June 24, 2019
June 24, 2019
June 28, 2019
Engineering Design Graphics
One of the fundamental concepts behind Adaptive Comparative Judging (ACJ) is that it is easier and more accurate to comparison judge a series of products, and to develop a rank order of achievement, than it is to score products using a more subjective method or rubric approach. Research in the field of comparative judging has shown a very high level of validity and reliability in this assessment methodology. The assessment approach appears to be effective at varying levels of rigor and academic achievement. Studies have examined adaptive comparative judging techniques in academic areas such as writing/composition, science education, and geography instruction. The areas of design and technology have proven to be especially effective topics for ACJ assessment, and are of special interest to the authors.
This paper examines the fundamental principles of comparative judging and adaptive comparative judging, and discusses some of the most recent and relevant research on this topic. Several readily available web-based ACJ tools and products are reviewed, especially as they relate to academic settings. Applications in the areas of portfolio evaluation, graphics assignment (CAD, animation, Web, etc.) assessment, and peer critiquing are also explored.
Additionally, the potential for large-scale collaboration using this methodology for analyzing educational productivity, learning effectiveness, and collaborative research is considered. Adaptive comparative judging has proven to be a method or assessment tool that is relatively straightforward to learn for faculty, and somewhat easily applied to a wide variety of topics and assignment approaches. Lessons learned to this point in time and application best practices using ACJ are shared with an eye toward future applications of this approach that is rapidly growing in popularity.
Bartholomew, S. R., & Connolly, P. E. (2019, June), Adaptive Comparative Judgment in Graphics Applications and Education Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. https://peer.asee.org/27537
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