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Evaluation of student knowledge using an e-Learning framework

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Abstract

This chapter introduces the concept of adding a fuzzy logic classifier to e-Learning framework. This conceptual model uses Fuzzy Logic Evaluation Sub-systems (FLESs) to implement “Theory of probability” curriculum. The customized sub-system is used to dynamically evaluate student knowledge. It is essential that the FLES-PRobabilty (FLES-PR) capture’s the students’ interest to maintain their motivation and increase the effectiveness of the learning experience. Given that interactive systems increase the education efficiency and the individual abilities of student, the routine actions of teacher are must be delegated to e-Learning system. In this chapter, artificial intelligence concepts, techniques, and technologies are used to deliver the e-Learning requirements. For instance, a fuzzy logic scheme is created to evaluate student knowledge when using the FLES-PR. The curriculum is delivered using two FLES modules instantiated using the “Matlab” 6.5 fuzzy toolbox environment. Each sub-system provides structured lessons, representing topics, content, and additional contextual parameters. The FLES is designed to gain the students attention, highlights the lesson objective(s), stimulates recall of prior knowledge, and progressively elicits new material to guide increased performance by providing feedback using benign assessment to enhance retention. The proposed evaluation system is designed as a functioning plug-into the universities “Moodle” server to leverage from the existing course management, learning management and virtual learning environment. It also recommends the pace and complexity of learning as the student progresses through the curriculum. This chapter case study discusses the success of the FLES-PR software tool and explains how it has been validated against the manual results of three human experts.

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APA

Favorskaya, M., Kozlova, Y., Tweedale, J. W., & Jain, L. C. (2015). Evaluation of student knowledge using an e-Learning framework. Intelligent Systems Reference Library, 84, 91–114. https://doi.org/10.1007/978-3-319-14645-4_5

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