Predicting Learning Behavior

Predictive analysis can help managers, who are responsible for hiring employees, to map the learning needs of the next generation of top talent.

You could say that I'm an analytical person. I love to analyze, specially on the predictive variant. At the same time, I'm biased. Because my current position relies heavily on technology that provides our clients with tools for predictive analysis. They can thus recruit employees who are best suited to a specific job or culture.

Better recruiting

When the phenomenon was still in its infancy, software for HR analysis had one goal: better recruitment. This would automatically lead to lower staff turnover and a higher level of performance. And indeed users experienced drastic results in those areas. More and more people were hired after using an analytical formula, which included a behavioral assessment in which performance indicators and the character of the function were linked. Then the application possibilities of analysis began to develop rapidly. There we were, on a gold mine of behavioral characteristics of millions of candidates, that helps us to predict and recommend the best employees. How could we further exploit these insights?

We already knew that predictive analysis could definitely be used for the introduction and the incorporation of new employees. From a large number of questions about behavior we distil the learning style that a candidate prefers, or how new material is most easily processed. We also knew how candidates manage tasks: are they technologically advanced and therefore heavily dependent on iPads, or are they 'old school' and do they use notepads? And do they approach a task on the basis of successive milestones, or do they first pick the easiest parts? And we could also predict how employees deal with time division, their preferences for working in teams and their expectations regarding supervision and the associated interactions.


This brings me to one of the most exciting aspects of predictive analysis: the current technology can provide insight into development aspects where the candidate doesn't achieve the optimal ambition level. For example, many employers attach great importance to measuring the independence of a candidate. The outcome of that measurement helps to determine the need for either a 'hands on' manager (in the case of a low independence score) or a manager who provides little regular supervision. The manager, who is responsible for hiring an employee, already has this information before the first job interview, before a candidate is offered a job, and before he or she starts work on the first working day.

If a candidate scores low on independence, while the employer searches for the category 'high', then predictive analysis can help in two ways to assess the relevant applicant. The first is an automatically-generated questionnaire based on assessment results. In an advanced HR technology environment, every non-optimal behavioral aspect (in the pre-recruitment phase) is converted into questions for the job interview. The responsible manager can thus deepen these aspects and decipher how the candidate can improve himself in the relevant areas.

After the recruitment of an applicant, these gaps, which indicate learning opportunities, can be tackled during special training sessions. Here, for example, employees learn how they can function successfully within different management styles. These and other learning opportunities can be proactively planned and dealt with at an early stage of a candidate's career. This provides support during the current work and during the continuation of the career. Organizations apply a proactive training strategy when their HR technology is able to select from the available training software courses that respond precisely to the gaps discovered during the initial assessment results of a candidate.

Predictive analysis

Under the rather widespread term 'training' predictive analysis helps managers to achieve results. In addition to identifying improvement areas, it helps to answer the question: 'How can an employee process information best?' If, for example, the assessment shows that a candidate scores low on independence, he or she probably works best in a class environment, with opportunities for interaction, instead of an isolated situation with an online course and the need for self-discipline. In contrast, young people (millennials), who grew up almost online, may score very high on independence. They probably have a preference for electronics as a tool for company training, instead of working in a group.

Predictive analysis can help managers, who are responsible for hiring employees, to map learning needs of the next generation of top talent. The supervisors concerned must be proactive in assigning relevant courses, offering them in a form that best fits the way an employee wants to learn, and monitor the results. It's likely that this style of optimized learning will become the basis for a new standard in how employees function and thus contribute to the financial results of the organization.

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