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Machine Learning and Learning Analytics: Integrating Data with Learning

机译：机器学习和学习分析：将数据与学习集成

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In the last years, the design, implementation and delivery of web-based education systems, such as the Learning Management Systems, has grown exponentially, thanks to the fact that neither students nor teachers are bound to a specific location. Moreover, this form of computer-based education is virtually independent of any specific hardware platform and, as an important consequence, these systems are storing a large amount of educational data that could be used to improve the learning, the teaching and the administration processes. Extracting useful information represents a new challenge involving Machine Learning, Data Mining and Learning Analytics. Machine Learning is concerned with a large number of algorithms that improve their performance with experience, in many fields of research such as those learning contexts where students interact with learning systems leaving useful tracks. Educational Data Mining is the science of extracting useful information from the large data sets or databases containing students interactions during their learning, for example in a virtual environment. Finally, Learning Analytics is a set of steps for understanding and optimizing the whole learning process, together with the environment in which it occurs. It is composed by several steps, where the first is strictly related to Educational Data Mining for capturing data by some machine learning algorithms. In this paper, we discuss the intersections and correlations between these three areas of research, trying to discuss their relationships and steps to give a useful overview on the learning processes from different points of views. Different models are introduced and discussed.
机译：在过去的几年，设计，实施和基于Web的教育系统，如学习管理系统的交付，已成倍增长，这要归功于既不是学生还是老师都绑定到特定位置的事实。此外，这种形式的基于计算机的教育几乎是独立于任何特定的硬件平台，而且，作为一个重要的结果，这些系统存储了大量可用于改善学习，教学和管理流程的教育数据。提取有用的信息代表涉及机器学习，数据挖掘和学习分析了新的挑战。机器学习涉及了大量的改善他们的经验与业绩，在研究许多领域的算法如学习环境，让学生与互动学习系统留下有用的轨道。教育数据挖掘是在虚拟环境中的学习过程中提取从大型数据集或包含学生的互动数据库有用的信息，例如科学。最后，学习分析对于理解和优化整个学习过程，在它出现的环境一起的一组步骤。它由几个步骤，其中第一个是严格相关的教育数据挖掘的一些机器学习算法捕获数据组成。在本文中，我们将讨论研究这三个领域之间的交叉和相互关系，试图探讨它们之间的关系和步骤给出一个有用的概述从意见不同点的学习过程。不同的模型进行了介绍和讨论。

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