The Digital Educator’s Toolkit
by Jason Cole
I want to take a moment and drill down into the digital toolkit all educators will need to understand in the coming years. As someone with a few decades of experience in this field, I hope to boil it down and reduce the hype, fear, uncertainty, and doubt.
First, I want to start by reiterating the technology itself isn’t what matters. What is essential is the new affordances it enables. You don’t have to understand the nuts and bolts of how a car works to know it can move you from point A to point B faster than walking or riding a horse. So as educators, most of us need to understand the technology well enough to drive. Some may want to get under the hood and tinker (and still others build cars). Over the coming months, we’ll explore just how much knowledge is required to drive.
So let’s start talking about technology and affordances. I’ll start with the bane of many educators, the mobile phone. Ah, the magic rectangle of distraction. It’s hard to remember Apple introduced the first iPhone in June of 2007. A scant ten years later, there are 2.7 billion smartphone users worldwide. That’s a third of the world population with a computer in their pocket. Americans spend more than a third of their media time on their phones, and TV time is shrinking.
Smartphones are always present systems for synchronous and asynchronous communication. They can create pictures, videos, audio recordings, music, drawing, and writing. An embedded suite of sensors, including location tracking, acceleration, and light levels interact with the physical world. Nearly any other sensor can be attached to the phone through a port or wireless connection.
For many students, especially those from economically disadvantaged backgrounds, the phone is their only device. They do their homework, interact with the school, and write their essays on their phone. It is their single internet access outside of school and the coffee shop.
So what are the affordances? First, is a vast reduction in the cost of communication by providing each person with an addressable device. Educators can send personalized messages to every student instantaneously, for almost no money. Imagine trying to do that in 2007? You could dial each person’s home and get their voicemail, or send them a letter. Mobile phones create the potential to send nudges to encourage behaviors that lead to student success.
The lower cost of communication increases the affordability of remote collaboration. Collaboration can include direct communication, like video conferencing or chat messaging. But it can also include data sharing from mobile sensors in citizen science projects and access to shared code repositories.
They also create the opportunity for personalized, context-driven learning, an area which we are just beginning to explore. Students can pull bite-sized learning experiences when they have a few minutes of downtime. I study French on Duolingo while I’m on the bus or the Tube. Using location data, we can deliver learning on the job, or create augmented reality experiences to contextualize geography, history, science, math, and sociology.
The reality is we are just beginning to explore the affordances of mobile. Health sensors attached to phones could revolutionize health care. In some nations, phone minutes are a form of currency when all else fails.
The next technology in the toolbox is cloud computing. Cloud computing is renting computer capacity from a central data center on an as-needed basis. Amazon Web Services is the market leader, but there are offerings from Microsoft, Google, and a few smaller suppliers. Cloud offerings have expanded from simple disk space and servers to sophisticated database, analytics, and machine learning offerings. All of them are available in small increments of time, from minutes to hours. Most have a free tier to get you started.
If you are a teacher or a non-IT admin, cloud computing seems like it doesn’t impact you directly. Why do you need to rent a server? But there are direct and indirect impacts of cloud computing throughout education. The affordances of cloud computing create new opportunities for educators and administrators to explore.
Cloud computing means unlimited data storage for video, audio, and text. Most cloud offerings include automated captioning, translation, and video conversion services. For an educational institution, these capabilities will radically lower the cost of video capture and captioning. While captioning may not be perfect, for most applications, it is near human level and continuing to improve. It’s also a lot easier to correct machine-generated captions than create them from scratch.
Leveraging cloud computing can also help close the digital divide. As I mentioned earlier, for many students, the phone is their only access. Even in a Chromebook school, the processing power on the device is minimal. With cloud computing, the end device doesn’t matter. Cloud enables scalable virtual desktop infrastructure (VDI) to deliver powerful desktops to any end-user device. Cloud-enabled desktops can eliminate the need for expensive workstations, allowing the students to use the resources when and where they need them.
Students in a cloud-enabled school would have access to advanced tools to help them learn and invent. Cloud tools enable relative novices to setup sophisticated analysis toolchains with a few clicks. Students can leverage advanced machine learning algorithms and explore the cutting edge of data science without the need to set up complex architectures. AWS even offers a robotics system to accelerate your robotics programs.
Cloud can also reduce the cost and complexity of managing back-end processes. Process automation systems in the cloud reduce the complexity and cost of implementing process automation. Student and staff facing systems are deployed quickly using no and low code development. So non-programmers can create new applications and processes.
We’ve all heard the hype about AI. If we believe the press, it’s either going to kill us all or take away all of our jobs. I don’t think either of those things is going to happen, so I’ll use the term machine learning.
Machine learning, in a nutshell, is a technique for developing computer programs through very sophisticated statistical analysis. It’s called learning because the machines do the analysis themselves. It enables us to analyze, categorize, and predict using more extensive and complex data sets than we could in the past.
Let’s take an example. Imagine you want to develop a program to recognize cats in a picture. If you were to sit down and write out all of the rules for identifying a cat, it would take you a very long time. You would need to write regulations for different color cats, cats with different length of hair, rules for different angles, and light conditions. You get the idea. It’s nearly impossible, and it breaks as soon as someone gives you a cat picture you hadn’t seen before.
Machine learning overcomes this problem by letting the computer train itself. Show a computer running a machine learning program a million cat pictures and tell whether it guessed right or wrong. Eventually, it will recognize cats with a high level of accuracy. Of course, it can only identify the types of cats you trained it on.
Machine learning is just getting started, especially in education. But the affordances could enable huge gains for students and teachers. Machine learning drops the cost of prediction, personalization, and knowledge capture to near zero. The cost of developing new models continues to decline as engineers develop new techniques. Cloud offerings from all of the prominent providers simplify the process. The most challenging part of a machine learning project is usually getting and cleaning the data.
So how do we use cheap prediction, personalization, and knowledge capture? The first applications of machine learning are already looking at predicting student success. Using student performance data on assignments, interaction with the LMS and historical performance, these systems create alerts for the student, teachers, and counselors. Early personalization offerings from several vendors track student progress and provide targeted activities to help the student learn each concept in turn. There have been a few instances of teachers outsourcing their knowledge to a chatbot, teaching it how to answer common student questions. The cost of replicating that part of their expertise then falls to zero.
These three technologies are just some of the technologies in the digitally literate educator’s toolkit. They have the most significant direct potential impact on learning organizations. Each one has the potential to reduce the cost of copying, sharing, personalizing, coordinating, and codifying knowledge and learning. The technologies are there now. The question is, how will you use them to improve student learning?
Jason Cole is the Editor of Learning Futures.