Learning Analytics Glossary

Below you can find explanations and examples of the most common data analytics terminology used in L&D

Learning Analytics

Learning Analytics is the art and science to turn learning and development related data into actionable insights through the measurement, collection, analysis, and reporting of data about learners and their contexts, to understand and optimize learning experience, transfer, impact and the environments in which it occurs.

Example: Analyzing the number of employees who completed a mandatory compliance training within the stipulated time.

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Data

Data is a collection of discrete or continuous values that convey information, describing the quantityqualityfactstatistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. In the context of learning analytics, data refers to all digital elements of information.

Example: A list of employee names and the courses they’ve registered for.

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A Data Table

A table is a structured set of data held in a computer, especially one that is accessible in various ways.

Examples: A table containing

  • All employees
  • All training locations
  • All training titles
  • All completions in October
  • All available videos
  • All training providers

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Learning Analytics Skills

The abilities required to perform tasks related to learning analytics: measure, collect, analyze, and report data specifically related to learning contexts. Depending on your role in L&D learning analytics you might require only basic or intermediate learning analytics skills like data interpretation from a dashboard, or being able to write a well formulated analytics question. However if you have the ambition to become a learning analytics expert or head of learning analytics, you might require additional skills like data management, data modeling and visualization.

Examples:

  • Critical Thinking
  • Data gathering techniques
  • Problem Definition
  • Data privacy & Ethics
  • Data cleaning
  • Data integration
  • Data driven design
  • Statistics
  • Excel, Power BI
  • Trend Analysis

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Up and Re-Skilling

The process of developing new skills (reskilling) or improve existing skills (upskilling). Or supporting the development or improvement of (new) skills.

Example Upskilling: Improving your MS Excel skills in order to be able to do more exploratory data analysis with MS Excel

Example Reskilling: Learn how to get to the bottom of analytics questions and requests by applying the ‘5x why and a how’ approach

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Data Type

A data type describes the sort of data captured in a data field within a table. Data types determine what type of analytics you can do: text related data types can not be used in calculations, while number related can. Date formats also behave differently.

Also if you want to merge data, you must ensure that the data to be combined is always of the same type

Examples:

  • String: a string of (any type of) characters
  • Integer: a whole positive number
  • Date or DateTime: A specific notation for year, month and day, sometimes including time (hours, minutes, seconds)
  • Boolean: A data type where only 2 values are allowed: ‘Yes’ or ‘No’

Link to Wikipedia