Almost half a decade back when Peter Drucker famously stated, “You can’t manage what you don’t measure”, little must have he known that ‘measurement’ would one day create a huge amount of data (not to forget the 900,000+ servers Google stores it in!). Huge numbers and serious implications.

It’s astonishing how ‘Data’, a word only associated with files inputted or stored on the computers, hard disks, etc. has turned into a worldwide phenomenon today. Internet, the devices we use, each click, each Like, each Share, the booking of a ride the content watched – is generating data every minute of every day! Data Never Sleeps 6.0, a report by DOMO, points out that “Over 2.5 quintillion bytes of data is created every single day, and it’s only going to grow from there. By 2020, it’s estimated that 1.7MB of data will be created every second for every person on earth.”

There really is so much of big data out there that begs the bigger question of ‘how is the data utilized?’ to be answered. Psst! Earlier this year, 30 organisations, including Facebook, were being investigated as part of a national inquiry into the usage (or rather, the leak) of personal data and analytics for political and commercial purposes.

In comes Analytics!

Which brings us back to Drucker’s thought that transformed the perception of business management. How does analytics work within an organization? While earlier it meant manual data collection, comparison, analysis and conclusions plotted as charts, graphs, etc., the modern workplaces handle it differently; either through a centralized people portal (like HRIS/ HRMS) or through a Learning Management System (LMS).

Right from Profile Creation to tracking the time spent on eLearning, most of the systems have various methods of collecting data. But ‘what’ to collect is the singular, common theme across all. For various functions that utilize data analytics, there are specific patterns and preferences that can be used to predict or manage consumer/ stakeholder/ learner behavior. These patterns or metrics set the premise for data analytics at the workplace. In other words, ‘knowing what to measure’ drives the show and not to forget the analytical tools that do the actual heavy-lifting. In the learning context, it means identification of factors that aid continuous improvement and value addition – from the L&D training perspective (while learner performance improvement and upskilling continue to remain a priority).

LMSs are the go-to tool for learner analytics. From analyzing the learner performance to identifying content that has scope for further improvement, the LMSs (Admin analytics) can sort it all.

As puts it, “the analytics process starts with data collection, identifying the information needed for a particular analytics application and then working to assemble it for use. Data from different source systems is transformed into a common format and loaded into an analytics system, there it undergoes profiling, cleansing, preparation, governance and only then is the analytical model built using predictive modelling tools or other analytics software and programming languages is put to work.”

Thankfully modern tools make all this pretty easy, with interfaces designed to make the whole process as simple as a click-and-show (and maybe a few additional inputs and moves).

LMSs use data in various ways considering that every move that learner makes online, leaves a digital footprint, information that can improve the learning strategy and L&D function in general. So, what can LMS analytics tell us?

  • Learner Time Spent

The average American spends 24 hours a week online. But that doesn’t mean they are all into learning now, are they? This is where the term, ‘Man-Hours’ comes in, in L&D parlance. Right from the time of logging-in and onward, a learner is considered online. The time spent within the learning framework is usually the first indicator of how successful a learning endeavor is. Easy drop off indicates the inefficiency of the learning design or points towards an internal glitch. However, from the learning analytics perspective, the number of hours spent on eLearning along with those spent in ILT/ Classroom Training sessions forms the crux of man-hours.

Image 1: UpsideLMS Admin Dashboard showing Learner Man-Hours spent on ILT

  • Learning Preferences

Different learners, different learning preferences. Nothing new about that. Within an LMS setup, the Content Completion Status for each element (Course, Video, Assessment, ILT/ Virtual Classroom session, etc.) acts as an indicator of learning preference. The type of learning content learners successfully complete not just helps the guardians of L&D to, monitor their learners, but also provides insights into the type of learning material they need to invest in.

Image 2: UpsideLMS Admin Dashboard showing Content Completion Status

  • Learner Satisfaction

While Learning polls, surveys, etc. tend to capture the user sentiments based on the responses shared, data analytics portrays the truth by tracking and drawing insights from the time spent, the curriculum completion status, the overall progress and other parameters through graphs and charts that showcase the general learning trends while pinpointing areas of concern and, most importantly, the actual level of learner satisfaction.

Image 3: UpsideLMS Admin Dashboard showing Curriculum Status

Image 4: UpsideLMS Admin Dashboard showing Popular Curriculums (either based on Ratings or Completions)

  • Criteria for Personalization

According to IBM’s report, “62 percent of retailers report that the use of information (including big data) and analytics is creating a competitive advantage for their organizations.” Data and Personalization go hand in hand, more so in terms of learning where all analytics is focused towards improvement. The data collected about a single learner, usually brings out the learning preferences, the device preferences, the skill gaps, etc. All of which, act as the criteria for personalization. And LMSs can easily be used to reach out to individual learners through built-in notification frameworks and personalized learning can be pushed in too.

  • Learning Progress and Learner Active-ness

The reporting feature of an LMS, can track user progress status for all the learners in the organization. This can be used to generate progress reports for individuals or groups and can be linked to how active the learner is too. Learner Activeness is generally a factor of Curriculum Completions, CPD Points and Gamification Points.

Image 5: UpsideLMS Admin Dashboard showing Overall User Progress

Image 6: UpsideLMS Admin Dashboard showing Most Active Learners

  • System Health

Gauging the health of your LMS starts with understanding if, first and foremost, users are ‘logging’ into the system, followed by the ‘completions’. The latter also indicates the level of learner engagement, motivation, and participation. Low completion rates are a tell-tale sign of low learner buy-in to the L&D intervention (or it may also be a sign of lack of knowledge about L&D benefits, or worse still, low system usability).

Image 7: UpsideLMS Admin Dashboard showing System Health for User Logins and Curriculum Completions w.r.t. a pre-set threshold (for a month) for each criteria

Analytics requires data, and data generation, in turn, depends on various components (not to forget SCORM, xAPI, etc.). But, data in its crude form would hardly be of any value unless it is categorized or presented as reports that provide an arena for comparison and scope for improvement. In the learning context, i.e. Learning Analytics using an LMS, it means intelligent tracking, deep-dive reporting and the simplicity in comprehension. After all, Learning Analytics is about ‘managing what you measure’, and improving in parallel.

To know how you can get Intelligent Reporting and L&D data deep dive, register for UpsideLMS Live Demo.