Steinbeis experts systematize skills development at companies
Enabling learners to benefit from personalized skills development is increasingly regarded as a grand design of the education system – a decisive approach to making lifelong learning attractive to all stakeholders, in order to remain capable of creative action in increasingly complex and dynamic situations, at work and in other areas of life. On the other hand, designing, analyzing, and supporting learning processes that are variable in nature, in order to personalize the acquisition of important competencies, has often until now been complex, anything but transparent, and rarely efficient. For its Learn4U project, the Steinbeis Innovation Center for Innovation Engineering looked at different ways to gear companies and workers to the demands of lifelong learning, also examining factors offering orientation in coping with the diverse selection of continuing education topics.
In the fast-moving world of digitech, young people consider it crucial to be flexible when it comes to knowledge acquisition in order to remain permanently attractive on the labor market. These were the findings of a Future Skills 2020 study conducted by Stifterverband, the German association of sponsors and donors. To meet these growing challenges when acquiring new skills in a business setting, it’s important to design and implement learning processes (so-called corporate learning) in such a way that they specifically strengthen the competencies of employees, not only in terms of hard technical skills, but also in the area of interdisciplinary or soft skills. In business, currently the following challenges are faced:
- There is insufficient awareness of the knowledge and skill requirements that are relevant to companies: What must companies know and be capable of in their field and business environment (products, industries, markets, competitors) in order to not only remain viable, but also to prepare for the future?
- There is insufficient definition of the need to take action in different areas (the company, teams, individuals): How does a company learn as an organization? What does this mean for team training? What do individual employees need to learn and how can they learn efficiently?
- Measures and learning opportunities are often only accepted hesitatingly by employees: What can be done to encourage employees to want to change? Which options are right for employees and which ones can best be implemented on/near/off the job?
- There is no systematic approach when monitoring success with respect to the fulfillment of learning objectives: What are the concrete benefits of training measures – for the company, teams, and ultimately employees? What can be done to determine or quantify the actual success of continuing education?
With many of the existing learning platforms, there’s an implicit expectation that technology such as artificial intelligence will provide the solution to all problems – for example, by lining up the demand and supply of learning content (matchmaking). Professor Dr.-Ing. Günther Würtz, Steinbeis Entrepreneur at the Steinbeis Innovation Center for Innovation Engineering, knows that as a concept, this is too simplistic: “What we’ve discovered with the Learn4U project so far is that the challenge is to formulate the current and future skills requirements of the company clearly and unambiguously, to work out the actions required to select and implement employee training, and – based on this – to make learning success measurable.” In practice, however, systematically planning, running, and monitoring staff training is far too often a black box activity and is not dealt with properly.
Made-to-measure learning thanks to Learn4U
In recent years, individualized products and services have even become a regular feature of our everyday lives at home. Products can now be customized according to people’s individual expectations and companies are capable of producing them at low cost. This has and still does make it necessary to find new methods, and translating those methods to the product of personalized learning lies at the heart of the Learn4U research project, which lays emphasis on practical application. The project is receiving funding and support from the Adolf Leuze Foundation. Learn4U is about using established methods and, in particular, defined processes to comprehensively and systematically understand the individual requirements of companies and their employees, to assess potential, and to make use of that potential in operational terms.
“On the one hand, this is achieved by merging the methods of engineering and complexity management, and on the other, you use the methods of human-centered design and agile development,” explains Günther Würtz. Essential engineering approaches are, for example, classic requirements engineering as a basis of product development, as well as lean and agile processes in conjunction with the recognized evaluation metrics of process management (such as Balanced Scorecard). With human-centered design, use is made of profiles specifically created for users (such as personality types/profiles), user-specific requirements (such as reframing), and modern forms of problem-solving (such as design thinking). By allowing both methods to interact within the personalized learning process, the specific skills requirement of a company and the individual abilities (skills) of employees can be brought into harmony.
Process model of personalized learning
The Learn4U project is based on a process model of personalized learning.
Its essential features include definition of a new process called “requirements development.” This acts as a basis for closing the gap (skill gap) between the demand for skills and the offer of training, particularly with regard to future skills. An important aspect is also systematically mapping existing training in the form of processes in order to make such activities more achievable (management) and controllable (controlling):
- The “offer-screening” process: What is offered by education providers?
- The “learning content delivery and training” process: Which offers are suitable for which employees?
- The “performance review” process: How are the learning outcomes of individual employees measured?
It’s also important to identify “matching points” at the overlap between processes in order to avoid the incorrect or incomplete sharing of information.
For the Learn4U project, the Steinbeis Innovation Center for Innovation Engineering created three assessments for the development of a corporate learning system, spanning processes, tools, and learning tools. This forms the basis for personalized learning.
- ASSESSMENT 1
The process of ascertaining knowledge requirements stemming from business/project goals: Based on an assessment of the maturity of projects or the business model, methods are used to determine changes required to products and processes, and those requirements are mapped with additional skills requirements. From this, it is established which additional knowledge is required and priorities are set.
Example: Offer digital services in the future – different skills are needed for different tasks and roles. - ASSESSMENT 2
The process of ascertaining skill requirements stemming from knowledge requirements: Based on target tasks and roles, specific to the context, a selected or existing competence model is used to identify the skill gaps of teams and employees. From this, the required learning needs and the level of needs are ascertained using self-assessments/third-party assessments and “transition paths” are defined.
Example: Data acquisition for digital services – who needs to know about/be able to do things with big data? - ASSESSMENT 3
The process of ascertaining learning content/formats stemming from skill requirements: Skill requirements and learners are clustered (learning types, learning profiles) and, based on this, appropriate learning paths (learning formats, learning units, etc.) are created. If necessary, these are aligned with existing skill models and personnel development concepts.
Example: Learning module on the fundamentals of big data. Same learning content delivered individually through different learning formats (podcasts, videos, etc.)