In order to evaluate how skilled labour and AI can successfully “cooperate” in manufacturing, a model is presented here that can support the evaluation process.

Keywords: Vocational Education, Industry 4.0, Autonomy, Automation, Human

1        Preliminary remarks

The article presents a model that provides guidance for describing, deciding, and evaluating AI-supported skilled work. With the help of the model, indications are given as to which contents are relevant for the shaping of skilled labor occupations. The obvious question of new occupations at the skilled worker level is not pursued in the article. To be able to do this, further research is needed. The empirical work available to date tends to see a need for modernization and redesign of existing occupations.

The model is based on the authors’ research in the manufacturing sector. Occupational science methods were used to investigate the requirements for skilled labour and potential applications of AI. The objective of the occupational science research was to survey the requirements for skilled work on the shopfloor. The investigations focused on organizational, technological and social developments related to work and the resulting consequences for skilled work (Spöttl & Windelband 2021).

The findings from the surveys were expanded to include defined autonomy levels for AI (BMWi 2019, 14). Particular attention was paid to

• levels of sophistication in applications of AI and
• possibilities of interaction (technological, organizational, social) with AI at the skilled labour level.

The model is understood as an application model for the interaction of skilled work and AI. Its performance lies in the fact that it can be used to characterize, assess and evaluate the possibilities of AI supported skilled work in manufacturing.

2        Industry 4.0 and the autonomy of technologies

A central question for vocational education and training is: Can technology replace human performance and human intelligence and how does this change qualification structures and learning requirements? There is no doubt that technological development has advanced to the point where large parts of individual areas of skilled work can be influenced or even replaced by automation: Welding robots produce basic body shells almost without human involvement, transport systems in production operate without drivers, and products are linked to local databases or even global (internet) information networks and thus control production processes independently. Thus, while the relief of humans or even the devaluation or replacement of human action by machine action has long permeated our private, social, and professional lives (Brynjolfsson & McAfee 2014), it is becoming increasingly clear that the thus changed and newly emerging worlds of life and work are themselves becoming the subject of skilled work. The automation of manual tasks (especially of repetitive tasks) by robots and increasingly also of more cognitive tasks is leading to sometimes far-reaching changes in employment structures, the required skill profiles, and possibly even occupational structures. So far empirical studies of sociological research are available (Rammert 2016) up to socionics, an interdisciplinary field of research between sociology and AI. The question is explored whether and how it is possible to develop computer programs capable of communication and cooperation based on the model of human society) and labor market research (Dengler & Matthes 2018). However, concrete changes in tasks and competence requirements are still largely unknown. So how are automation, digitization or even artificial intelligence related and how do they change the requirements of skilled vocational work and the vocational training geared to it?

2.1        Industry 4.0 and connectivity

The core of “Industry 4.0” (I40) is the linking of physical components – for example, a machine tool – with components computerized or digitized via the Internet to realize the vision of self-regulating production. Systems linked with software in this way are referred to as cyber-physical systems (CPS) or, with direct reference to production, as cyber-physical production systems (CPPS). CPPS systems dissolve the hierarchical structures of the automation pyramid (VDI & VDE 2013) and integrate people and machines in a production method oriented toward flexibilized production structures with the help of standardization approaches (Acatech 2013).

The increasing networking of “things” is currently creating an “Internet of Things” (IoT). The IoT is used as a term for the infrastructure in which different objects communicate with each other and data are processed directly via data processing (Spöttl & Windelband 2021). Interaction occurs in the sense of data acquisition, data processing and data transport between technical systems. A decisive characteristic of the interaction is the “intelligence” it contains, which is dimensioned by how autonomously the interaction takes place and how appropriate it is for our human coexistence and the real requirements for technical work. Since the autonomous triggering of an interaction presupposes decisions, it is precisely these that are to be scrutinized. While old familiar programs basically incorporate figures and facts with their algorithms, AI goes beyond this and evaluates them.

2.2        Intelligence of technical systems as an object of work

The autonomy of systems with respect to effects that cross system boundaries is a decisive feature for characterizing the intelligence of technical systems and thus of AI. In general, it is very difficult to precisely define the concept of intelligence (Russell & Norvig 2021). Turing’s (1950) characterization still provides the most solid approach to identify “smart systems” (i.e. “intelligent systems” that are able to continuously analyze processes and situations and make predictions and decisions on their own based on the data situation and react accordingly to changing situations). The so-called Turing test consists of checking whether a technical system behaves in such a way that intelligence would be attributed to a human being if it behaved in the same way. Seven characteristics (Becker 2004, 169 f.; Bechtel & Graham 2017; Bechtel, Abrahamsen, & Graham 1998, 10 ff.) are specific for this test:

1. Ability to take in, process, and translate information into behavior appropriate for the situation (informing).
2. Ability to store functions in a “memory”; retrieval (networking).
3. Ability to learn from changing systems and environmental conditions (learning).
4. Ability to make appropriate decisions based on changing systems and environmental conditions (decision making).
5. Ability to independently plan actions based on experience and characteristics of systems and environmental conditions (planning).
6. Increasing communication capability between systems and the human-system complex (communicating) and
7. Ability to focus on the environment when acting in systems (interacting).

Associated with these seven characteristics there are active interventions in living and working environments. These are generally associated with intelligence to characterize the effects of “intelligent” action. This also includes intelligent interaction with the environment and social & emotional intelligence (Mahdawi 2017).

When applied to technical systems, these seven characteristics are rarely met without restrictions. Therefore, they should be seen as a continuum of individual and yet at the same time interrelated elements that are evaluated in a comparison between technical and human behavior. Moreover, they provide starting points for the study of skilled work in technology fields permeated by AI.

