Weaving Analytics in Learning and Performance Measurement

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We live in a world where data on almost everything is captured. Think about how many times you have given your name and email address to different people, organizations, or sign-up forms. These information are stored somewhere and were captured for varying purposes.u00a0

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Since the dawn of the computer era, our businesses, organizations, and personal lives have increasingly been directed by data. Itu2019s a data revolution we are all facing.¹ Learning is not an exception.

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The concept of Big Data Science or Analytics has penetrated the world of learning and performance. While bigger organizations have invested a significant amount of money on analytics, others still miss to verify how it can make improvements to their organization. In the world of learning today, traditional analytics are becoming irrelevant and misleading as learning now is more fluid, which gives training completion rates little meaning. To capture its true essence, we have to rethink learning analytics with more focus on value. The value that learning professionals bring to an organization should also be visible through data.u00a0

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The Evolution of Learning Analytics

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There are five identifiable stages in learning data and reporting. Letu2019s look at them and identify which stage we should be focusing on to capitalize on the value of learning analytics.

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Stage 1: LMS Data: Administrator Focused

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This is the type of data we could generate from an LMS (Learning Management System) or a manual database (Microsoft Excel trackers). This includes a simple list of names and course completion dates for each learner. This data is useful for tracking who has and has not completed a course. In this type of report, there is no need for analysis as it aims to present only the completion rates for a specific course. The data gathered here does not bring too much value as we cannot measure how training completion relates to performance.

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Stage 2: More Data, More Answers: Learning Manager Focused

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There will be a need for more comprehensive data as the size of the learner base and learning function responsibilities increase. The ability to query, analyze, and answer questions become necessary to paint a better picture of learning. This stage requires data analysis tools or even a reporting tool that can be extracted where questions like the following can be answered:

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• Was there an increase and decrease in course completion rates for the past two years?
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• How many enrolled learners actually completed the whole course? How many did not?
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• What are the reasons why the enrolled learners did not complete the training?
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Stage 3: Graphical Presentation of Data: Learner Focused

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The increase in data gathered would mean more complex analysis so interpretation should be easier. Instead of numbers appearing in reports or dashboards, it would be easier to view results in a graphical representation such as pie or bar charts. Trends can also be shown easily using line charts.u00a0

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The dashboard could quickly show course completion rates, levels of competency, etc. This can then be used not just as a reporting tool but also a career development and business tool. Some learning management systems have this feature so it will justu00a0 be a matter of giving access to those who need to see the results such as managers, HR, department heads, etc. If you are not using an LMS then a separate analytics tool might be necessary or Excel can be sufficient enough.u00a0u00a0

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Stage 4: Dynamic Reports: Line Manager and Business Manager Focused

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Once data has provided managers and business leaders enough evidence to make daily decisions, there will be a need to speed up the analysis and reporting. The use of ad hoc reporting tools that can update, analyze, and report information whenever needed would become necessary. The level of data should be able to cover the entire life cycle of an employeeu2019s learning and achievement from the day they start until the day the leave the organization.

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Having these data readily available and generated anytime could allow a manager to see his teamu2019s weekly learning progress or a report that could help him on his team memberu2019s performance review.u00a0

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Stage 5: Big Data and Beyond: Business Focused

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Big data is a collection of data sets so large and complex that it is so challenging to process or analyze using traditional database management tools. To create the big picture, the input of big data should be more informative than the previous levels of analytics. Big data for learning would incorporate every data point across the organization like the following:

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• Demographics
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• Course completions
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• Assessment results
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• Skill levels
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• Performance reviews
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• Time on system
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• Course access points
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Big data analytics would answer new sets of questions related to learning and performance such as:

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• Which courses or certifications are correlated with improved performance in a specific business unit or department?
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• Is there a correlation between when and where course content is accessed and the level of employee engagement or performance?
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Evolve and Grow with Data Analysis

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It is indeed challenging as learning analytics require a certain level of skill and understanding to be properly used, so thereu2019s really a potential for growth in this area as we continue to bring value and partner with the business.

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Each organization needs to assess its own capabilities and readiness for higher levels of learning analytics. A good start would be building a solid database and data capture practices and reporting on essential and standard data required by particular audiences.u00a0

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References:u00a0

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1. The Growth of Learning Analytics, Brandon Hall Group, 2013

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