Technology changes everything and even the most inert systems such as education are not immune to its influences. Electronic learning has become a standard among agile businesses a long time ago, but now it keeps evolving thanks to state of the art technologies.
Of course, the evolution is not done yet as there are many new ways to enhance eLearning experiences. One of the most interesting solutions is to utilize the power of data analytics and make eLearning a more accurate and productive industry. By definition, data analytics is the science of analyzing raw data in order to make conclusions about concepts or phenomena.
Being a digitally-powered activity, eLearning gives educators tons of valuable data that they can use to improve educational processes. In this article, we will discuss different ways and mechanisms to make it possible. Let’s take a look!
Data analytics types and techniques
If you want to make use of elearning data analytics to the fullest extent, you need to understand how it works and which types you are dealing with here. First of all, we can divide the process in two different segments:
Descriptive analytics: It’s a special type of data analytics that provides users with insights about the current state of affairs. For example, you can use it to analyze training programs, online courses, onboarding sessions, and so on. This is exactly what custom writing services like Australian writings do when they want to assess customer satisfaction.
Predictive analytics: The second type is called predictive analytics. As you can probably guess already, its goal is to identify forthcoming trends and help you to improve eLearning processes in the nearest future.
Of course, each of these two types is relevant only if you conduct research on a regular basis. Learning procedures tend to evolve over time, so it’s your job to establish analytical patterns which keep track of precisely determined periods.
This basically means that you’ll get the best results if you analyze eLearning in milestones. For example, you can assess each course individually or follow lectures semester by semester to compare key findings.
Besides that, another fundamental detail is to ensure quality data presentations. What makes this feature so important? Well, you do need to present the results to decision makers and convince them to make the change based on data-driven insights.
Prepare for data analysis on time
Before you rush into data analysis, we strongly encourage you to prepare for the process by asking yourself a few major questions. Let’s take a look at each one here:
1. Why do I need to analyze my eLearning program?
The first question is rather obvious as you probably don’t want to waste time and money doing studies you don’t need. For example, content writing experts conduct UK Writings reviews or Superior Papers reviews to determine whether the agency is good or bad. The same goes for eLearning, so make sure to know your plan before initiating data analysis.
2. Do I know my key metrics?
The results of data analytics must be tangible and easy to act upon. That’s why you need key performance indicators (KPIs) to tell you what to do next. KPIs can cover anything from average grades and activity scores to site logins and course completion. Your mission is to find the most relevant metrics and use them in your reports.
3. How can I implement data analysis?
The last question is often problematic for eLearning professionals because they do not know how to put ideas into practice. This is where you need to be careful because data analytics can take a wrong turn in case you don’t answer this question accurately.
The first thing you need to introduce data analysis in education process is the database. Can you withdraw relevant information from your eLearning platforms? If yes, the other thing left is to have serious data analysts who can extrapolate meaningful conclusions out of seemingly unrelated sets of information. This may be someone from your team, but if you are not sure of their qualities – perhaps it’s better to outsource the job.
How to use eLearning analytics
Now that you understand the functioning of data analytics and know how to prepare for it properly, you should also figure out how to use it to enhance your eLearning courses and training programs. Generally speaking, your mission has two distinct objectives:
Educational goals: Improving your eLearning studies is the single most important aspect of the process. No matter what sort of content you are dealing with, your job is to improve the experience of eLearners and boost their performance long-term. You should use every insight to remove irrelevant parts from the training program, add meaningful novelties, and improve the course in general.
Business goals: Of course, the second aspect of data analytics is also very important because you do want to make eLearning as productive as possible. The goal is to maximize return on investment by cutting costs, reducing bounce rates, and targeting the right candidates.
However, you have to pay attention when using data analytics for eLearning purposes. A number of obstacles may occur in the process and you need to be aware of them on time.
One of the major concerns for most eLearning systems is affordability because they need to accumulate funds for data analytics tools, specialized training, and mentoring. Although long-term benefits surpass such one-time investments, a lot of eLearning decision makers still hesitate to give green light to these projects.
Of course, there is always the timing issue as many eLearning specialists don’t have enough time or don’t want to spend it embracing new technologies. If this happens to be the case with your team, then you should explain the privileges of data analytics and tell them how it can make their jobs much simpler.
eLearning already changes the way we approach traditional education and corporate training, but it still has a long way to go thanks to data analytics. The power of information is so immense that it can easily take eLearning to the higher level, but it requires a good strategy to make use of all data sources.
In this post, we discussed how to unleash the power of data analytics for electronic learning. It’s a complex process, so feel free to write comments if you have other useful information to share with our readers – we would love to see your opinion about this exciting topic!
This is a guest post by Tiffany Harper. Tiffany is a talented writer from New York, an extremely active woman, and a real leader. She began her career as a journalist and later proceeded it as an educational writer and editor. Now she works as the subject matter consultant with academized reviews, mostly in technology and educational area. Please do not hesitate to contact her on Twitter.