This post has been updated on March 26, 2020.
In a time where everything is rapidly transforming and what is state of the art today becomes nearly obsolete in what seems like an instant, employees display a lot of resistance to change. This happens partly because it is human nature not to trust the unknown and partly because it seems to happen so much that most individuals feel they are on a merry go round they just want down from.
Corporate learning makes no exception when it comes to the general preference of doing things in a familiar way and with the enormous shift from classroom to e-learning and from comprehensive units to bite-sized modules most might say that is enough change for a long time.
Yet along comes xApi, big data and analytics for learning. It is obvious that they all have great potential for optimizing L&D activity and for getting superior results, yet implementation might prove a little bumpy if it is not thought out and executed properly.
Have clear objectives and make them known
As with all major projects in an organization, implementation of learning analytics need to have clear and measurable objectives. These will need to be connected to the company’s medium and long term goals because they will have to be communicated at all levels. If there is going to be some disruption and possibly some discomfort, people have to know what positive outcome it is all deemed to have.
The organizational culture and values also need to be considered and any initiative closely aligned to them. All the recent upheaval generated by various online platforms being accused of storing (and possibly distributing) user information has shown how much of a concern virtual privacy is.
Employees will demand to know what data will be collected and how it will be used. While drawing up a communication plan for the project, it is wise to start from the premise that it may be received with resistance rather than enthusiasm, at least by a part of the employees.
The Crawl, Walk, Run methodology
After it is fairly clear why implementing Learning Analytics is necessary and how the project will be presented, it is time to begin the actual implementation. I have already mentioned that it may be met with some degree of circumspection by the learners but it may prove to be rather difficult for L&D professionals as well. xApi, big data and learning analytics are essentially very technical concepts and though the general idea is easily grasped, how they really work and, more important, how they can be made to work in favor of better learning results, may still seem like the stuff of science fiction to many.
So in order to assuage everybody’s concerns, it is best to take a moderate approach to make it an integral part of the company’s L&D strategy. A “crawl, walk, run” manner of implementing learning analytics will provide a good framework and enough time for all to adapt and figure out how to overcome challenges.
The “Crawl” phase is one in which stakeholders can identify the objectives that are most important and rank them according to priority. Maybe they need to improve user engagement, information retention, or knowledge transfer. Once the goal is settled, a single learning module (or a series of sessions) can be chosen and more information drawn from it. With the content established, a data visualization and reporting tool is established and several reports generated in order to figure out where the most valuable input comes from and in what form.
This is like a trial run that is meant to prove to all involved that it works and has value. If there is a need for it, several systems can be connected to show how the technology can make sense of what seems separate, unrelated online environments.
Moving into the “Walk” stage things start to become more complex. There can be numerous systems connected to sources of data that are used for reporting. It moves more into the realm of detailed learner behavior analysis with xApi extracting various types of data and interpreting them to give a better image of how users act in connection to formal and informal learning within the organization.
If the Crawl step was more like beta testing, this step means actually diving into the potential of learning analytics and making the best of it for more than one separate learning module. All the data can be used to create comprehensive and relevant reports for all stakeholders in the learning process. It’s also possible to correlate data into a data visualization tool that is integral to whatever Business Intelligence platform is already in use in the company.
Running is the “professional” stage of implementing learning analytics. In order for it to work as it should, apart from mastering the first two steps, it will require a listing of exact specifications and thorough planning. Obviously, it will take more time and possibly extensive testing. It is why for some companies, the Crawl stage is far enough. However, if the goal is to tap into the potential of adaptive and automated techniques it’s necessary to enroll in the marathon.
Constructing sets of rules and workflow systems around learning modules may take some time but they will lead to incredible data visualization and evaluation reports. They will make it a lot easier to figure out what is working and what isn’t as well as find the best ways to address the issues and improve the programs based on received data and apparent trends.
Learning Analytics are obviously the future. Not all companies may be prepared for it just yet and that’s why this particular approach is optimal. The crawl phase is easy to implement and will give an authentic feel of what these new technologies are and what they can do.