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：开放学习分析（OLA）是一个新兴研究领域，旨在提高终身学习环境中的学习效率与效果。OLA采用多种方法从各类学习环境与情境中产生的多样化教育数据中提取价值，以洞察不同利益相关者的学习过程。由于该研究领域仍相对年轻，只有少数技术平台可用，且缺乏对需求的共识。本文对2011-2019年间学习分析文献中可用的工具进行了系统文献综述，重点关注其对开放性的支持。从九个学术数据库中收集了137个工具作为本次综述的基础。基于四个维度对所选工具进行分析，即“数据、环境、情境（什么？）”、“利益相关者（谁？）”、“目标（为什么？）”和“方法（如何？）”。此外，对社区中五个知名的OLA框架进行了系统比较。综述最后引出了有效OLA平台的主要需求，并指出了这一新兴领域的关键挑战与未来工作方向。