In technology, the following fields can be distinguished in which AI approaches are applied (Becker et al. 2021):

• Skill-based systems: robotics, transportation systems, Computer Integrated Manufacturing warehouse systems in the field of logistics.
• Knowledge-based systems: expert systems, assistance systems, and agents.
• Learning-oriented systems: Fuzzy logic (a theory developed primarily for representing human knowledge and reasoning for processing in computers), neural networks, machine learning, and model-based methods.
• Simulation-oriented systems: Digital Twins (Ostaševičius 2022).

In further development of the above features, the technology fields mentioned are significant and are taken into account. The seven characteristics can be applied to the individual fields of technology. Whether the people involved in them are ultimately considered to behave intelligently would be a question that still needs to be clarified empirically. However, that is not the intention of this article.

1        A model for acting within the framework of specialized work with artificial intelligence

The further development of automation approaches towards technical realizations of artificial intelligence (AI) is increasingly approaching an implementation in work environments. One example of this is the development of numerous assistance systems. With the help of a model that characterizes “intelligent” interaction in skilled work as acting on and with AI, the authors aim to provide a basis for investigating, describing, and evaluating this acting. The model is also intended to help reintegrate skilled workers (Schaupp 2022, 11 ff.). This should make the intelligent interaction in the specialized work more transparent so that it can be supported in a more targeted manner. It should be noted that artificial intelligence covers the entire continuum from simple information processing to thinking processes (Minsky 1988) on the one hand, and on the other hand also all transfer problems between cognitively shaped and skill-based actions, and highly different professional tasks which are associated with it (Dreyfus et al. 1986).

The so-called learning systems anchored in AI independently find solutions for their defined tasks, among other things by observing their environment and the deriving of rules. A distinction is made between strong and weak AI. “Strong AI” is understood here as a programmed computer that thinks and acts like a human and can ultimately even have consciousness[1]. “Weak AI” is geared toward solving specific tasks in a previously defined area – and only in that area (VDI Technology Center 2018). Industrial-technical professions such as industrial mechanics, machining mechanics, mechatronics engineers, and even production technologists are already confronted with the effects of such intelligence in their everyday work (Spöttl et al. 2016; Becker & Spöttl 2019). The handling of the systems is related to the concrete work tasks.

3.1        Design of a model

The already existing schemes for assessing the permeation or digitalization readiness (i.e., the degree of digital maturity based on various dimensions) of companies and skilled labor tend to be technology driven. Such a restriction to the technology dimension is not expedient. Social, personal, and ethical components as well as values and questions of sustainability must be integrated in the same way and, finally, the actions themselves must be subjected to an evaluation. Even an existing classification of autonomy (BMWi 2019, 13) can only be a first step towards assessing professional action. The classification of autonomy according to publications of the BMWi (2019) already focuses on people (see Table 1). This is an important step for the design of a model.

Table1: Overarching definition of autonomy levels in industrial production not/little influenced by AI (BMWi 2019, 14).

In the end, the greatest challenge lies in mastering the complexity of these systems. Today, specialists try to make their decisions based on experience in maintenance, among other things, and use intuition, feeling, sentiment and the evaluation of various process data for this purpose (Dreyfus & Dreyfus 1986; Böhle 2017). In the case of AI-based systems, the question arises as to which intervention options remain for skilled workers and which (new) tasks arise when using such systems (Schaupp 2022). This is known by the term “automation dilemma” (Bainbridge 1983): In increasingly automated systems, the building up and use of expert knowledge on the part of humans is made more difficult or is even prevented. At the same time, however, there is a need for skilled workers to maintain the ability to act and make decisions to identify errors and to develop possible solutions in crucial situations. At the same time, depending on the training occupation, skilled workers must learn how to analyze relevant data and to evaluate and process them in relation to the situation. In this respect, a model is required that can be used to describe the actions of specialists in and on AI-influenced systems. It should be noted that there is no such thing as “AI”: In reality it always encompasses a continuum of the five analytically described levels (see Table 1).

An extension of the labeling of an Industry 4.0 penetration in the two technologically dominated perspectives of “product” and “production” (as focused by the VDMA[1] classification) is achieved by labeling work requirements. The latter are brought into an action context in production or in dealing with the product. In this way, action spaces and changed work processes become clear from the point of view of the skilled workers. This results in a classification of the permeation of action spaces of skilled workers by CPS and AI. The five levels of autonomy (see Table 1) are linked to the interaction of skilled workers with the respective degree of AI expression and the concrete product and process technologies of Industry 4.0. The decisive factor here is the interaction of skilled workers with the respective degree of expression, which in turn is characterized by the emerging human-machine collaboration. After all, it is the mechanism of artificial intelligence that once automate skilled worker actions or change processes so that skilled workers have to deal precisely with these changed processes.  Automation with the help of artificial intelligence (Hirsch-Kreinsen & Karačić 2019) thus becomes the object of action of skilled work, either the realization of automation itself or the precise work process changed by automation (Schaupp 2022, 7 ff.).

3.2        Levels of a model

Level 1 is characterized by the fact that information about a product or a process can be attached to an object and specialists are confronted with exactly that. In automated assembly systems, for example, they are challenged to view order data, production data and delivery data along with the product and, if necessary, to relate them to planned production processes. Specialists are confronted here with a new variant of information transport and must be able to deal with it, both in the sense of processing the information and by using an intelligent procedure in the transport of data and information.

