A lot has changed in Learning! From learner preferences to the type of content available and the skills we train ourselves on. That’s because change is constant and inevitable and we are all either drivers of that change or continuously learning to keep up with that change.
SAP talks about their whole self-model for Incorporating Expressive States into the Human Experience at Work.
This model talks about how users, teams, and organisations as a whole benefit if each individual brings their “whole self” to work. This means not just my “work self” i.e., my work styles, job-related skills, and preferences but also my personal self - bringing in my preferences, my interests, my aspirations, motivations, etc. Bringing all of those expressive states allows us to be at our full potential in terms of employee skills and give our utmost to learn the skills employers look for.
A key thing is that these aren’t static either, most of these expressive states change on an ongoing basis. We want to look at how through technology, we can guide and support individuals to bring their whole selves and be their best selves!
Learning should be driven to meet the learner’s personal preferences. Examples of these are:
Based on the employee’s interests. They may not always be based on a work skill e.g., running, sports, politics amongst many other topics that align directly with work skills and topics. These interest lists should not be curated based on roles as it’s a way to offer the learner some learning opportunities beyond their job skills.
Ability to offer learning based on the learner’s skills profile, combined with the employee skills typically associated with that job role. If a learner has identified a target role, we should also be able to support them with learning the skills needed for that target job.
My recent activity:
As we continue to talk about changes, these happen on a daily basis, in our personal lives, work environment, or even societal changes e.g., the pandemic, changing political situations, social movements, etc. While many of these changes may impact or interest us, we don’t constantly go and update our interests or skills in a system to get the right learning recommended. That’s where it’s important to track the user’s recent activity, identify trends and topics and plan the next set of recommendations for the individual.
While we plan based on our individual preferences and development, we are often influenced by and influence the teams we are part of. Learning through the team and contributing to the team and the community you are part of is also an important part of developing ourselves.
Humans are social beings; we live and learn in a society and are most influenced by those we are directly connected with or those we follow. Learning Recommendations based on what my direct connections i.e. my manager, my employees, others in my department, and those that I follow in the social channels can influence what I learn but also ensure I don’t miss out on key work topics that others may be learning on.
People Like you:
While the immediate team-based recommendations are great, to be able to pick up the right skills and knowledge, it’s also vital to pick up learnings from people like us regardless of where in the organisation they belong. People like you can be defined by location, division, job code or many other attributes of the user as these can vary depending on organisation type, HR data available, etc.
Peer to Peer:
While we let the machines do their job, a human element, a personal recommendation always holds a greater value for many and direct recommendations from my peers can be very valuable.
As we start to bring our whole self into work and learn based on our own preferences, our social influence - the organisation starts to understand us better too, and is able to better support our aspirations. The insights also give an organisation a better picture of the skills within the organisation and the gaps.
Skills in Demand:
As organisations review the changing business climate and objectives, often new skills are identified that may be important to the organisation’s success. With better insights into learning preferences, we get a better insight into the skills within the teams. This also helps identify the skill gaps within the organisation and can allow them to promote or curate content on those skills promoting a better uptake of that content and building an interest and knowledge in that skill.
Jobs Related skills:
To be able to function to expectations and deliver, organisations need to ensure that employees keep up to date with their job-related skills. These can come in many forms: automated assignments if required, training and collaboration groups to learn from my peers, and offering additional resources and support through those job-related skills can help individuals keep up to date with the skills needed for the role.
As organisations understand the teams better, their learning preferences better and the content individuals are looking for, they get a better insight into the demands of the team and can easily tailor their SAP Successfactors offerings to suit the employee needs better.
Many organisations struggle to gain a holistic view of the skills that exist within their workforce or the ambitions of their people. The latest SuccessFactors innovations bring together data, machine learning and AI to provide organisations with a better understanding of the capabilities within their workforce and actionable talent intelligence to align their people with the needs of the organisation.
Together with SAP SuccessFactors Opportunity Marketplace, these innovations deliver the intelligence and adaptability needed to help build workforces that are more skilled, agile and equitable.
TalenTeam’s BLEND LXP combines together the best of SAP SuccessFactors and any External Learning library into a single destination for the ultimate learning experience. It enables continuous growth for individual learners, teams, and your organisation.