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首页> 外文会议>Proceedings of the 2008 spring simulation multiconference >A Soft Computing Decision Support Framework to Improve the e-Learning Experience
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A Soft Computing Decision Support Framework to Improve the e-Learning Experience

机译：用于改善电子学习体验的软计算决策支持框架

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摘要

In this paper an e-learning decision support framework based on a set of soft computing techniques is presented. The framework is mainly based on the FIR methodology and two of its key extensions: a set of Causal Relevance approaches (CR-FIR), which allows reducing uncertainty during the forecast stage; and a Rule Extraction algorithm (LR-FIR), that extracts comprehensible, actionable and consistent sets of rules describing students' learning behavior. The analyzed data set was gathered from the data generated from user's interaction with an e-learning environment. The introductory course data set was analyzed with the proposed framework with the goal to help virtual teachers to understand the underlying relations between the actions of the learners, and make more interpretable the student's learning behavior. The obtained results improve the system understanding and provide valuable knowledge to teachers about the course performance.
机译：本文提出了一种基于一套软计算技术的电子学习决策支持框架。该框架主要基于FIR方法论及其两个主要扩展：一组因果相关性方法（CR-FIR），可以减少预测阶段的不确定性；以及规则提取算法（LR-FIR），该算法提取描述学生学习行为的可理解，可行和一致的规则集。分析的数据集是从用户与电子学习环境的交互生成的数据中收集的。该入门课程数据集采用提出的框架进行了分析，目的是帮助虚拟教师理解学习者行为之间的潜在关系，并使学生的学习行为更具可解释性。获得的结果提高了系统理解能力，并为教师提供了有关课程绩效的宝贵知识。

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