If the object or asset itself has a form of artificial intelligence, i.e., if it is equipped with a microprocessor and a program with corresponding logic, this results in Level 2 ofthe manifestation of CPS. Such a constellation is called an embedded system (an embedded system is a digital system – also called a computer system – that is embedded in and interacts with a surrounding technical system). Stage 1 capabilities are embedded in the object itself and no longer tacked on. Industrial products based on embedded systems can be supplied with different data and thus acquire different properties. In addition, depending on the interfaces available on the embedded systems, there are possibilities for the automated transfer of information to other parts of the plant. Specialists then carry out tasks of coding (adaptation in relation to a certain configuration), parameterization (provision of the object with a certain property) or monitoring of self-regulation (automated passing on of information to another plant part).

Level 3 of the CPS characteristic consists of the interaction between tools, computers, and systems. In other words, “things” with Level 2 properties independently pass on information in both directions depending on system properties or triggered by predefined time intervals or “jobs” and automatically change the way the production system functions. Networking between the mold and the system enables, for example, system monitoring, fault diagnosis or remote maintenance. One example is the CAD-CAM (Computer Added Design – Computer Added Manufacturing) coupling in workshop production with connection to quality assurance systems, for example to record and document wear data of tools.

In Level 4, production data are used to influence production processes. A smart meter (a smart meter is an intelligent measuring system) with a digital and internet enabled measuring system and a communication unit (gateway). This allows to communicate current consumption data to be queried by the user, and feeds electrical energy into the grid depending on the electricity price. In case of reports of a reduction of the wear and tear stock of a machine, the respective tool is ordered automatically. Such interactions must be monitored, installed and configured by skilled personnel and, if necessary, adapted to changed production environments.

Finally, Level 5 corresponds to the actual vision of Industry 4.0. With the inclusion of human beings, things (tools, plants, systems) are assigned “intelligent” properties so that the overall system or process regulates itself as far as this is technically feasible. The extent to which this stage will generate new tasks for skilled workers and which role skilled workers are still likely to play is currently least foreseeable and represents a company-specific and even a societal design task.

Table 2: Model for describing levels of skilled work on and in AI-influenced (production) systems (based on Becker 2016, 74; Dreyfus & Dreyfus 1986).

The need to significantly expand the technology-oriented view of products and processes by taking into account the autonomy of the resulting human-machine systems as such and in particular as a work object of skilled professional work becomes apparent. This step is a basic condition for human-centered production and assembly. This is what makes socio-technical work design possible in the first place.

4        Example of professional technical work on and with artificial intelligence

Numerous examples of working with and on AI-supported systems could now be cited (Becker, Spöttl, & Windelband 2021, 45 ff.), one of which should serve as an example to demonstrate the “new” character of specialized work quite clearly: Working with digital twins.

Digital twins (Ostaševičius 2022) are already in use in production and are a further development of simulation programs. They can reflect holistic images of reality including all physical properties: in the area of predictive maintenance, in energy data management, in resource optimization or production planning for trouble-free production. Specialists are working at the digital copy of a plant just as they would on the physical plant to put it into operation, determine maintenance requirements, and reduce energy and material consumption. One application example is the widely used automation environment developed by Siemens with NX (NX is a fully three-dimensional system with double precision that enables the exact description of almost any geometric shape) and the TIA Portal (Totally Integrated Automation Portal: it enables complete access to the entire digitized automation). This which brings together various simulation and control programs for production (Siemens 2018). In the future, the handling of “Internet operating environments” such as MindSphere (MindSphere is an industrial IoT service solution that uses sophisticated analytics and AI to implement IoT solutions from Edge to Cloud) will also be part of these tasks. However, in the case of digital twins, the abstraction and the theory-oriented approach sometimes reach a level which, at least at present, still primarily requires academically trained specialists.

The switch between work in virtuality and reality is becoming increasingly commonplace in skilled work due to digital twins. It is remarkable that it is the human being who must design and monitor this switch and interactivity. Undoubtedly, this switch can also be automated and thus devalue human work. It remains a design task to shape it in a way that an overall social acceptance prevails (Noble 2011). Examples observed by the authors in their technical work indicate that, similarly to the automation dilemma, it is difficult, if not impossible, to ignore the human capacity for action. A very simple example: A fully automated production line for the manufacturing of ball bearings is not able to consider changed conditions, for example, outer rings which have started to rust during their intermediate storage. Despite full automation, it is the human being who determines measurements for a “fully automated” production based on experience.

1        Conclusion

The model can be helpful for the description, assessment and evaluation of AI-supported skilled work. The advantage of such a model is that skilled work can be classified and evaluated even when used in highly complex systems because professional competencies can be described via the different levels of expression of artificial intelligence. In this feature, it can be seen as a reference for planning qualification initiatives. Above all, false conclusions for the design of industrial-technical training regulations and framework curricula are to be avoided by examining the vocational action sequences with the help of the model more closely in terms of content. In doing so, it is not only important to consider technological changes, but also the shifts in communication, cooperation and collaboration structures due to the organization of process flows. Influenced by AI, changed competence requirements arise for professionally qualified persons who process tasks in partially completely changed work organization structures.

