We live in a world that is powered by data. In the world of professional learning, this abundance of data allows for two rapidly advancing fields to come together: learning analytics and artificial intelligence (AI). This synergy is poised to redefine how organizations and professionals navigate a world that is increasingly going to be powered by lifelong learning and upskilling.
Learning analytics, according to the Society for Learning Analytics Research, is “the measurement, collection, analysis, and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occurs.”
We can break this out further into three kinds of approaches:
- Descriptive analytics: These focus on understanding what has happened, such as tracking learner engagement and completion rates. Descriptive analytics offer a foundation of data-driven insights into past performance.
- Diagnostic analytics: Moving beyond the “what,” these approaches delve into the “why,” helping to identify issues and bottlenecks in learning. What sorts of patterns can you see in the data that might explain what you are observing? Perhaps there was a relationship between a certain characteristic of the learner (like background, knowledge, or preferences) and the outcomes? Or maybe learners who access particular resources are more likely to succeed?
- Predictive analytics: These approaches anticipate the future. That is, models that can make sense of patterns in existing data can also be deployed to monitor incoming information and use it to detect trends and even critical moments in a learning journey. A learning experience could predict a student's likelihood of completion or even a probability of getting a specific question wrong. Within a larger learning organization, models could predict when companies are lacking critical skills across their workforce or whether employees are at risk of leaving due to lack of development opportunities.
AI has the potential to build off learning analytics to create even more powerful, engaging, and impactful learning experiences. It can help address the next question after a prediction—what can I do to help move things in a better direction? Learning analytics systems can already flag organizational risks or surface interesting insights about student struggles in a learning platform. AI can be integrated to help make smart, quick decisions about what to do to address them. Rather than an early alert simply being a flag for a person to seek additional help, it can now start a dialogue with an AI that helps a person come up with a personalized learning plan. Organizations can use predictive analytics combined with AI to forecast which courses will be most in demand and how that aligns with current offerings. And, in the near future, these systems will also be able to help develop courses in real time.
The landscape of corporate learning is evolving rapidly, and the combination of learning analytics and artificial intelligence is going to be an exciting space to watch. These technologies are helping unlock a new era, where education is not just about accumulating knowledge but a journey of lifelong learning in a constantly changing world.
