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Improve Learning With The Significant Learning Taxonomy

Authors: Larita Killian;

Improve Learning With The Significant Learning Taxonomy

Abstract

For decades, educators and accreditation bodies have used Bloom’s (1956) taxonomy of educational objectives to design and assess instruction. An alternative, Fink’s (2013) significant learning taxonomy, may be preferable for some purposes. The SLT goes beyond Bloom’s taxonomy and encourages instructors to incorporate new learning dimensions into a course. The SLT is simpler, more accessible, and especially effective at incorporating ethics. For typical instructors who must balance teaching with research and service, the SLT is a proven and accessible route to improved learning. This paper reviews the purpose of learning taxonomies, summarizes the comparative advantages of the SLT, and describes the SLT dimensions with examples from accounting that can be adapted to other disciplines. Assessment and lessons-learned are also discussed. It is not an “either-or” proposition, however. Both the SLT and the revised Bloom framework can add value, in different ways, to instructional design and assessment.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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