Macrolearning To Microlearning And Ways To Transform Your Course Library

Macrolearning To Microlearning And Ways To Transform Your Course Library
Summary: This article is simply about the complex process of moving from macrolearning to microlearning and updating your courses for mobile learning.

Moving From Macrolearning To Microlearning: How To Transform Your Course Library

Educational technology—or eLearning—isn’t new. While microlearning and video learning are trending industry topics, not many companies have implemented mobile and app learning micro-courses. In fact, according to an ATD study, only 34% of all companies have implemented mobile learning programs while mobile-only users now outnumber desktop-only users by 80% to 14%.

Currently, much courseware is 'locked up' in one-hour SCORM packages and unavailable in bite-sized pieces which then begs the question: How do we leverage current learning assets for microlearning?

As a solution, we developed a scalable content conversion process through our Vector Solution’s RedVector GO App, wherein we modernized our course library of over 5,000 accredited courses—without completely recreating them—to meet microlearning and mobile-learning demands.

The process we developed to convert hundreds of courses into thousands of micro-clips of valuable, on-demand microlearning for an increasingly mobile workforce was as follows:

1. Identify Easily 'Convertible' Content

The first goal was to identify the types of courses and content that would most easily be consumed in a microlearning experience by identifying the content that could most easily and quickly be converted into short microlearning segments.

For example, we determined that text-based content spanning several pages would be very difficult to display. However, video-based content was already segmented into 3 to 5-minute standalone learning objects, making content easier to break down and consume with the help of an app.

The objective, then, was to sift through a course library and determine the most important skills learners would need and the courses that would deliver that content in an easily transferable format.

2. Identify Most 'On-Demand' Content

The second objective was to identify courses that included content topics and skills commonly used on the job that would need to be available 'on-demand' while working.

The main goal was to deliver content that was applicable at the 'point-of-need' to improve the performance, accuracy, and safety of workers by providing the right information at the right time.

In surveying clients, our product management team determined that there were roughly 600-700 hours of SCORM course content that supported workplace performance. Due to the volume of that video-based content, however, we needed to develop an innovative microlearning programming model that would enable us to identify only those learning objects that were workplace-relevant within each SCORM course.

We determined that only some of our compliance-based courses had content that would be organically useful on the job and extraneous content, such as introductions and conclusions, would need to be disregarded. In doing so, we were able to identify over 10,000 separate microlearning video-based objects in the 600-700 hours of video-based content.

3. Make Content 'Searchable'

Since microlearning focuses on very specific, isolated topics, metadata would be needed to allow users to easily search for—and find—the information necessary. And with on-the-job searches often being time-sensitive, irrelevant search results would not only lead to employee dissatisfaction but also wasted time and money for the company.

Therefore, the RedVector GO App needed to identify metadata, independent of human intervention.

The third objective, then, became to develop an 'innovative programming model' that would extract data from the SCORM courses from the course production database, and then place the extracted information into a microlearning database optimized to include only microlearning metadata.

This back-end architecture of the microlearning app would allow for faster queries of information and the efficient retrieval of video-based content and its associated metadata.

4. Scale The Process

This streamlined process would, then, allow content creators to automate the conversion of an extensive course library. Now, efficient and effective microlearning with highly targeted, skilled content can be delivered whenever and wherever the learner needs it.

Accessibility is key for professionals who are not able to escape the job site or plant floor. With microlearning apps like Vector Solutions’ RedVector GO, workers can easily access information on specific topics to refresh knowledge of a situation they may currently be facing on the job. Such a contextualized learning strategy not only increases productivity, efficiency, and safety but also increases comprehension rates.