Data-Driven Education: What it Really Means Today

By: James Stanger

Data Driven Education - What it Really Means Today thumbnail

When I was in high school, our volleyball team had a couple of really bad years in a row. When we got a new coach, she put a big sign up on the entrance of the locker room that read, “You’ve got to start somewhere.” No one could quite figure out what that phrase meant, until my girlfriend talked me into asking her. The new coach answered, “You start with the hearts and minds of your people, Mr. Stanger.” She intimidated me, so I didn’t dare to ask more about what she meant.

However, the more I’ve thought about what she said and how she quickly created a great team, I realized she was right. Change starts with figuring out what folks are thinking, feeling and doing. This applies to every human situation, including information technology professionals. Healthy companies that can transform when faced with challenges know that transformation starts with people. Which explains why nothing is more transformational to an organization than education and training.

Sure, a great CEO can inspire, and great technology can allow us to achieve great results, but people still matter, and transforming people is critical to prepare the workforce.

What does the workforce need to be prepared for?

  • Working with generative artificial intelligence (AI) and predictive AI: Organizations need employees that know how to train, as well as work and play with their AI and machine learning (ML) co-workers
  • Using the cloud in a smart manner
  • Creating automated solutions using Internet of Things (IoT) and operational technology (OT)
  • Using data generated by IoT and OT
  • Implementing zero-trust technologies, such as user and event behavior analytics, and SIEM integration
  • Sophisticated security analytics and event correlation for applications, APIs and OT
  • Better incident response

IT education can drive real digital transformation, because even in an age where ChatGPT writes papers for our kids and writes really cool, original poetry to our loved ones, people still play an integral role in making these things a reality.

My high school volleyball coach was right – and that “somewhere” is not a place, it’s people. If you’re going to transform people in today’s revolutionized workplace, you’ve got to know them, which includes collecting data about them as they learn.

Moving Beyond Traditional Approaches

When it comes to collecting data about employees, I’ve found that many organizations are just barely moving out of the paper-based world, even if they’re using e-Learning. Many of which spend little time reviewing the results of student learning over and above completion rates, and few organizations have taken the steps necessary to gather more meaningful data about how their employees learn.

But those that do gather this important data have been able to create valuable resources that benefit their businesses, like:

  • Data marts: A collection of information about a particular area of a business. In essence, a database dedicated to a specific subject. You can create a data mart, for example, about people and how they learn.
  • Job marts: A collection of positions in the company, complete with a list of skills and related skills pathways.

Using these types of resources, the most sophisticated organizations are able to crunch the data left behind by students as they take courses in learning management systems (LMS) and various platforms. By searching through the log files left behind by students, they can learn quite a bit about how to transform people, and the way that organizations engage with them.

Leveraging Learning Artifacts

The data, or “learning artifacts,” left behind can include:

  • Time spent on a topic
  • Resources that a student consulted outside of a course
  • The number of times a learner re-read a topic
  • How learning resources were used: For example, how much a learner used videos versus reading through the text or listening to a topic

Depending on the way that learning resources, such as video files, web pages and even paragraphs of courses are tagged, organizations can glean critical information about how their employees learn. This tagging of learning resources is called metadata. Metadata can be used to describe each learning tool, or resource. By reading through the metadata, learning professionals can identify critical learning patterns. Many advanced products such as Cognii, Google AI and TensorFlow use AI and ML to sift through data more quickly.

Progressive Data Gathering for Learners: An Applied Example

Many companies pride themselves on being as data-driven as possible. My friend works for a large retailer that is no exception. He was recently put in charge of a project designed to improve the security best practices of several teams of coders. These programmers worked to create customer-facing code using templates that had been created over the years to make sure that their code adhered to certain standards and could be created as efficiently as possible.

The Problem

Traditional education solutions often don’t work very well when it comes to developers, because the education offering is often (and sometimes, inevitably) conceived and created separately by non-developers. As a result, the program can be far removed from the practical world. Developers tend to perceive such offerings as being repetitive and, even worse, out of touch. That happens even if the learning resource teaches relevant skills.

But in this case, my friend had a serious, unique problem. He discovered in his research that developers were forced to use resources and templates that led them to create code that used weak encryption and storage resources that weren’t particularly secure. I know that sounds crazy, but these things often happen in a gitlabs-driven, commit-based world. The “make do and mend” realities of practical software creation often lead to problems. In this situation, no amount of training could solve this problem, so he had to get a bit more creative.

The Solution

To solve this problem, my friend did two creative things:

1. He worked with department leaders of the coding teams to make a pre-education change and gathered data about the institutional procedures and templates that, in many ways, caused the security problems. He engaged in some focus groups, and a few surveys, and then with a few third-party security workers (I was one of them), and several senior security workers in his company.

As a result, my friend got the application development department managers to make changes in the templates that the developers were forced to use. This alone helped reduce security findings quite a bit, but that wasn’t all.

2. Following up on the pre-education change, he was able to create learning resources that taught developers how to use these new templates. He also tagged the learning resources with metadata that made it possible for learning managers and department leaders to sift through the learning artifacts left behind by these application developers as they took the courses.

As a result of my friend’s work, the application developers were able to create code quickly, and reduce security findings concerning encryption and storage by 80% to 30%. That’s quite a transformation. What’s more, it’s a transformation that happened because my friend was able to use a data mart and tagged data to tease out useful, actionable information about people, a great example of the progress organizations can make with the power of data-driven education.

Interested in learning more about CompTIA’s custom training solutions? Find out more.

Read more about IT Workforce.

Tags : IT Workforce