Moodle represents a great contribution to the educational world since it provides an evolving platform for Virtual Learning Management Systems (VLMS) that became a standard de facto for most of the educational institutions around the world. Through the pedagogical functions provided, it collects in the many globally spread out databases a huge amount of information regarding the activities that teachers and students perform during the learning process. This reality makes Moodle a natural choice for conducting experimental research by Artificial Intelligence researchers interested in theories for improving learning and teaching; particularly those related with the controversial learning styles concept. Roughly defined, a learning style intends to be a model of the way and media an apprentice acquires knowledge and hence the way a teacher should present that knowledge to the apprentice matching his/her learning style. Independently of the many controversies (be these scientific, psychological or even ethical) about the soundness and real outcomes that such ideas can bring to improve learning, it’s a worthy intriguing research area for many researchers pursuing the ideal automated teacher: the teachbot dream. Behind this goal we have developed Middle, a Moodle plug-in able to infer the learning style of each student taking a course using an advanced version of a Bayesian network model that we previously tested. Middle intends support personalized teaching based on the Felder-Silverman’s ILS model and has been evaluated through controlled experiments and pilot test in high schools and university courses. Such experiments showed promising results that shed some light on learning styles modeling and its potential outcomes. During the experience we found strong limitations in the Moodle design regarding its supposed flexibility to incorporate new functionalities. From a strict software architecture point of view, we found that such flexibility is far from being enough to easier the implementation of the dynamic computational behavior required to support a teachbot. This made our effort much harder than expected, perhaps because of the illusion induced by the widespread use of Moodle. In this article we present our results and experiences extending Moddle with intelligent behavior from a software architecture point of view, focusing on the lessons learnt in such extension. Our experience indicates that this simplicity is far from being so and hence it is worth to share the limitations and how we overcome them.

Moodle 代表了对教育界的巨大贡献，因为它为虚拟学习管理系统 (VLMS) 提供了一个不断发展的平台，该平台已成为全球大多数教育机构事实上的标准。通过提供的教学功能，它在全球分布的许多数据库中收集了大量关于教师和学生在学习过程中进行的活动的信息。这一现实使 Moodle 成为对改进学与教理论感兴趣的人工智能研究人员进行实验研究的自然选择；特别是那些与有争议的学习方式有关的概念。粗略地定义，学习风格旨在成为学徒获取知识的方式和媒介的模型，因此教师应该将知识呈现给与他/她的学习风格相匹配的学徒的方式。除了关于这些想法可以为改善学习带来的合理性和实际结果的许多争议（无论是科学的、心理的甚至伦理的）之外，对于许多追求理想的自动化教师的研究人员来说，它是一个值得关注的研究领域：教学机器人的梦想。在这个目标的背后，我们开发了 Middle，一个 Moodle 插件，能够使用我们之前测试过的贝叶斯网络模型的高级版本来推断每个学生的学习方式。中意支持个性化基于 Felder-Silverman 的 ILS 模型进行教学，并通过高中和大学课程的对照实验和试点测试进行了评估。此类实验显示了有希望的结果，这些结果为学习风格建模及其潜在结果提供了一些启示。在体验过程中，我们发现 Moodle 的设计存在很大的局限性，因为它可以灵活地结合新功能。从严格的软件架构的角度来看，我们发现这种灵活性远远不足以更轻松地实现支持教学机器人所需的动态计算行为. 这使我们的努力比预期的要困难得多，也许是因为 Moodle 的广泛使用引起的错觉。在本文中，我们从软件架构的角度展示了我们用智能行为扩展 Moddle 的结果和经验，重点是在这种扩展中吸取的教训。我们的经验表明，这种简单性远非如此，因此值得分享这些限制以及我们如何克服它们。