In our last post, we discussed the idea of learning and performance, using generative AI as a way of unpacking what it means. Today, let’s dive into another example of how emerging technologies can help elucidate interesting aspects of successful learning.
We live in the information age, where the ability to integrate and make sense of diverse sources of data is a powerful tool. Many companies leverage the internet of things (IoT) to gather input from a variety of devices. IoT networks encompass physical devices equipped with sensors that collect and share data. Examples from everyday life include wearable fitness trackers and smart thermostats. In the industrial sector, companies use GPS trackers, RFID chips, and environmental monitors to manage operations and monitor conditions (for example, by tracking the location and temperature of cows).
The primary benefit of IoT lies in its capacity to collect and analyze multiple data sources. When combined, these data streams generate insights far greater than their individual devices contribute. For example, sensor data on machinery performance can be used to improve maintenance schedules, and tracking data on delivery trucks can optimize routing. Put together, they may be able to create new ways of operating that reduce inefficiencies and environmental impact and improve the company’s operations.
Varied representations
Learning, much like IoT data integration, benefits significantly from multiple sources. When we encounter new information, we build a mental model to understand it. Seeing the same information presented in different ways helps form that mental model with a more comprehensive and nuanced understanding.
Imagine reading about a complex process, like what happens in an internal combustion engine. While reading about it can provide an initial understanding, you’d still have some errors or gaps, so following that up with a visual demonstration can be extremely helpful. In mathematics, viewing a quantity through various “representations”—such as graphs, equations, and physical objects—deepens understanding. For example, children studying fractions can start with visual aids like pizza slices before moving to numerical representations like "1/4." By moving across these different ways of showing the same idea, the student can begin to form a more coherent and consistent understanding of the underlying concept. Varied representations are equally crucial for adult learners. Someone learning about the role of transformers in artificial intelligence might benefit from theoretical explanations, practical demonstrations, dynamic visualizations, and other ways of clarifying the concept.
Compare and contrast
Comparison may be “the thief of joy,” but it is critically important for effective learning. Analyzing multiple representations and identifying similarities and differences enhances understanding. For instance, comparing multiple business case studies on AI integration can yield critical insights. One can learn about each company's approach individually, but the true value lies in comparing strategies, focusing on what worked, what didn't, and why.
In education, creating "alignable differences" helps learners make these comparisons effectively. Alignable differences are structured to highlight crucial aspects, making it easier to notice significant similarities or differences. For example, in geology, comparing divergent and convergent plate boundaries using diagrams can reveal key dynamics of tectonic movements and their effects, such as earthquake potential. A pedagogical approach called “variation theory” builds off this concept. It suggests that by varying certain aspects of a phenomenon while keeping others constant, learners can discern critical features. This method helps in building a deeper and more flexible understanding of complex topics. While it may be hard for an individual to craft experiences that match this approach, each learner can think about the key questions like, “Are these similar or different? In what ways? What can I learn from that?”
Seek multiple examples
The ability to generalize is a key aspect of successful learning, and it is hard to do with just one example. As a learner, seek out multiple examples, and compare and contrast across them. For instance, when improving programming skills, examining different solutions to the same problem can reveal various approaches and techniques, helping you understand why and when to use each one. When you don’t have multiple examples, you can still use self-explanation to ask yourself questions that work as contrasts, such as “What would happen if…?”
IoT helps build a richer, more powerful understanding of complex situations, based on multiple sources of data. Similarly, by leveraging multiple examples, sources, and representations, learners can build a more robust and nuanced comprehension of their chosen topics.