For vocational training, some open questions can be derived that will be relevant in the future:

• Does the influence of AI create independent new, or rather integrative and changed competence requirements?
• The increasing autonomy of technology, also with an effect on action structures, leads to the replacement of occupational competences, certain occupational qualification and possibly even entire professions. As with the question of the automation dilemma (Bainbridge 1983), the question of the autonomy dilemma[1] is now arising under the influence of AI: If competences, experiences and decision-making bases for certain contexts of action are “absorbed” into machines, how can these then be prepared for learning processes and transferred to subsequent generations of skilled workers’?
• Will the influence of AI-supported technologies lead to the emergence of new professions or new types of professions, such as hybrid professions? Following the introduction of production technologists, which arose rather from the idea of an optimized production organization, considerations are now in the center of interest aiming at replacing industrial mechanics with asset managers (in the Industry 4.0 context, an asset is any virtually mapped object that has a value for an organization. Simply put: any object in the physical world that has a connection to the internet). The basis for such a job profile would be an empirically identifiable range of tasks that would stand up to the criteria for a profession (VDI & VDE 2013).
• What needs to be investigated are the concrete changes in skilled work due to the changed role of technology and the resulting qualification requirements. Only in this way useful descriptions can be created for the design of occupational profiles and training regulations.
• What do the new and changed professional tasks and competence requirements due to AI- influence look like in the four fields (see Figure 1), which are raised by the two dimensions “information processing” and “autonomy”? And can the hypotheses assigned to the fields be confirmed? (Rohrbach-Schmidt & Tiemann 2013)

The model for the characterization of skilled work in connection with AI, which was developed on the basis of various approaches to the design of industrial and manual work, requires supplementary empirical validation for further verification. The following hypotheses (see Fig. 1) can form the basis for it.

Hypothesis 1: A low degree of information processing and a low autonomy of technology lead to a relief of occupational tasks by machines.

Hypothesis 2: A high degree of information processing and a low degree of autonomy of technology lead to an increase in occupational tasks.

Hypothesis 3: A low degree of information processing and a high degree of autonomy of technology lead to a substitution of occupational tasks by machines.

Hypothesis 4: A high degree of information processing and a high degree of autonomy of technology lead to a high demand for occupational tasks unless they are not algorithmic in nature and to a low demand for occupational tasks if they are algorithmic in nature.

The four hypotheses provide a basis for discussion. The aim is to clarify

• how far the autonomy of technology is likely to develop and
• what level of information processing is likely to be achieved.

Depending on the results, considerations can be made about the design of the qualification of skilled workers.

A further step can be the optimization of the model in order to have a tool available for dealing with AI that can be used to assess, describe and evaluate AI-supported skilled work.

[1] An expert system provides people with solutions for complex problems in a limited specialist area. It acts as an expert, so to speak, and provides support in the form of recommendations for action.

[2] A digital twin refers to a computer-based model of a tangible or intangible object that can replicate as many of the properties of the physical object as possible (Ostaševičius 2022).

[3] In the field of artificial intelligence, the concept of consciousness expresses the fact that the perceived environment and the input data can be combined in the computer programs to form a representation of reality that conveys “meaning”. This is calculated by the AI and must be measured against what humans would define as meaning in such a context (Schaupp 2022).

[4] VDMA (Verband Deutscher Maschinen- und Anlagenbau e. V.): With over 3400 members, the VDMA is the largest network organization and an important voice for the mechanical engineering industry in Germany and Europe.

[5] Following the debate about an increasingly supervisory role of skilled workers due to an increase in automation, Ohno (1988) coined the term “autonomation” for the opposite school of thought about the increasing autonomy of machines.

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It is widely acknowledged that the world of work is changing. Technical and vocational education and training (TVET), however, largely continues to follow a traditional model developed in the 19^th century which used to prepare youth for industrial work. As some economies in the Asia-Pacific are becoming knowledge-based, there is growing recognition of the mismatch between skills taught in TVET and skills needed in the labour markets. As a result, transferable skills are increasingly seen as a missing link between education and training and the world of work.

But what are transferable skills? There are different understandings and conceptualizations of these skills across countries but in general transferable skills refer to a number of important competencies (communication, problem-solving, collaboration skills, etc.) that can be learned and can help people to make transitions between education levels, education and the world of work, as well as within and between sectors. They are non-occupation specific skills that can give workers the comparative advantage in an increasingly interconnected and competitive world of work. 

To prepare students for the jobs of today and tomorrow, TVET, which has traditionally provided specific occupational skills for existing jobs, now needs to give more attention to improving students’ holistic development and pay more attention to developing their transferable skills for future jobs. This, however, can only be achieved if TVET policies clearly define transferable skills for the country context and set out clear guidelines for implementation. Despite a general agreement on the importance of transferable skills among most Asian countries, there is still a pressing need for clearly defining transferable skills and creating a shared understanding of these skills among all TVET stakeholders. Besides establishing clear policy guidelines, the challenge remains in aligning policies with curricula, pedagogies and assessment for transferable skills in TVET.

At implementation level, TVET teachers are the key in ensuring that students acquire the appropriate levels of transferable skills for their future occupations. Many TVET teachers in Asia, however, seem to lack understanding, skills, pedagogies and resources to effectively impart transferable skills in their students. In addition, in some Asian countries TVET can be found in different streams and at different education levels which results in some disagreement between the general education teachers and the TVET teachers on the relative responsibility of each group for teaching transferable skills. Clear policy guidelines are, therefore, instrumental in addressing this and other issues related to transferable skills. 

Despite existing challenges, there are some examples of the ways in which some Asian countries conceptualized transferable skills in their TVET and VTE policies, and there are examples of some promising initiatives of teaching and learning of these skills. The 3^rd issue of TVET@Asia gives an insight into these developments and explores other skill-related topics. The issue is based on selected country reports prepared for a regional study entitled “Transferable Skills in TVET: Policy Implications”, which is the result of a collaboration between UNESCO Bangkok and the Regional Cooperation Platform (RCP), as well as additional submissions. It is hoped that the issue will contribute to broadening the knowledge base on skills, and particularly transferable skills, in Asia and provide the basis for further research in this area.

