CAUDIT Analysis Part 3: Learning Analytics

CAUDIT Analysis Part 3

Issue 8: Learning Analytics

“Supporting improved student progress through establishing & utilising learning analytics.”

Every interaction from students, staff and researchers, through any of an institution’s systems, is a data point of interest. These points of interest are a wealth of information that can be used to improve services and experiences institution wide.

Numerous studies show the importance of basing decisions on quantifiable measurements. It allows you to target, change and measure outcomes. For example, measuring student performance beyond the standard result orientated structure can allow an institution to intervene earlier in a student’s progress. One study showed that a university was able to intervene as early as two weeks into a semester (Sclater, 2016). They identified students who were at high risk of dropping out and helped these students adjust their program.

These areas may be outside of the typical ICT purview, but are becoming increasingly ICT driven. ICT tends to own the analytics platform and should be responsible for ensuring it meets an institutes requirements.

Challenges and Considerations

Challenge: Manage increased data inflow from a vast array of sources across campuses.
Students and teachers can bring in a multitude of devices for use on campus, including laptops, tablet, smartphones and more. They can also access institutional facilities, applications, systems, and data whilst at university, work, home. These are just a few examples of different institutional data points. Combining all of these devices across a variety of locations depicts how data points are increasing exponentially.

Each data point when analysed in the right manner, holds valuable information - information that determines how an institution should respond and/or intervene to ensure it is providing the right solution to the matter at hand. In order to gain this value, an institution should first understand how to manage many and different data inputs.

JDS has the experience in building and implementing platforms that can provide a universal solution - a solution that can collect, index and analyse any machine data at massive scale, without limitation. Institutions can then search through this data in real time, correlate and analyse with the benefit of machine learning to better understand institutional trends and ultimately make better informed decisions.

Challenge: Cross collaboration across institutional stakeholders (eg. students, teachers, admin, etc.)
An institution such as a university has a wide variety of internal and external stakeholders, all with the need to share information between one another. This information also often originates from a large variety of sources.

The challenge here is making this data from students, teachers, researchers and administration staff accessible to each other in a controlled, normalised manner. These stakeholders will also have different questions to ask of the same data, each with their own requirements. ICT does not necessarily have a deep understanding nor require a deep understanding of the content of this data, but they can provide a single platform for collaboration. A common platform can address these issues and allowing cross collaboration between stakeholders while improving an institutions products and services.

With the help of JDS, ICT can provide such a platform that allows ease of generating dashboards, reports and alerts. JDS can enable the institution to not only ask various questions of the same data but also create visualisations and periodic reports to help interpret and use the information gleamed.

JDS has implemented such solutions across various Australian organisations, unleashing the powerful data collection functionalities of a variety of industry leading monitoring tools. We have our own proprietary solution, JDash, purpose build to help organisations collaborate to understand service impact levels, increase visibility into IT environments, allow efficient decision making and provide cross silo data access and visualisations. JDash is also fully customisable to various institutional needs and requirements.

Challenge: Using analytics for early intervention with student difficulties.
In a recent article in The Australian, the attrition rate from some universities was above 20%. Numerous factors contribute to this, which can ultimately be reduced through early prevention. Institution can make better informed decisions if they have the right data at the right time.

The CAUDIT report highlighted the need to identify student performance early. This allows an institution to intervene by either advising a student of a different direction or providing the right resources to handle their difficulties.

In order to provide this level of insight into student drop-out rates and early intervention, institutions need to build and manage a learning analytics platform. A learning analytics platforms collects, measures, analyses, and reports data on the progress of learners within context.

JDS can assist institutions with building a centralised, scalable, and secure learning analytics platform. This platform can ingest the growing availability of big datasets and digital signals from students interacting with numerous services. From the centralised platform, institutions can interpret the information to make decisions on intervention and ultimately reduce attrition rates of students.

About JDS

With extensive experience across the higher education sector, JDS assists institutions of all sizes to streamline operations, boost performance and gain competitive advantage in an environment of tight funding and increasing student expectations. With more than 10 years’ experience in optimising IT service performance and availability, JDS is the partner of choice for trusted IT solutions and services across many leading higher education institutions. Contact us to find out more.