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A Fine-Grained Outcome-Based Learning Path Model

Yang, Fan; Li, Frederick W.B.; Lau, Rynson W.H.

A Fine-Grained Outcome-Based Learning Path Model Thumbnail


Fan Yang

Rynson W.H. Lau


A learning path (or curriculum sequence) comprises steps for guiding a student to effectively build up knowledge and skills. Assessment is usually incorporated at each step for evaluating student learning progress. SCORM and IMS-LD have been established to define data structures for supporting systematic learning path construction. Although IMS-LD includes the concept of learning activity, no facilities are offered to help define its semantics, and pedagogy cannot be properly formulated. In addition, most existing work for learning path generation is content-based. They only focus on what learning content is delivered at each learning path step, and pedagogy is not incorporated. Such modeling limits the assessment of student learning outcome only by the mastery level of learning content. Other forms of assessments, such as generic skills, cannot be supported. In this paper, we propose a fine-grained outcome-based learning path model allowing learning activities and their assessment criteria to be formulated by Bloom's Taxonomy. Therefore, pedagogy can be explicitly defined and reused. Our model also supports the assessment of both subject content and generic skills related learning outcomes, providing more comprehensive student progress guidance and evaluation.


Yang, F., Li, F. W., & Lau, R. W. (2014). A Fine-Grained Outcome-Based Learning Path Model. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(2), 235-245.

Journal Article Type Article
Acceptance Date Apr 26, 2013
Online Publication Date Nov 13, 2013
Publication Date Feb 1, 2014
Deposit Date Jul 6, 2016
Publicly Available Date Jul 6, 2016
Journal IEEE Transactions on Systems, Man, and Cybernetics: Systems.
Print ISSN 2168-2216
Electronic ISSN 2168-2232
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 44
Issue 2
Pages 235-245


Accepted Journal Article (8.4 Mb)

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