Persona Driven Customized Training To Boost Learning Outcomes

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The technical education system in India is facing a huge gap between industry requirements and academic curriculum. The All India Council for Technical Education (AICTE) report states that over 1.5 million people graduate from engineering colleges every year and substantial percentile of them are not employable. The rest need to be trained in knowledge and skill in the respective industry before they can be deployed. Hence, the HR and learning & development departments of organizations of mid-sized and large companies face several challenges in preparing and on-boarding these engineering graduates.

The major challenges are:

  • Selection:  Selecting individuals who can be trained for a given job description.
  • Training: Formulating the methodology of training that aligns to individual learning capacities and styles in order to optimise the training duration.

In the current scenario, both the above processes are designed on the basis of the learning outcomes of knowledge and skills of what the organisations require. It’s a one-size-fits-all kind of approach where participants are put in classes and made to undergo learning in the same way, as it was done in engineering colleges!! So, we cannot expect a different outcome.

Even at the end of the training, only some of them are trained to satisfy the skill-requirement. And the rest do not know why the skills they have acquired fail miserably when they are deployed on projects is not addressed adequately.

What are we addressing?

This article will talk about a participant’s persona-based learning intervention. The learning design will focus on both the skillset and mindset of the participant. The skillset can be judged based on their performance in education and pre-assessment test. 

Psychological and Socio-Economic data on individuals:

In the persona-based training approach, we need several data points to understand participants’ mind set, which in turn helps us make a decision regarding the most accelerated learning style. Data about the participants’ logical skills can be tracked by an IQ test. The mindset can be assessed based on the data collected from an EQ test score. The understanding capacity, articulation skills and other related abilities   depends on the medium of education, socio-economic status, type of city/town they are brought up in etc. We can use the data obtained from these inputs and design several different learning approaches to suit individual learning styles, resulting in an efficient training plan where most participants meet the desired learning outcomes.

New age learning intervention: 

Researchers in education have discovered that there are several learning styles, among which only one or two will be prominent in an individual Memletics learning model is of great assistance in building the elements of the participant. The different learning styles listed in memletics are based on different sensory organs, mind power and logic building capacity of a person. There are tools available to collect data regarding learning styles. These tools can be administered, and this is a scalable model.

Memletics Learning Styles

Once all the relevant above data mapping the participant’s skillset, mindset, learning style, logic building and language skills are obtained, modern technology like data analysis, Machine learning and algorithms can be adopted to create different personas.

Machine Learning (ML) and Artificial Intelligence (AI) based algorithms can be applied to create the best-suited teaching methodology and relevant pedagogy. 

It is often apparent that some individuals have difficulty in visualising certain concepts. Augmented Reality (AR) and Virtual Reality (VR) based devices can be included as part of this modern learning design. This aids in visualisation and promotes understanding of concepts and enhances the participant’s knowledge base on the topic.

We have also observed that sometimes, participants are not able to make appropriate connections between knowledge and the tools used.  In this regard, one suggested experiment is to adopt a bottom-up learning approach.

We start the learning intervention with a real-world problem and challenge the participants to solve it. As different solutions emerge, in addition to related concepts, technology, tools and implementation methodologies, the facilitator helps to decide between the different options by focusing on the pros and cons. This forces the participants to think through the problem. They also now know the importance of the concepts and tools that they must learn as they are aware of the larger picture and they are able to understand how and why the different elements are used.

A modern learning style along with modern technologies is bound to enhance the learning experience. To begin with, we should start with creating four to six different personas that would be applicable to a majority of the participants.

In the next stage we should collect data regarding the actual outcome of this method of learning intervention and fine tune the personas by adding more elements.

At some point we should be able to build a model where every individual will be able to learn through a customised learning process. With the advent of technology and an increased need for learning interventions, especially in the adult learning, that day is not far!

Thus, we hope that persona-driven customized training will boost the learning outcome of the individuals and help the organizations to get the best of their human resources.