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An architecture for e-learning system with computational intelligence

机译：具有计算智能的电子学习系统的体系结构

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Purpose - The purpose of this paper is to introduce a new kind of learning management system: proactive LMS, designed to improve the users' online (inter)actions by providing programmable, automatic and continuous intelligent analyses of the users' behaviours, augmented with appropriate actions initiated by the LMS itself. Design/methodology/approach - Proactive systems adhere to two premises: working on behalf of, or pro, the user, and acting on their own initiative, without the user's explicit command. The proactive part of the LMS is implemented as a dynamic rules-based system, and is added next to the initial LMS. They both use the same database as their source of information on the users, their activities, the available resources and the current state of the whole system. Findings - How the proactive part of the LMS was implemented on the basis of a dynamic expert system is shown. Also how it looks like from a user's point of view is sketched. Finally, examples of intelligent analysis of users' behaviours coded into proactive rules are given. Research limitations/implications - Future work should include the design and the implementation of sets of rules (packages) dedicated to common users' needs, enabling useful proactivity on the basis of elaborated intelligent analysis. Originality/value - Current learning management systems (virtual educational and/or training online environments) are fundamentally limited tools. Indeed, they are only reactive software: these tools wait for an instruction and then react to the user's request. Students using these online systems could imagine and hope for more help and assistance tools: LMS should tend to offer some personal, immediate and appropriate support as teachers offer in classrooms. The proactive LMS can, for example, automatically and continuously help and take care of e-leamers with respect to previously defined procedures rules, and even flag other users, like e-tutors, if something wrong is detected in their behaviour.
机译：目的-本文的目的是介绍一种新型的学习管理系统：主动LMS，旨在通过提供对用户行为的可编程，自动和连续智能分析来改善用户的在线（交互）行为，并适当地加以补充LMS本身启动的操作。设计/方法/方法-主动系统遵循两个前提：代表用户或亲用户工作，并且在没有用户明确命令的情况下主动采取行动。 LMS的主动部分被实现为基于动态规则的系统，并被添加到初始LMS的旁边。他们都使用相同的数据库作为关于用户，他们的活动，可用资源和整个系统当前状态的信息源。调查结果-显示了如何在动态专家系统的基础上实施LMS的主动部分。还绘制了从用户角度看的外观。最后，给出了将用户行为编码为主动规则的智能分析示例。研究的局限性/意义-未来的工作应包括设计和实施专门针对普通用户需求的规则集（程序包），从而在详尽的智能分析的基础上实现有益的主动性。原创性/价值-当前的学习管理系统（虚拟教育和/或培训在线环境）从根本上来说是受限制的工具。实际上，它们只是响应式软件：这些工具等待指令，然后对用户的请求做出反应。使用这些在线系统的学生可以想象并希望有更多的帮助和协助工具：LMS应该倾向于像教师在课堂上提供的那样，提供一些个人的，即时的和适当的支持。主动式LMS可以例如根据先前定义的过程规则自动连续地帮助和照顾电子求职者，甚至在检测到用户行为有误时，甚至标记其他用户（如电子老师）。

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