The terms personalized and adaptive are often using interchangeably in the learning arena, and it is this casual substitution that has led to some confusion. However they are different learning techniques, and can be used together to deliver organizational benefit in a modern learning scenario.
Personalized Learning provides employees with various learning paths, programmed by L&D according to a number of factors such as:
Employees’ baseline knowledge levels are identified initially using a test. Thereafter, learning content is produced for the learning system to deliver to the employee. As the employee takes the training, the learning path will be opened based on the individuals scores. I.e. once they attain a certain score the next module will open up. Therefore personalized learning is primarily linear.
Adaptive Learning, takes into account the same individual characteristics as personalized learning, but uses sophisticated data driven and nonlinear algorithms to:
We posed the question at the outset, do we need both personalized and adaptive learning? The answer is yes, and an example will be used to demonstrate this.
Personalized learning creates a starting point
Arthur and Nancy join Blueco as forklift operators in the warehouse operations division. Their job role specifies that they need specific knowledge and skills including:
Arthur and Nancy take some short online tests to evaluate their current knowledge levels. Their scores indicate they both have a number of knowledge gaps and as such they both begin at Level 1.
They both complete their learning and take the final test. Arthur scores 75% and Nancy scores 95% in terms of their knowledge levels. The pass mark was 80% and therefore Nancy moves onto other learning but Arthur must re take the training.
Taking the course a second time and re taking the test is not particularly efficient. The learning path itself is exactly the same as the first time he took the training and therefore does not allow him to address the specific weakness areas, and look to close those. Adaptive learning can help in his example.
Adaptive learning ensures subjects are mastered across the board
If an eLearning system is delivering adaptive learning it would present Nancy with learning on the topics she finds challenging at an easy level. Once those are mastered the system would present learning at increasingly difficult levels.
In essence, it will place emphasis on the individual’s weaker areas. In our prior test example, it would continue to provide learning and evaluation to Arthur until he reached or exceeded the 90% threshold.
Personalized and adaptive learning combined
As already explained, personalized and adaptive learning address employee learning at different stages in the learning process. The most powerful and effective learning occurs when personalized and adaptive learning are used in combination:
The result is learning which satisfies the needs of modern learners by allowing them to increase their knowledge efficiently and successfully.
To find out how Axonify can cut your training budgets in half, increase knowledge retention and have a direct impact on your company’s bottom line, lets continue the conversation and make an appointment with a solutions expert.