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PERSONALIZED LEARNING EXPERIENCE THROUGH ADAPTIVE TESTING USING BLOOMS TAXONOMY

Authors: Saqueba Z. Mahir Mistry;

PERSONALIZED LEARNING EXPERIENCE THROUGH ADAPTIVE TESTING USING BLOOMS TAXONOMY

Abstract

Technology is advancing quickly in the modern world, and we daily are exposed to innovations. Artificial intelligence is one of the rapidly developing fields of computer science that is poised to usher in a new era of technological advancement through the development of intelligent machines.Artificial intelligence is now pervasive in our world. It is currently engaged in a wide range of subfields like Mathematics, Biology, Psychology, Sociology, Computer Science, Education etc.The study of this paper focuses on the application of AI with Adaptive testing using Bloom’s taxonomy [1] for effective teaching and learning process.The study emphasiszes the importance of Bloom’s Taxonomy [2] that is used setting the learning objectives for the students integrated with the benefits of adaptive testng which enables the student work on the areas they are weak with. For improved accessibility for students and teachers, the suggested method might be implemented as a feature of an LMS or an Android application

Keywords

Bloom Taxonomy, Artificial Intelligence, Adaptive Testing

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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).
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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.
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