is a research article and certain level of understanding in the subject is required.

Potential Effectiveness as a Teaching Tool

Rating:
Strengths:: It is a piece of reading material. The content aligns with the interests of students in computer science. Even though texts are dense, there are charts and graphs to illustrate the major points presented.
Concerns:: Some of the concepts are from the field of learning design and technology. Students in CS might not be familiar with them. There will be a learning curve. Instructors will need to guide them at the beginning. It does not have assessment items.

Ease of Use for Both Students and Faculty

Rating:
Strengths:: It bears all the strengths of a PDF document. Users will be able to search by keywords and also annotate it.
Concerns:: PDF documents are static and have a lack of interactivity. It might not appear engaging to some of the users.

Other Issues and Comments:: This paper covers content that intersects computer science and learning technology. It's a nice addition to the collection of materials in computer science.
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