Personalized Learning

Publication Year: 
Category of Information: 
Sample size: 
Sample description: 

"Personalized learning refers to the range of educational programs, learning experiences, instructional approaches, and academic support strategies intended to address the specific learning needs, interests, aspirations, or cultural backgrounds of individual students." (Source:

Today’s learners want a learning experience that fits their personal needs, learning speed, preferred learning style, and, most importantly, their learning pathway – in other words, learning personalized for them. In a personalized learning environment, contents display is adapted to individual learning styles and needs. Content discovery moves from a “course catalogue” style to an adaptive model. In the old model, everyone learns from the same materials at the same pace. In an adaptive model, students are presented with learning activities based on what they know, what they need to know, and what has worked for other students like them.

[...] The adaptive learning mechanism comprises embedded assessments associated with the designed content.

"Technavio’s market research analyst predicts the global adaptive learning software market to grow at an impressive CAGR of close to 31% during the forecast period 2016-2020. The adaptive learning software market in the Americas is the largest among all the geographical segments and is expected to generate revenues of over USD 2 billion by the end of 2020." (Source:

An organization ready to support and build personalized learning needs to begin collecting learning analytics. Learning analytics, in many ways, is “big data,” applied to education.

"Whereas traditional forms of analytical processing rely on existing management data, such as student demographics, grades, and recruitment figures, more recent approaches to analytics rely on data that has greater variety and arises from traces left as people use IT systems. This is a central concern for learning analytics, where the data arises from normal use of multiple pieces of software designed for accessing learning resources, social interaction, content creation, etc. In many cases, therefore, practical learning analytics requires that data moves from operational to analytical systems and be put to a different use than originally intended. For example, the data structures in a VLE or LMS are likely to have been designed not for analytics, but to realise teaching and learning use cases - e.g. for accessing video content, participation in forums – in a way is technically scalable and maintainable. When statistical processing or data mining is undertaken, for example to support analysis of learner engagement, data has to be re-interpreted. This situation is further amplified by the necessity of combining data from various sources, or maybe to use cloud-computing based data mining engines, to build, test, and apply useful statistical and predictive models." (Source: Learning Analytics Interoperability – The Big Picture In Brief - Adam Cooper, Cetis, University of Bolton, UK)

As personalized learning integrates into the corporate space, learners will be able to collect and report on their own learning accomplishments using the Experience API, also called xAPI or Tin Can. Organizations and learners can use the Experience API to collect data outside of an LMS from any learning experience, completed in any environment, on any device.