Real-time Learning Analytics

Real-time Learning Analytics (RLA) visualizes and analyses the data as it appears in the computing system which is a Learning Management System (LMS). It is the use of data and the related resources for analysis as soon as it becomes available in the LMS. The term real-time always refers to computer responsiveness and is associated with streaming data architectures which enable real-time operational decisions automatically through process automation and/or policy enforcement.

Real-time analytics helps us with the following:

1. Forming just-in time teaching decisions and applying them to learning activities -- including day-to-day teaching/learning processes and transactions -- on an ongoing basis.
2. Viewing dashboard displays in real-time with constantly updated transactional data sets.
3. Utilizing existing prescriptive and predictive analytics
4. Reporting historical and current data simultaneously.

Real-time Learning Analytics (RLA) ensures that data analysis is done as close to the data's origin as possible. In addition to edge computing, other technologies that support RLA include:

1. Processing in memory -- a chip architecture in which the processor is integrated into a memory chip to reduce latency. 
2. In-database analytics -- a technology that allows data processing to be conducted within the database by building analytic logic into the database itself. 
3. Data warehouse appliances -- a combination of hardware and software products designed specifically for analytical processing. An appliance allows the purchaser to deploy a high-performance data warehouse right out of the box. 
4. In-memory analytics -- an approach to querying data when it resides in random access memory, as opposed to querying data that is stored on physical disks.
5. Massively parallel programming -- the coordinated processing of a program by multiple processors that work on different parts of the program, with each processor using its own operating system and memory.

In order for the real-time data to be useful, the RLA applications being used should have high availability and low response times. These applications should also feasibly manage large amounts of data, over time up to terabytes. This should all be done while returning answers to queries within seconds.

The term real-time also includes managing changing data sources -- something that LMSs may have as third-party tools. As a result, the real-time analytics applications should be able to handle big data. The adoption of real-time big data analytics can maximize institutional returns, reduce operational costs and introduce an era where machines can interact over the internet of things using real-time information to make decisions on their own.

Real-time Learning Analytics (RLA) enables educators to react without delay, quickly detect and respond to patterns in learner behaviour, take advantage of opportunities that could otherwise be missed and prevent problems before they arise.

Institutions that utilize real-time analytics greatly reduce at-risk students throughout their teaching tenure since the system uses data to predict outcomes and suggest alternatives rather than relying on the collection of speculations based on past events or recent scans -- as is the case with historical data analytics. Real-time analytics provides insights into what is going on in the moment. The way forward.