The Editors of Issue 3

Barbara Trzmiel, Cheol Hee Kim, Roslin Brennan Kemmis, Matthias Becker

Citation

Trzmiel, B., Hee Kim, C., Brennan Kemmis, R., & Becker, M. (2014). Editorial Issue 3: Transferable skills in technical and vocational education and training (TVET) and vocational teacher education (VTE): Policies and implementation. In: TVET@Asia, issue 3, 1-2. Online: http://www.tvet-online.asia/issue3/editorial_tvet3.pdf (retrieved 30.06.2014).

Standards with a focus on learning and supporting measurable learning processes including their outcome have been under discussion since several decades. In the last two decades more and more standards have been discussed for teacher training for technical education and vocational education and training. Teachers in this area are facing a bunch of challenges worldwide. The reasons for this vary from country to country because of the very different approaches and organization models of teacher training for technical education and vocational training. This situation has prompted some planners to creating standards for whole regions such as Asia, Europe, the United States and so on. This approach is not followed in this article. The concept of standards demonstrated in this article concentrates on teacher training in technical education with a clear link to scientific disciplines at university level in individual countries. Additionally the concept of standards follows a dynamic approach which allows teachers to specify standards for the requirements of quality indicators. Via a bottom-up approach, this concept can be developed for a whole study programme and the quality indicators could be compared between countries and regions.

Keywords: Quality, Quality Assurance, Competence; Learning; Shaping of Standards, Dynamic Standards, Roles of Teachers

1 Introduction

In Technical Vocational Education and Training (TVET) standards (in a narrow sense) are usually applied for four purposes:

• One purpose is to define the quality of education, i.e. what should be achieved by the learner in the educational process.
• The second one is to guide the development of curricula including the selection of content and methodologies.
• The third purpose is to define the quality of teacher training programmes.
• The fourth purpose is their application for assessment, i.e. to check whether the educational programme provides the intended results or whether the learners performed sufficiently. The fourth purpose implies that standards have to be formulated so as to be measurable.

In the article standards for TVET teacher training, the requirements, and the framework for it will be discussed. A model will be shown how to shape them in a way that learning results can be determined.

2 The role of standards

Standards have been intensively discussed since several years. The respective literature concentrates above all on educational standards (Department of Education UK 2013; Spöttl & Becker 2013; Göldi 2011; Becker, Spöttl & Blings 2007, 89 ff.) that first and foremost describe competences in a subject or learning field. Klieme et al. (2003) have drafted an elaborate systematic of national educational standards encompassing

• educational goals,
• competence concepts,
• competence models and
• verification of competences.

The authors understand educational standards as defined requirements for teaching and learning at school. They formulate goals for pedagogical work, expressed by desired learning outcomes for pupils. Thus standards concretize the educational mandate that schools have to accomplish.

Educational standards determine which competences the students must have developed if important goals of schools should be considered achieved. These requirements are systematically assessed in competence step models illustrating the aspects, the grading, and the development trajectories of competences.

The most important reason for the difference between vocational and general education is the fact that vocational initial and further training concentrates on acting in complex learning and work situations and work processes and that the ability to act in a self-organized way is being highlighted. This is why a broader comprehension of standards is necessary. 

Along with the idea that standards should provide a frame of reference for high quality, more parameters are emerging with regard to the vocational educational system which eventually will have a considerable impact on quality development and quality assurance, such as:

• vocational occupational profiles, training and task profiles;
• profiles of teachers and trainers;
• the duration of vocational education and training;
• requirements for exams;
• entry prerequisites;
• curricula (contents, structure, level);
• methods and learning targets and
• the qualification levels of the trainees.

Standards – and this must be emphasized – neither replace curricula nor didactical and methodological approaches and learning concepts. They must be understood as an orientation for more objectives that are described and specified in detail in curricula and other ordinances. Standards should provide the central orientation framework for the respective quality demands for target groups and learning environments (Windelband, Spöttl & Becker 2014, 302).

An analysis of international experiences in this regard shows that standards are basically derived from competency models. Such a model has been sketched below and reveals ideas of a learning culture forming the core of the standards for teacher training in TVET. The international literature agrees on the fact that standards should determine in which learning areas and fields of a subject competencies should be developed in the long term. An evaluation of international literature results in the following “tasks” of standards (Spöttl 2009, 20 ff.):

• standards should determine which competencies should be acquired at a certain time,
• standards should be oriented to a core area of a domain (specialist and/or learning area),
• standards should be structured by so-called competency models,
• standards should describe competencies which can basically be recorded by testing procedures.

Based on these reflexions, quality standards have to be formulated on two levels:

1. For the training of teachers with a view on their field of activity.
2. For the domain, the subject and/or the learning area.

The standards are designed in a way that they describe competencies. At the same time it is ensured that there will be a competency development rather than an output orientation for “teaching to the test”.

Thus it is basically important to safeguard the quality. This is why the process orientation of standards moves into the centre of the reflexions. From an overall perspective, the standards should help to meet the following three criteria (Spöttl 2009, 21f):

1. Standards should be used for quality development in the TVET teacher education courses at the participating institutions. This includes the development of the institutions themselves, the development of teaching personnel (lecturers, professors) as well as the curricula used and the learning opportunities for the students. The term “quality” has to be defined by means of the standards.
2. Standards are needed as a basis for the creation of transnational degree programmes. Each of the participating institutions need to rely on the other institutions providing with their study courses a certain standard of quality, contents, and learning environment to the students.
3. Standards can also be used as a basis for mutual recognition of study achievements between the participating institutions.

