Breaking down Big Data – Analytics in elearning

 

You may have heard of Big Data at the workplace or over the news and it seems to be a concept introduced in the recent few years. However, this idea was actually conceived 7,000 years ago. Over the course of centuries, people have been trying to use data analysis and analytics techniques to support their decision-making process. The ancient Egyptians around 300 BC already tried to capture all existing ‘data’ in the library of Alexandria. Moreover, the Roman Empire used to carefully analyze statistics of their military to determine the optimal distribution for their armies. 

The volume and speed in which data is generated has changed over the last two decades. With technological advancements and the presence of social networks, more and more data is created on a daily basis, and these data can be used by businesses in different aspects, e.g, customer experience, users’ behaviour and other analytics. 

Simply put, Big Data refers to large and complex data sets. These huge data sets can be used to analyze business problems you would otherwise have not have been able to address before. Big Data incorporates internal and external data. Some examples of internal data are customer databases, emails, medical records, mobile applications, sensitive information like network and server log files. Examples of external data are weather and traffic conditions, financial markets and geographical information. A report by Forbes stated that UPS saved $50 million annually in fuel, maintenance and time just by using data they collected to minimise the number of left turns drivers had to make. Less time spent waiting at traffic lights for these turns, along with installing specialized part sensors in vehicles, has also reduced carbon dioxide emissions and saved 1.5 millions gallons of fuel. 

Types of Data Sources 

Where does all the data come from? 

The bulk of big data generated comes from three primary sources: social data, machine data and transactional data. 

  • Social data comes from general social media applications like Facebook, Twitter, Instagram – likes, picture and video uploads, comments and shares. These data provides invaluable insights into consumer behaviour and sentiment which can be very influential in marketing analytics. Search engines like Google and tools like Google Trends can be used to increase the volume of Big Data as well. 

 

  • Machine data is information generated by industrial equipment. sensors that are installed in machinery, web logs which track user behavior. Sensors such as medical devices, smart meters, road cameras, satellites, games and the rapidly growing Internet Of Things will deliver high velocity, value, volume and variety of data in the very near future. This type of data is expected to grow exponentially as the Internet Of Things grows ever more pervasive and expands around the world.

 

  • Transactional data is generated from all the daily transactions that take place both online and offline – invoices, payment orders, storage records. 

 

Yes, we know that’s a whole lot of data. So how does this affect elearning and how can Big Data change the way training is conducted? 

Big data in elearning

Big data is reshaping the future of elearning as new systems are able to better track the user activity, ultimately understanding the impact of a course in developing a learner’s specific skills. This way, we can really understand what’s going on at the end of any module by tracking the whole learning experience and monitoring how effectively a course is applied to specific skills. Additionally, Big Data can help you understand the behavioural patterns of your learners more accurately, allowing you to gather highly valuable information on how they learn and help you to make better decisions about your learning structure as well as identifying design flaws. 

With the huge amount of collected data on hand, administrators might not know how to process them. In essence, the gap between effectiveness of data usage and gathered data flow is understanding what decisions these data can help make and how it can actually help.

In order to simplify the process of analysing Big Data in elearning, let’s break the process up to 3 main steps – Objectives, Metrics and Analysis. 

First up, let’s understand the objectives of your data. 

You have to be clear on the skills that need improvement and the metrics that define improvement. Therefore, it is important to rationalize your data. Not all data that you gather can be useful to your goals. Some will be actionable, and some will be redundant. 

The next step is to determine the key metrics that will allow you to measure progress towards your high-level objective.

These metrics are also known as training key performance indicators (KPIs). For example, if an employee is in the course of completing a training module, his/her progress, time spent, activity results, comments and any other data produced can be metrics you use to measure and collect data from. All the data collected forms your Big Data in elearning. 

If you are new to elearning data analytics, and do not know where to start, here are some common learning KPIs to focus on when reporting: 

  • Training Completion Time 

This metric can help you discover which employees sped through the course and which of them are falling behind. You will then have clear data about how much time is being spent learning and  learners are faring within the course and create clear solutions to help learners improve their learning process. 

  • Quiz Attempts 

It is crucial to track how long employees take to finish the course but it is just as important to figure out where they get stuck and how many times they try to pass the same module. For example, an employee might take a few tries to pass a certain test while another may get it right the first time. In this case, you will be able to gauge if a course is too hard or too easy and better adapt the course to your learners’ needs and capabilities. 

  • Activity Scores 

Tracking activity scores show the level of efficiency and impact the course has on the learners’ performance. By keeping track of your learners’ scores, you will know how much of the targeted information has been grasped by your employees. 

Sometimes, you will even need to have a combination of two metrics, for instance, the final activity scores combined with the number of attempts and completion time are a great indicator of your training success. This combined data will inform you on how successful learners were in retaining knowledge. 

  • ROI Tracking

At the end of the day, it is about the company’s return on investment. Upskilling your employees will improve their performance at work which in turn benefits the company; If your employees are thriving, the company will as well. Therefore, taking a closer look at the impact of training can assist you to make decisions around your company’s L&D strategy moving forward. The Lumina Foundation has done several studies on the ROI of training, with one of the case studies showing that Cigna realized a 129% ROI from its educational reimbursement program between 2012 and 2014. 

You might find that consistent employee training in certain topics resulted in better business performance, and thus adjust the budget to offer more educational opportunities for your staff. However the opposite can also happen and this is when you would need to reevaluate the efficiency of your strategy.

  • Feedback and Surveys 

Statistical data aside, one of the best elements of online training is the instant and regular feedback you get from employees before, during, and after course completion. Feedback and surveys are the best way to know what is lacking about your training program as well as the trainers’ performance, if any. Collecting this type of feedback will inform you what kind of training you should offer, the effectiveness of the current training, and satisfaction with the style and method of training. 

After determining your key metrics, what’s next? 

Big Data Analysis 

These data will only increase in size overtime, and subsequently, you must have a scalable, flexible analytics tool. Learning Management Systems are amongst the most effective analytics tools in elearning. The system allows you to track learner progress and insights of their learning habits. 

If the scale of your Big Data becomes too huge for your organisation to handle, know when to outsource a data analyst to further effectively analyze and manage your data. 

Big Data will be able to help you with evaluating the quality of your course and how well it worked. How much time do your learners spend on it and what parts of a course are easy or difficult for them to understand? The data collected allow us to know about the shortcomings of the content and what should be improved.

Conclusion 

The integration of Big Data has opened space for innovation, providing both trainers and learners new and improved ways on learning processes, reshaping elearning structures throughout the years. Hence, understanding how to use learning analytics and data effectively will help you to improve your existing training programs. Establishing a strong analytics framework is the very first step in that journey.

 

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