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Tracking User's Programming Skills Using Multi Agents on E-Cloud

机译:跟踪用户在电子云上的多个代理的编程技巧

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摘要

Most of the current E-Learning technologies assess the learners either by tracking the learning activity or estimating their programming knowledge. In this paper, a cloud based E-learning scaffold that utilizes performance tracking using Multi-Agents for assessing the learning and coding behavior of the users is proposed. Bayesian Networks are employed to analyze and compute the overall performance. In-order to improve the performance of the learners, a feedback based PQR (Performance-Tracking-Recommendation) rating algorithm for measuring the quality of the Learning Objects (LO) is suggested.
机译:目前的大多数电子学习技术通过跟踪学习活动或估算其编程知识来评估学习者。在本文中,提出了一种基于云的电子学习脚手架,其利用使用多种子的性能跟踪来评估用户的学习和编码行为。贝叶斯网络被用于分析和计算整体性能。为了提高学习者的性能,提出了一种用于测量学习对象质量的基于反馈的PQR(性能跟踪推荐)额定值算法。

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