Big Data: Instructional Design Boost

Big Data_ellicom

By Natalia Matusevscaia, Learning Strategist at ellicom

What is all the fuss about over big data? How important is big data for instructional design? What do we need to know about it? How can it be used within training frameworks? All of these questions have attracted the attention of instructional design online communities. I took an interest in the topic and read many articles and blogs posts. Let me tell you, big data is that missing piece we are looking for in instructional design in order to build robust learning solutions for diverse learner populations.

Let’s start from the very beginning: what is big data? The term first appeared over 20 years ago (can you believe it?), in 1997. Due to rapid technological development, information growth, and new challenges in data management, several definitions of big data evolved over time. The term is most commonly defined as a dataset that is so large in volume that database software tools are unable to capture, store, manage, or analyze it. The term big data also applies to separate pieces of information from large datasets, such as learners’ digital footprints.

As an instructional designer, imagine you are able to determine how much time learners spend on every activity you create and you can adjust the task difficulty level for each particular learner. Or maybe, you can personalize training activities when you know where a learner clicks and is most engaged. This is where big data can come into play and improve your approach to training design. Collecting information on the clicks made, time spent, buttons activated, tasks completed, repeated actions, etc. will provide you with a clear portrait of each learner and their personal preferences.

To build a course, instructional designers focus mainly on what is referred to as the “average learner” within a group of learners. Indeed, it seems futile to strive for personalization and to try and cater to learner differences when you need to train hundreds or even thousands of employees within a short timeframe. However, with tools such as TinCan API, it becomes possible to collect some of the big data needed to track and analyze the range of experiences a person has with the training. Here are three main advantages to collecting and analyzing big data:

  1. It enables you to observe learners’ behaviour in online training in real time because all the information is collected immediately. This means you can plan for and begin implementing changes and adjustments right away instead of having to wait for post-training assessments.
  2. It enables you to track learning preferences. This means that you can record and analyze what, when, where, and how a learner engages with the training content. This information allows you to tailor the learning to meet learners’ needs.
  3. It enables you to discover learning patterns. This means that you can build different learning paths with the same content within the training program in order to cater to individual learners and their needs.

Big data is everywhere. Let’s take advantage and use it to improve instruction. Why not boost your instructional design solutions with big data?

 

References:

Pappas. C. (2014). Big data in elearning: The future of elearning industry. Retrieved from https://elearningindustry.com/big-data-in-elearning-future-of-elearning-industry

Press. G. (2014). 12 big data definitions: What’s yours? Retrieved from http://www.forbes.com/sites/gilpress/2014/09/03/12-big-data-definitions-whats-yours/#6327b1fe21a9

TechTerms: Digital footprint. Retrieved from http://techterms.com/definition/digital_footprint

What is the Tin Can API? Retrieved from https://tincanapi.com/overview/

Wroten. C. (2013). Big data and how it’s changing e-learning. Retrieved from https://elearningindustry.com/big-data-and-how-it-is-changing-e-learning

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