The following advantages of standards for TVET teacher training have been identified:

• Standards may provide clear descriptions of TEVT teachers’ core activities and their actual contents in order to identify the teachers’ strength and weaknesses.
• Standards may provide guidelines for the professional development of the teaching profession by implementing reasonable policies.
• Standards may help to optimise teacher training by adjusting and modernizing pre- and in-service training contents and forms.
• Standards may provide a scientific, justified and effective basis for accreditation of achievement and assessment of teaching performance.
• Standards may provide a platform for international communication.

In addition to these advantages it is being expected that standards exert a positive influence on the quality of teaching and teachers’ education:

• Standards may provide a larger scope of the teachers’ choices, flexibility and responsi­bilities.
• Standards may enhance the process of teachers’ professionalisation.
• Standards may enhance the implementation of modular, workplace- and performance-oriented curricula and education of teachers.
• Standards may close the gap between pre- and in-service teacher training.
• Standards may help to establish a flexible and coherent TVET teacher training system by combining and accrediting different individual access, pathways and levels of qualification.

3 Background to standards: Profile for teaching staff

Standards for teacher training are the blueprints by which a country designs the type of nation it wants to be (Soysouvanh 2013). Programme accreditation is the means by which achievement is assured. As such, these academic standards are of fundamental importance. In the case of a mature and complex higher education sector, the responsibility for setting programme standards will be shared among relevant stakeholder groups (governments; professional bodies; independent quality agencies; universities themselves, the public media etc.).

Along with the standards for teacher training requirements are formulated which have to be met by teachers. Educational and pedagogic objectives play a central role. The following professional profile meets these goals with respect to schools (Spöttl & Becker 2012):

1. Teachers are experts for teaching and learning. Their core tasks are the target oriented and scientifically sound planning, organisation and reflexion of teaching and learning processes as well as their individual assessment and systemic evaluation. The professional quality of teachers is measured by the quality of their instruction.

2. Teachers are aware that their educational task at the school is closely linked to instruction and the school life. This is the more successful the closer the cooperation with parents is encouraged. Both sides must come to an agreement and should both be prepared to find constructive solutions for emerging educational problems or failing learning processes.

3. Teachers carry out their assessment and counselling tasks during instruction and in a competent, just and responsible way. Advanced pedagogical-psychological and diagnostic competencies of teachers are crucial for these tasks.

4. Teachers continuously develop their competencies and like any other professional group they make use of further and continuous training offers in order to consider the new developments and scientific findings of their profession. In addition teachers should always maintain contacts to external institutions and to the world of work.

5. Teachers participate in school development, in shaping a school culture suitable to enhance learning and to create a motivating school climate. This also includes the willingness to participate in external evaluations.

The important role of teachers and trainers is highly supported by the new skill agenda of the European Commission under the topic “Modernisation Efforts” (COM 2016).

4 Competency areas and emphases of teacher training

Standards in teacher training describe the requirements for the acting of teachers. They refer to the competencies and thus to the abilities, the skills and the attitudes of teachers to cope with their professional tasks. The targeted competencies trigger requirements for the entire training phase and the professional practice.

The vocational educational sciences are a basic prerequisite for the acquisition of competencies for vocational education. They encompass the vocational disciplines which deal with the educational and pedagogical processes, with educational systems, the practice of vocational training as well as with the respective framework conditions.

The formulation of competencies and standards for vocational education takes into consideration that education, instruction and learning in the world of work are closely linked to specialist contents.

The curricular emphases of the educational sciences during teacher training are:

a) Vocational scientific qualification in a vocational discipline;

b) Vocational educational qualification in the fields of

• Education and pedagogics
  Justification and reflexion of education and pedagogics in institutional processes.
• Profession and role of the teacher
  Professionalisation of teachers; dealing with conflicts and decision making situations linked to the profession.
• Didactics and methodology
  Design of instruction and learning environments.
• Learning, development and socialisation
  Learning processes of young people in school and in companies.
• Motivation for performance and learning
  Motivational basics of the development of performance and competencies.
• Differentiation, integration, promotion
  Heterogeneity and variety of conditions in schools and companies.
• Diagnostics, assessment and counselling
  Diagnosis and support of individual learning processes; performance measurement and assessment of performance.
• Communication
  Communication, interaction and conflict management as basic elements of teaching and education.
• Media education
  Handling of media in terms of concepts, didactics, and practical aspects.
• School development
  History of the educational system; structures and development of the educational system and the development of the individual school.
• Vocational Educational research
  Aims and methods of educational research; interpretation and application of the results.

Using standards in the education of teachers for technical education and vocational training means to ensure scientific quality in all fields of teacher training.

5 From quality indicators to open and dynamic standards[i]

The related standards for TVET teacher training describe the measures that are suitable to promote the change from the actual situation to the target situation. The clear addressee of the change in the named quality area is the teacher even if the necessary changes in the implementation certainly entail changes in different quality areas. Therefore standards are described in a way to clearly show which changes should be envisaged in terms of a quality improvement. Standards are, however, no curricula – the latter are developed based on standards. Nevertheless standards must name both the change processes and the targeted learning result. This requires an open and dynamic approach.

The current situation is determined in the respective VET institutions and results from a key question, an event or an identified problem. The decisions for the target situation have to be taken transparently by the project partners and are either based on results of teaching and learning research or on normative societal requirements. Standards are defined by the requirements for changes towards the target situation. They describe appropriate measures presumed to help to reach the desired target situation.

