Open Learning Analytics (OLA) is an emerging research area that aims at improving learning efficiency and effectiveness in lifelong learning environments. OLA employs multiple methods to draw value from a wide range of educational data coming from various learning environments and contexts in order to gain insight into the learning processes of different stakeholders. As the research field is still relatively young, only a few technical platforms are available and a common understanding of requirements is lacking. This paper provides a systematic literature review of tools available in the learning analytics literature from 2011-2019 with an eye on their support for openness. 137 tools from nine academic databases are collected to form the base for this review. The analysis of selected tools is performed based on four dimensions, namely 'Data, Environments, Context (What?)', 'Stakeholders (Who?)', 'Objectives (Why?)', and 'Methods (How?)'. Moreover, five well-known OLA frameworks available in the community are systematically compared. The review concludes by eliciting the main requirements for an effective OLA platform and by identifying key challenges and future lines of work in this emerging field.
翻译:开放式学习分析(Open Learning Analytics,OLA)是一个新兴的研究领域,旨在提高终身学习环境中的学习效率和效果。 OLA采用多种方法从各种学习环境和背景中获取价值,以便深入了解不同利益相关者的学习过程。由于研究领域还相对年轻,因此只有少数技术平台可用,缺乏对需求的共同理解。本文对2011年至2019年学术数据库中学习分析文献中的工具和框架进行了系统文献综述,着眼于其对开放性的支持。筛选出来自九个学术数据库的137个工具用于此综述。选定的工具分析基于四个维度,即“数据,环境,上下文(什么?)”,“利益相关者(谁?)”,“目标(为什么?)”和“方法(如何?)”。此外,还系统比较了社区中可用的五个知名的OLA框架。综述最后总结出了有效的OLA平台的主要要求,并确定了这个新兴领域的关键挑战和未来发展方向。