Open Source Analytics in the Cloud Support National Course Outcomes

Improving Course Outcomes with Open Source Analytics on AWS

Jisc Effective Learning Analytics Project

For over two years Unicon has been a core partner in the development of Jisc’s Learning Analytics Service for the UK Higher Education and Further Education sectors. During the R&D Learning Analytics Project, Unicon provided infrastructure and a blend of on-site and remote consulting support for the development of an open source predictive data model. Unicon also deployed an open source case management tool (Student Success Plan - SSP) to enable visualization of ‘at risk’ indicators from the predictive data model. These indicators assisted tutors in identifying students and expediting interventions (via SSP) to support student attainment, progression, or retention. As the R&D Learning Analytics Project moves towards being a beta Jisc service, Unicon is currently providing transitional support to the Jisc technical team for the remainder of 2017.

Jisc member institution stakeholders identified that the effective use of learning analytics and student data has the potential to improve student retention, degree attainment, and student satisfaction. Retention and attainment are key issues in the UK. With a 10% rate of students expected to leave higher education without a degree[1] and a cost of millions of pounds per year due to student loss[2], improving retention has clear financial value. Beyond just the direct costs, reduced degree attainment has long-term effects on the employability of individuals as well as broader economic effects of a lower-skilled workforce[3]. Beyond just the direct costs, reduced degree attainment has long-term effects on the employability of individuals as well as broader economic effects of a lower-skilled workforce[4].

Jisc’s Effective Learning Analytics Project addresses these core education issues by establishing an infrastructure that tracks student learning activity and other student data to identify students at risk for completing a course. Jisc’s solution includes tools for support of interventions to improve course completion, retention, and attainment[5][6][7].

Challenges

Data analytics environments often have complex infrastructure and integrations, large storage needs, and periodically consume large amounts of compute resource. Additionally, analytics environments commonly require high degrees of agility to allow researchers to deploy and experiment with new analytics algorithms.

In the case of learning analytics, there is highly sensitive/restricted personally identifiable information (PII) regarding student performance and demographics. Within the overall environment, individual identifiers need to be preserved so that student interventions can be made; therefore, anonymization is not an option. Moreover, Jisc’s member institutions operate in the EU, with stringent data privacy requirements. Given the privacy requirements and sensitive nature of the data, a strong security infrastructure along with a set of governance/operating controls must be established.

Faster Innovation

Building a dedicated physical infrastructure for learning analytics would easily consume months as well as substantial budget just in labor. AWS supported substantially faster “time to value,” enabling Jisc to move quickly to the proof of concept stage while providing a flexible environment where analytics staff could execute, evaluate, and finalize the necessary predictive models to identify at-risk students. The rich AWS Elastic Map Reduce (EMR) ecosystem provides the needed tooling for bringing in data from source systems, centralizing the data in a learning record store, and then depositing that data in S3 buckets and AWS Data Pipeline (to move source data to HDFS). Data is then extracted from the HDFS data store into Apache Hive, orchestrating workflow and executing predictive model jobs using Oozie. The result is the push of model outputs (at-risk scores) back to the learning record store and the case management solution.

Reduced Costs

By provisioning the AWS resources only when they are needed and leveraging the TCO advantages of the more “fixed” resources for the case management solution, overall costs are substantially reduced. The EMR cluster is an on-demand cluster; CloudFormation Templates are used by AWS Data Pipeline to provision the cluster when the predictive model needs to be run, and de-provision when model runs are completed.

The case management solution is the open source Student Success Plan - SSP. SSP relies on a classic web application architecture with the front end running on EC2 and the back end on RDS. The TCO for running web applications in AWS can be as much as 80% savings[6]. The Jisc project environment includes dev, test, and production, so cost savings quickly compound.

Security

To address jurisdictional mandates related to the Data Protection Act (DPA) and EU General Data Protection Regulation (GDPR), the Unicon team established a secure architecture for the run time environments that would ensure all data and data processing could be confined to AWS regions within the EU.

The architecture, based on AWS best practices, constrained all data to a single region. Strict processes were invoked to control any movement of data outside of that region. The run-time architecture was implemented in a tiered structure with carefully controlled access to public-facing services contained within a virtual private cloud populated with isolated subnets. Sensitive data resided in the most protected tier, replicating this structure across several availability zones within the region for fault tolerance. Encryption was leveraged throughout (EBS volumes, RDS instances, EMR clusters) and AWS key management services were employed to simplify key rotation and frequency. Amazon Workspaces were used in the appropriate environments, enabling the team to interact with the data, while adhering to the tenets of the DPA/DPD. In addition, automated processes were created to perform quarterly audits of accounts across all instances with special emphasis on privileges and user accesses. Both CloudWatch and CloudTrail services monitored system level activity for audit and forensic purposes. The team adhered to the strict data governance policies imposed within the UK to deliver a secure, flexible, and verifiably compliant solution to the client (by utilizing automation, AWS services aligned with AWS best practices, and Unicon’s internal policy/procedure). Unicon was able to bring its experience and technical expertise to the Jisc Learning Analytics Project, particularly in terms of addressing challenges, the need for faster innovation, reduced costs, and robust security.

Meeting the Challenges:

Unicon Services for AWS

With experience in many of the AWS services including EC2, RDS, CloudFormation, EMR, and Data Pipeline, Unicon is able to help clients fully realize time to market advantages, high levels of reliability and scalability, and demand-based sizing and costs. Unicon has staff certified in current AWS certifications, including AWS Certified Professional level Solutions Architects and DevOps Engineers.

Unicon’s team of AWS professionals developed and deployed components of Jisc’s Effective Learning Analytics Project to the cloud to meet stringent data privacy requirements, increase agility, and satisfy storage along with compute resource needs. This secure cloud architecture enabled Jisc to realize faster innovation, reduced cost, and enhanced security for their data analytics environment.

Unicon is an Advanced Consulting Partner in the AWS Partner Network (APN). This allows Unicon to leverage AWS to its fullest potential given Unicon’s deep expertise in deploying and operating applications on AWS as well as application development.