5.1 Quality areas and quality characteristics for the indication of changes

Quality areas mostly serve to name the characteristics for the processes, the results and the impact of educational measures which exert an influence on quality and to join their forces. (With reference to Altrichter und Posch (1990), these are named input, process and output/outcome qualities in most of the quality management systems for schools.)A considerable disadvantage of this structurization is the fact that a lot of focus is laid on the determination of a certain grade of quality for each named characteristic and that the acting persons in schools cannot clearly determine what has to be done in order to achieve an increase of quality. For example in the actual debate about quality indicators on international level the following definition of this term is used, which focus only on a state without respecting the need for developments and shaping measures: Indicator: “Quantitative and/or qualitative phenomenon measured and assessed” (CEDEFOP 2011) or Quality Indicator: “Formally recognised figures or ratios which are used as yardsticks to judge and assess quality performance” (ibid.). These assumptions about quality indicators are undoubtedly not enough to be able to support the quality of learning. In order to promote quality during the learning process via adequate shaping measures it is not sufficient to consider only the formal framework conditions.

Therefore quality characteristics are developed in the course of the Leonardo da Vinci project QualiVET (Nationale Agentur 2007) aiming at the change, the improvement and the shaping of “quality” with a focus on quality of the learning process. Characteristics and quality areas are defined in a way that they do not focus on the detectability and the meas­urability of a condition but that the changeability and the shaping of a discrepancy between the actual situation and the target state should be in the centre of interest. This becomes obvious with the denomination of the quality areas. Some key examples of quality areas are mentioned in Figure 1 as a result of the QualiVET project. It might be possible to identify some more quality areas via an empirical process. The further discussion in this article will focus on quality area 1 – trainers and teachers. 

Figure 1: Quality areas in QualiVET

The list in Table 1 shows the general understanding of quality terminology the authors are using. This fundamental quality terminology is applied to the subject “shaping” of the above named quality areas and the focus is “the process of change”. Each change of the mentioned quality areas is linked with interdependencies in other areas (see Figure 1). For example: If an instruction mode is changed, this entails different learning processes. In spite of the interdependencies characteristics – i.e. shapeable characteristics – can be named which especially aim at changing a certain aspect of the quality area.

With regard to the terminology of the quality terms we rely on the fundamental definitions in Table 1 – applied to actions for improvement. In a sense of focus on shaping and changeability we will use the term shaping oriented quality indicator for characteristics giving indications to changeable areas. The term indicator is of Latin origin (indicare) and means „show“, „specify“ and even „give away“. Thus an indicator shows or gives away something. The origin of the word clearly underlines that an indicator shows something that is not obvious at first sight (Windelband & Spöttl 2003, 3).

Table 1:            Terminology for quality terms

These quality areas – described in the overview below – are shapeable areas. 28 quality indicators were described in the project QualiVET (Nationale Agentur 2007) in these six quality areas of Figure 1 which have the function to support the key players in the shaping of the framework for learning. Some of the indicators will be explained later in this article. In the ASIA-Link Project this approach was extended to TVET teacher training and 29 standards were defined based on the idea of this approach (TT-TVET Consortium 2009, 151 ff.).

Table 2:            Description of the quality areas

5.2 Quality indicators and shaping measures as standards for quality development

A quality indicator consists of the designation of an actual condition and the naming of a target situation. The difference compared to measuring scales used in evaluation processes lies in the fact that not the measuring and the determination of a certain grade of quality is in the focus of interest. Moreover the changes necessary to improve the quality of practices requiring improvement are made visible. The quality indicator is designed in a way that it clearly indicates the necessary change. It is crucial that the change

a) describes an innovation in the quality area,

b) is expressively addressed to the quality area.

The latter means that in spite of interdependencies between the quality areas the changes have to be made by the addressee. In the example below (cf. Table 3): the teacher or trainer and his or her behaviour is the addressee (quality area 1). This can of course entail changes in teaching and learning methods. In the example the quality indicator shows that teachers should make use of tasks differing from those used so far in their teaching practice. The “target” (desired aim) of the example results from the German stipulation on the implementation of curricula that learning in the vocational school “should generally be based on concrete occupational acting” (KMK 2000, 10).

Table 3:            Example for a quality indicator oriented to changes

The entire standard describes the (minimum, maximum or medium) requirements for change.

The clear addressee of the change in the named quality area of Table 1 (Spöttl 2009) is the teacher even if – as mentioned above – the necessary changes in the implementation certainly entail changes in other quality areas as well. Corresponding to this example the addressee of change in the other quality areas is expressly “the learning process”, “the training and teaching methods”, “the training and teaching contents”, “the learning environment” and “the reflection of training and teaching”. Within the definition of standards these implications have to be considered.

Therefore standards are described in a way to clearly show which changes should be envisaged in terms of a quality improvement of the entire learning processes.

A definition given by CEDEFOP for the term quality standard is:

“Technical specifications which are measurable and have been drawn up by consensus and approved by an organisation recognised at regional, national or international level. The purpose of quality standards is optimisation of the inputs and/or outputs of learning” (CEDEFOP 2011).

As we can see with the help of this definition the optimisation in this sense the implementation of change plays an important role and also the input has to be shaped – not only measured (Spöttl 2014).

The desired aim of the standard (cf. Table 3) is based on a decision for the target situation which has to be transparently shaped (e.g. curricula, legal framework conditions, results of teaching and learning research, normative societal requirements).

