How Amazon Redshift ML can help enhance outcomes for underperforming, at-risk students

Higher education institutions are under increasing pressure to demonstrate the effectiveness of their programs and provide students with a clear path to degree completion. Data analytics can help these institutions proactively identify and support at-risk students, allowing them to develop personalized intervention strategies to improve student retention and graduation rates.

In this post, we’ll explore how Amazon Redshift ML, a powerful machine learning (ML) capability within the Amazon Redshift data warehouse, can enable higher education leaders to quickly predict student outcomes and communicate insights to key stakeholders.

The challenge: Bridging data silos to gain insights

Higher education institutions typically manage student data across two key systems – the student information system (SIS) and the learning management system (LMS). The SIS serves as the central repository for core student data, such as enrollment, academic performance, and administrative information. The LMS, on the other hand, tracks students’ digital learning activities and interactions.

With data siloed across these distinct systems, institutions struggle to gain a comprehensive, 360-degree view of their students. Integrating this data can unlock powerful analytics and predictive modeling capabilities using artificial intelligence (AI) and ML techniques. Informed by these insights, institutions can implement proactive monitoring and early intervention programs to boost student success.

Consider a scenario where a university’s SIS contains information about a student’s demographic background, enrollment status, and academic performance, while the LMS holds data related to their online activity, assignment submissions, and engagement with course materials. By combining these disparate data sources, the university can gain a deeper understanding of the factors influencing student outcomes. This allows them to identify patterns and trends that may indicate a student is at risk of falling behind or dropping out, enabling timely interventions to support their academic journey.

The solution: Amazon Redshift ML for predictive analytics

Amazon Redshift ML empowers higher education institutions to easily build and deploy machine learning models directly within their data warehouse environment. With a single SQL command, you can instruct Amazon Redshift to train a variety of ML models, including those for classification, regression, and clustering.

The key benefits of using Amazon Redshift ML include:

1. Seamless integration: Amazon Redshift ML provides a seamless interface to build and deploy ML models, leveraging the power of Amazon SageMaker AI in the background. This eliminates the need to export data, develop models using separate tools, and then re-import the model back into your data warehouse.

2. Ease of use: An analyst with SQL skills can easily create and train models with no expertise in ML programming languages, algorithms, or APIs. Amazon Redshift automatically selects the appropriate ML algorithm and tunes the model for the problem.

3. Reduced overhead: Amazon Redshift ML saves you the time and effort normally spent on tasks like data formatting, permission management, and building custom integrations. You can focus on building your models and generating insights rather than managing the underlying infrastructure.

4. Predictive capabilities: With Amazon Redshift ML, you can make predictions and perform predictive analytics directly within your Amazon Redshift cluster, without the need to move data out of the data warehouse.

By leveraging Amazon Redshift ML, higher education institutions can build predictive models to identify students who are at risk of underperforming or dropping out. For example, you could create a classification model to predict the likelihood of a student graduating on time based on factors such as their academic history, engagement with coursework, and demographic information. This allows you to proactively intervene and provide targeted support to those students who need it most.

Empowering data analysts to drive insights

Many higher education institutions lack dedicated data science teams but are interested in empowering their existing data analysts and developers to explore solutions to common challenges, such as predicting at-risk students.

Amazon Redshift ML enables these data experts, who are already familiar with the institution’s data and SQL, to quickly build on their expertise and start creating and training ML models using their Redshift data. This allows them to uncover valuable insights and identify interventions to improve student outcomes, without the need to learn specialized ML tools and programming languages.

For example, a data analyst at a university might use Amazon Redshift ML to build a model that predicts the likelihood of a student dropping out based on factors like attendance, assignment completion, and midterm grades. They can then share these insights with academic advisors, who can proactively reach out to the identified at-risk students and offer personalized support, such as tutoring, counseling, or financial aid assistance. By empowering data analysts to leverage ML, the university can develop targeted strategies to support student success.

By democratizing access to machine learning, Amazon Redshift ML empowers a wider range of stakeholders within the institution to participate in the data-driven decision-making process. Data analysts can collaborate with faculty, advisors, and administrative staff to interpret the model outputs and develop targeted strategies to support student success.

Conclusion

By leveraging the power of Amazon Redshift ML, higher education institutions can break down data silos, gain a comprehensive view of their students, and predict academic outcomes with greater accuracy. This enables them to proactively identify and support at-risk students, ultimately improving student retention and graduation rates.

As higher education continues to evolve, the ability to understand and predict student outcomes becomes increasingly critical. Amazon Redshift ML offers a robust, accessible tool for institutions to gain deeper insights, ultimately supporting students’ academic journeys more effectively. The future of student success transcends data collection – it’s about transforming raw information into meaningful, proactive support strategies that address individual student needs and challenges.