Big Data In eLearning: Relevance, Importance, And Opportunities
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Relevance, Importance, And Opportunities: Using Big Data In eLearning  

Specifically, in this article we will discuss the role of Big Data in eLearning, benefits of Big Data for eLearning professionals, and how Big Data impacts the future of eLearning. We will also look at ways to effectively manage and analyze Big Data.

Big Data: What Is It?

In today’s digital age, almost every interaction we have with another individual, company, or service providing system generates data. In the eLearning environment, that may include:

  • Who is taking a course (demographics- age, sex, geographic location, educational and professional background, etc.).
  • Information about how a learner first “discovered” a particular eLearning program (advertisements, social media, corporate websites, referral programs, etc.).
  • Data about the types of devices used to log-on to a particular course (desktops, smart phones, tablets, etc.).
  • The types of browsers and operating systems that the eLearning system interacts with.
  • Progress, assessments, and feedback received from the eLearner.

From an eLearning context, all of this data generated as a result of various eLearning interactions, not just the volume but also the diversity of it, is called Big Data.

The Role Of Big Data In eLearning

The role of Big Data in eLearning is as similar as sensitive intelligence gathering is to a successful military operation. All of the developments discussed earlier (new tools, new delivery mechanisms, new Learning Management Systems) are data-driven, and produce their own sets of data. Moreover, just as success for military leaders depends on analyzing and deciphering the intelligence gathered, so too can eLearning administrators and developers learn much from the Big Data they accumulate.

More specifically, amongst a host of others, Big Data’s role in eLearning is to:

  • Shed valuable insight into learning gaps across organizations.
  • Identify preferred methods of pursuing eLearning by individuals and groups of employees.
  • Highlight the strengths and weaknesses in an organization’s eLearning strategy.
  • Provide clues on how to individualize and personalize specific eLearning experiences to provide best results for learners.

Through such analytics, course developers and sponsors will be able to improve the efficacy of eLearning.

5 Benefits Of Big Data For eLearning Professionals

So, as an eLearning professional, how specifically would you benefit from analyzing Big Data? Here are a few ways that Big Data can help you:

1. Popularity.

Big Data analysis can help you identify which courses, modules, or segments of a course are the most popular amongst your audience.

2. Structure.

By looking at Big Data, you could pin point structural issues with your course design: Why do 96% of eLearners abandon a particular scenario-based exercise half-way through?

3. Deployment.

You may get invaluable insight on whether particular deployment strategies are worth the time/effort: Only 1% of students are using a Windows-based platform, with 93% learning from Android-based systems and 6% using iOS. Should you continue to support a multiple OS deployment strategy?

4. Design.

Big Data can offer design-tweaking clues for you to consider in your next course update/refresh cycle: If, in the last 5 years, no employee has ever used the “Download and print” option of the Corporate Expense Policy course – should you spend time and money updating that segment of the course feature?

5. Shortened Refresh Cycle.

Big Data is almost instantaneous – you get it immediately upon each interaction. As a result, you could start parsing the data after each batch of learners has finished the course, as opposed to waiting several months before planning your next refresh cycle.

Without the use of Big Data, these benefits may be time-consuming to accomplish, and could be rather cumbersome to achieve.

Big Data And Its Impact On The Future Of eLearning

A profound impact of Big Data will be on how future eLearning content is designed, delivered, and monitored. Some of the more discernible impacts include:

  • Decision-making on what authoring tools and Learning Management Systems a company should use.
  • Selecting the most effective delivery mechanism – corporate learning sites, social media platforms, or individual downloadable modules?
  • Data-driven course personalization.
  • Influencing budget allocations for specific eLearning strategies.
  • Analysis of individual and group behavior as they interact with a course.

All of these insights will help eLearning professionals build bodies of knowledge that will shape the future of eLearning.

7 Best Practices To Effectively Manage And Analyze Big Data

To take full advantage of Big Data in an eLearning environment, course sponsors, developers and eLearning professionals must effectively manage and analyze the data. Here are a few best practices to help you get started on that front:

1. Data Objectives.

Start out by defining clear-cut goals and objectives for why you are collecting the data, and what you intend to accomplish from analyzing it. For instance, “Employee Skills Improvement” should be supported by the types of skills that need improvement, and measurable metrics of what “improvement” means.

2. Data Assimilation.

As indicated earlier, Big Data has multiple sources (authoring tools, Learning Management Systems, social media platforms, corporate eLearning sites). Make sure you cast a broad net and gather all of that data instead of being selective in what you harvest.

3. Data Rationalization.

Not all of the harvested data (the make/model of the device used; the version of the OS; the ID of a corporate terminal used to access the course) will be “actionable” from an eLearning stand point. Rationalize (parse) your data based on the analysis goals and objectives set out in Step 1.

4. Data Prioritization.

Sometimes, you may end up having several priorities (skills improvement; course popularity, etc.). Prioritize your data elements based on the importance of the analysis you wish to conduct.

5. Data Metrics.

Of course, Big Data doesn’t always come in structured packages. To play its role in enhancing the effectiveness of eLearning environments, you must use appropriate metrics to get Big Data to reveal its secrets. Some metrics might include: Student Progress/Completion; Proficiency levels; Ratings and Evaluations; Relative Rankings and Return on Investment.

6. Data Analysis Tools.

It’s now time to use some tools to conduct your analysis. Most often, your corporate Learning Management System will include a set of big data analysis tools as well. If you do not have built-in analytics within your Learning Management System, you may need to tap into 3rd party tools like Google Analytics to see how students interact with your online courses.

7. Data Protection.

The most important aspect of managing vast amounts of Big Data is security. Because of its sensitivity, ensure your eLearning data is well protected, possibly through data encryption – especially if it’s being stored outside the corporate environment in the Cloud.

Final Word

Now that your data is assimilated, rationalized, and analyzed, you need to take steps to act on the resulting analysis. Use your findings to tweak, update or revamp your eLearning strategy, or to make individual courses more effective.

In the Instructional Design for eLearning: Essential Guide to Creating Successful eLearning Courses book, you will find tips, tricks, suggestions, and best practices for designing your future eLearning courses. This book is also available in Spanish. In addition, you may be interested in the Instructional Design For eLearning course, which also focuses on creating effective training materials.

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