The current situation of the learning process is determined in the respective TVET institution and results from a key question, an event or an identified problem and represents practices requiring improvement (cf. Table 3).

Standardsin this sense are defined by the requirements for the changes. They describe adequate shaping measures presumed to motivate and help to reach the desired target situation and to change the practices requiring improvement. It is obvious that the term „standard“ used here differs from terms describing a minimum requirement for a competence or the state of learning of a student (performance expectations). The standard does not describe the static condition but rather the shaping and changing itself. It is the changing that is the aim of a shaping oriented standard rather than the measuring and evaluation. Some European countries already work with this amended conception of standards which may contribute to the development of a better instruction. The novelty is focussing on the development and the changes in lieu of evaluation.

With shaping oriented standards new ways will be paved to initiate the development processes for an improved instruction. The freedom of shaping of the teachers is not reduced – as it might be presumed according to the above mentioned example. Moreover adequate shaping measures will be named which can be of help with the development of the desired instruction quality.

It must be underlined that the described conception of indicators and shaping oriented standards always concentrates on the process of

• learning,
• the shaping of the environment,
• the application of learning modes.

The standards mark the shaping framework in the form of a possible result that can be reached by a certain design of the learning process. They are, however, changeable during the process. Thus a certain dynamics should be promoted to avoid static procedures. Standards therefore should describe what students, teachers and the (training) organisation should know and be able to perform (this includes abilities). At the same time it should be characterized which results are possible during learning with regard to selected contents and how the learning environment should be shaped. In order to achieve this target it is necessary to characterize the indicators and standards in more detail.

5.3 Standards and Indicators

The specification of the indicators and standards is done according to Table 2 (Spöttl, Blings & Becker 2007, 96 ff.). Thus the problem and/or the core requirement in the field of acting of VET in the metal sector related to the indicator are described.

Indicators thus describe the process of changes which has to take place in order to attain the quality demands determined by the standards. Therefore standards must determine what the school, the school organisation, corporate learning environments, students, teaching staff and organisations „should know and be able to do / to ensure as a result of the study process or the contents or the shaping of learning environments etc.” (Henkens, Janssens & ten Brinke 2011).

Standards should name acting references for VET which not only determine their cognitive dimension but also contains process references. This is true for all quality areas and their standards.

With the example of a concrete training project in a vocational school, the characteristic of shapable standards can be explained: During the VET training course for a target group, the trainees have to solve a control technological tasks for the control of a rolling gate for a garage. In order to reach a high quality of the training situation, the trainer/ teacher (with reference to quality area 1) is facing certain requirements. The requirements result from

• the curricula where the training contents and the competences to be imparted are described,
• the concrete task which requires to play a certain teacher role,
• the available period of time, the available equipment and the prerequisites for students and trainers who prefer a certain approach and certain process structures.

With the aid of the requirements it is possible to name quality indicators and shaping measures as in Table 3. In this case the key question is whether the teachers or trainers are working in a team and how the team is acting within the whole situation.

Table 4:            Standard – sub-category “teamwork”

Standards and their further formulations are no substitute for curricula. Moreover they should be used for the implementation of curricula in the sense of guidelines. Standards do not stipulate what should “happen” during learning processes. They do, however, have a binding character when it comes to reaching the quality demands.

The example shown in Table 3 underpins that core questions can be used to formulate indicators and standards. By adhering to these considerations it is important to identify all significant key questions for a quality area. The QualiVet Project followed this approach and the project consortium identified 28 key questions for the six quality areas (www.qualivet.info). The Asia-Link Project TT-TVET extended the approach to TVET teacher training and defined 29 standards. In this case, however, the consortium focused on the learning environment of the individual partners for the implementation. If considering formulating standards for the teacher training of a country, it is useful to formulate sub-categories for the individual quality areas.

6 Summary

The article shows that indicators geared towards quality, combined with the targets to be reached form the basis for the formulation of standards. Based on indicators, the standards define a shapeable framework for the implementation of the targets. A creative leeway is crucial as learning processes cannot be shaped like technical functional routines. For the formulation of standards for vocational teacher education it is therefore important to always shape them in a way that learning results can be determined. On the other hand, however, it must be ensured that teachers are capable to shape learning processes in an open and dynamic way.

Quality areas, quality assurance and standards have to be developed in close connection. One critical point of standard development is the definition of indicators considering that really the right things are in the focus of the expected quality. Most of known standards neglect two points: The requirement of performing developments and the requirement to shape really crucial points for professional teacher acting.

(See the German standard debate and results under http://www.kmk.org/fileadmin/Dateien/veroeffentlichungen_beschluesse/2008/2008_10_16-Fachprofile-Lehrerbildung.pdf or the initiative in the UK under https://www.gov.uk/government/publications/teachers-standards ).

The shaping approach explained in the article could be an answer to this challenge. Especially the quality area 4 “training and teaching contents” should be in the main focus in the future because otherwise we risk that TVET teacher training misses the requirements of the working world of the learners.

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i This chapter is oriented to the outcome of the Leonardo da Vinci project QualiVET (Nationale Agentur 2007) and the ASIA-Link Project on Teacher Training in TVET (responsible coordinator: Georg Spöttl), funded by the European Commission, conducted by partners from Germany (ITB), Spain (AU), Indonesia (UPI and Malaysia (UTHM).

Citation

Spöttl, G. & Becker, M (2016). Standards – an instrument to enhance the quality of TVET teacher training. In: TVET@Asia, issue 7, 1-17. Online: http://www.tvet-online.asia/issue7/spoettl_becker_tvet7.pdf (retrieved 2.8.2016).