Abstract
In recent years, learning analytics has become a hot topic with many institutes deploying learning management systems and learning analytics tools. In this paper, we introduce learning analytics platforms that have been established in two top national Japanese universities. These initiatives are part of a broader research project into creating wide-reaching learning analytics frameworks. The aim of the project is to support education and learning through research into educational big data accumulated on these platforms. We also discuss the future direction of our research into learning analytics platforms. This includes introducing a model in which learning analytics tools and the results of research can be shared between different education institutes.
Highlights
As the digitization of learning environments is advancing, interest in learning analytics and its effect on education is increasingly gaining attention
The master template is based on the design of our Learning Management Systems (LMS) independent Learning Analytics (LA) platform, and we propose that the master template would be comprised of four types of components: LMS, behavior sensors, learning record store (LRS), and Analysis and Results
Moving to a modular learning analytics system that is connected by standards-based specifications has advantages in that the tool pipeline can be customized to suit the institution in which it is being deployed
Summary
As the digitization of learning environments is advancing, interest in learning analytics and its effect on education is increasingly gaining attention. Learning analytics, which have been define as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” on the LAK11 website (https://tekri.athabascau.ca/analytics/). Research into learning analytics has mainly focused on highly localized contexts with a very narrow scope of investigation. These limitations were imposed due to a lack of infrastructure, data, and analysis tools available at the time. As the collection of education big data is increasing in many different facets of learning environments, research analyzing data from a wide range of learning contexts is a significant challenge (Ferguson, 2012). Creating platforms to support the automatic analysis of educational data is fundamental to the continuing development of learning analytics
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