Data helps us to understand patterns and make predictions and in education, each data point tells a part of a student’s journey. As machine learning, AI, and LMS analytics become interwoven into learning strategies, it’s essential to remember the person behind the data. In 2024, over 180 million people have used ChatGPT(external link), and that’s just one AI tool. AI has changed how students and educators can access information, and what data educators have insight into. Let’s explore how student data can be used ethically in AI and LMS analytics to inform pathways to delivering strong learning outcomes.
Understanding the bigger picture
In education, test scores and performance levels can be collected to identify trends and averages for students, providing a rough outline of a person. However, those data points don’t capture everything that makes up a person’s identity, like their friends, family, culture, and hobbies—data extends that identity. When the context of identity is taken away from a person to anonymise their data, it takes away what makes them, them. Additionally, in a Māori context, data is seen as a taonga (treasure) and when used, there must be mindful context.
How you host your LMS also influences who has access to student data. If you’re in New Zealand, partnering with a NZ-based cloud provider(external link) ensures your data is sovereign, and subject to only under New Zealand laws.
Developing guidelines for the ethical use of student data
As an educator, you have the opportunity to ensure students are seen beyond facts and figures when their data is used. Developing guidelines ensures everyone is on the same page and values can be aligned. Here are a few questions you can start with:
- What ways can we protect and keep control over student data?
- How can we ensure our AI tools and LMS analytics don’t reinforce biases?
- How are we communicating with students about the data we collect and how it’s used?
- Who’s responsible for ensuring AI and data decisions are ethical?
- What training has taken place for the ethical use of data?
Consider resources such as The Association for Authentic, Experiential, & Evidence-Based Learnin(external link)g (AAEEBL)'s digital ethics principles in ePortfolios. The Digital Ethics Task Force developed principles placing ethics at the core of the design. At the time of writing, there are ten main principles in version three(external link). The principles have a strong emphasis on responsible data use, including:
- Diversity, Equity, Inclusion, Belonging, and Decolonization (DEIBD): keeping students and representation at the forefront.
- Data Responsibility: Use data when necessary and be transparent with how it is used.
Be transparent with your students
Your students deserve to know when their data is being used. Keep them informed on our decisions and what data is being collected and why, such as test scores and login times. Also, clarify what information the AI will have access to and the role it plays in their learning. For example, sharing what features your LMS may have that are driven by AI, or where it’s appropriate for them to use AI as a resource. Since AI can hallucinate (makes things up when it doesn’t know the answer), work with your students on methods for confirming the accuracy of answers in AI-generated content.
If you need support developing your guidelines, consider partnering with an experienced eLearning consultant who understands your organisation’s goals.
Leveraging data to support students
Consider reviewing education technologies that have established AI principles or data policies in place. For instance, Moodle LMS focuses on a human-centred approach to AI(external link). They lead their work through research and transparency, noting limitations and potential areas of concern, such as privacy and false content.
The University of Canterbury (UC) (external link)wanted to gain a holistic view of when engagement was dropping and when a student may need help. Catalyst supported UC in developing Analytics for Course Engagement (ACE) to provide timely and appropriate support for their students. ACE was created by leveraging data through machine-learning AI in Moodle to ensure students are seen behind the data. Additionally, ACE provides insights to students to understand how they’re tracking for their own studies. Built-in Moodle LMS analytics can also identify:
- peak usage times
- engagement patterns
- time spent on content
- performance trends.
Having visibility into the learning journey for each student can help in knowing when to offer personalised learning paths, spot potential future learning needs, and current areas needing focus.
The power of data visibility
AI and LMS analytics are powerful tools that can transform learning and development strategies and the learning experience. Like other education tools, they need consideration for how they work for your students. By using student data with intention, and keeping students at the centre of decisions, you’re empowering your students to shape their learning journeys. Whether you’re just starting to explore AI or already have data policies in place, our eLearning experts at Catalyst are here to help. Contact us today to discuss how we can support your goals and learning strategy.
