How graph databases can enhance learning

by Lavanya Sood | on

Educators envision a world in which technology supports personalized education experiences to promote continuous lifelong learning. In this world, relevant content can be delivered in moments and in a way that’s most effective for learners. Graph databases can help make this vision a reality. Graph databases can better organize, analyse, and connect learner data to deliver unique, personalized learning experiences in real-time.

Graph database solutions like Amazon Neptune from Amazon Web Services (Amazon Web Services) help organize learner information to enable more holistic analysis for educational institutions and education technology companies (EdTechs). By linking learner data from multiple sources and drawing new correlations from data, educators can deliver more personalized learning experiences. In this blog post, learn how educators can use graph databases to enhance the learning experience.

The basics of graph databases

Graph databases are designed to store and navigate the relationships between data—they put relations between data in focus. While conventional databases, or relational databases (RDBs), store links between data in the data themselves through primary keys and foreign keys, the data in graph databases is represented by nodes that have properties (or tags) and are directly related to other nodes by edges representing semantic relationships between nodes. Graph databases allow for the flexible addition of relationships between data and support the fast retrieval of complex structures that are difficult to model in RDBs. Plus, the data in graph databases does not have be uniform—a useful attribute when some data is incomplete or organizations want to scale up a graph database project or make changes.

How graph databases support personalized education

Using a graph database can help educators create a knowledge graph, in which learning resources are organized and linked to form a large web of content. Conventional online learning systems typically provide options that allow you to adjust the difficulty level and learning progress, but the curriculum never changes. However, a knowledge graph system based on a graph database can provide a conceptual framework of school subjects and can help manage the students and their learning record. By incorporating artificial intelligence (AI) to review the learning pattern in the graph database, an AI can perform analysis of the learner’s academic achievement, and the learning path through the subject can be customized for every user as relational data. This can be used to recommend a study path and test progress, including with reinforced learning and retesting on topics where needed.

For example, if a learner using an online learning system is trying to solve a particular question in “definite integrals” but they get stuck at a step that requires knowledge about differentiation of logarithmic functions, which they had either forgotten or skipped, the system can recommend content on that topic to the learner.

Developing an MOOC application for an educational institution with graph databases

One example use case in which graph databases are uniquely positioned to support the learning journey is through massive online open course (MOOC) platforms. Standard learning platforms and learning management systems (LMS) that support MOOC platforms may present instructional content digitally without fully taking advantage of the flexibility and opportunities for linking multiple resources offered by a dynamic platform. However, incorporating graph databases into these platforms can provide a richer experience for interacting with course content that is more suitable for learners, with the information transmitted between system components.

Incorporating a traditional LMS into an MOOC web application that leverages a graph database offers benefits in this scenario as well. An LMS offers useful components, including storage of student-specific information like login, performance assessments, course progress, and more.

There are three main architectural connections in this type of application design:

The connections between the MOOC’s browser-based graphical user interface (GUI) web application and the graph database determines where the learner is situated in the curriculum graph and which nodes are immediately accessible, barring access restrictions if applicable. By providing a map of the curriculum and where the user is located in it, an MOOC application can use the graph database to help the user navigate back to previous nodes without getting lost in complex linkages—much like a road map.

The connection between the user interface and the LMS is primarily used to provide the user interface (UI) information about access restrictions for a particular learner if they are enrolled in a course that limits access to some information. Starting from the entry point of the curriculum graph, or from any other remembered bookmarked node in the graph, the user should be able to freely navigate to any other point and back using the structure of the graph.

The connection between the graph database and the LMS informs the latter about the overall structure (though not the details) of the curriculum. The LMS is used to store assessments for those aspects of the curriculum that do have associated assessments, and to provide to the graph database the assessments that are appropriate to each component of the curriculum. If a student undergoes an assessment more than once for the same component of the curriculum, the LMS holds the information needed to determine which components of the assessment should or should not be repeated.

Getting started with graph databases

Storing learning resources and content, as well as learners’ information, as graphs can enable institutions to build large scale systems that support learning in a more holistic way. This can help eliminate rote learning, and support each learner on their own education journey with the resources they need, when they need them.

Learn more about Amazon Neptune and graph databases on Amazon Web Services . Plus, discover how educators, EdTechs, and educational institutions around the world use Amazon Web Services to support student success and more at the Amazon Web Services Cloud Computing for Education hub.

 Read related resources:

  • What Is a Graph Database?
  • Complement Commercial Intelligence by Building a Knowledge Graph out of a Data Warehouse with Amazon Neptune
  • How Skillshare increased their click-through rate by 63% with Amazon Personalize
  • How to scale and optimize Moodle LMS on Amazon Web Services
  • Modernize Moodle LMS with Amazon Web Services serverless containers
  • How EdTechs use artificial intelligence and machine learning to create personalized learning experiences

Subscribe to the Amazon Web Services Public Sector Blog newsletter to get the latest in Amazon Web Services tools, solutions, and innovations from the public sector delivered to your inbox, or contact us .

Please take a few minutes to share insights regarding your experience with the Amazon Web Services Public Sector Blog in this survey , and we’ll use feedback from the survey to create more content aligned with the preferences of our readers.