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Modeling the Characteristics of Finnish WorldSkills Competitors Vocational

Expertise and Excellence

Running head: Characteristics of Vocational Expertise and Excellence

Professor Petri Nokelainen

School of Education, University of Tampere, Finland

Contact person:

Professor Petri Nokelainen

School of Education

33014 University of Tampere

Finland

Tel: +358 40 557 4994

Fax: +358 3 3551 7502

Email: [email protected]

http://www.uta.fi/~petri.nokelainen

Paper presented at the annual meeting of American Educational Research

Association, Vancouver, Canada.

Nokelainen: Characteristics of Vocational Expertise and Excellence 2

Abstract

This mixed-method study investigated the role of Finnish WorldSkills Competition (WSC)

participants' natural abilities, intrinsic characteristics, and extrinsic conditions to their talent

development with qualitative (n = 30) and quantitative (n = 64) samples. The results of the

semi-structured interviews with competitors, their parents, trainers, and working life

representatives showed that self-reflection (stress tolerance), volition (perseverance, time

management skills), cognitive skills (development potential), and motivation (extrinsic and

intrinsic) were considered the most important characteristics. Characteristics related to

volition, self-reflection, and cognitive skills played an important role in all three

developmental stages of vocational talent (initial interest, perseverance, and mastery of the

skill). The role of both teachers and trainers was deemed important to the stages of vocational

talent development. The results of the survey showed that the most successful competitors

were characterized by their linguistic and interpersonal abilities. They also believed that effort

was more important to their success than ability. The most successful competitors were more

performance-approach goal oriented and less performance-avoidance oriented than were their

less successful peers. A supportive home and school atmosphere positively affected the

development of vocational talent. Future research directions regarding competitors’

characteristics should include examination of their mindsets, health (lifestyle), hobbies,

safety, and media skills, and also focus more on those WSC skill areas requiring teamwork.

There is demand for a longitudinal research design with control groups that would allow

examination of the long-term impact of natural abilities, intrinsic characteristics, and extrinsic

conditions on the development of vocational talent.

Keywords: vocational education, career and technical education, expertise, excellence,

characteristics, skills competitions


Introduction

International vocational competitions in different skill areas (e.g., plumbing, hair dressing) are

gaining increasing interest around the world. What started in 1947 as a small regional

competition in Spain has now become the WorldSkills Competition (WSC), a world-

renowned event that draws competitors and visitors from all over the world (56 countries

currently participate in WorldSkills competitions). For example, when Finland hosted the

2005 WorldSkills Competition in Helsinki, more than 40 000 visitors per day watched 700

international competitors working for five days on 45 skill categories. On the national level,

the Finnish Taitaja (“Expert”) competition in 2010 hosted 450 competitors in 40 skill areas

and almost 20 000 daily visitors. By comparison, the SkillsUSA Championships have been

held since 1967 and have become a multi-million dollar event with more than 5200

contestants in 91 separate events.

Whether it is a national or international vocational skill competition, the same parties are

involved: Students participate in training and competitions, teachers from vocational

education institutions train them together with national special units (e.g., WorldSkills

Finland), and industry offers financial support alongside local governments (e.g., Finnish

Ministry of Education and Culture). To determine why and how the system works, we

conducted structured interviews (N = 30) with key actors in Finnish skill competitions during

the preparation phase of this study in 2006 -2007. The non-probability sample included

principals and teachers of vocational education providers (n = 13), corporate sponsors (n =

12), and government leaders (n = 5) involved in WorldSkills Finland operations.

The results showed that providers of vocational education aim to attract capable students

by providing high-quality working premises and up-to-date teaching. Vocational teachers who

train competitors in these skills were motivated to update their knowledge and be recognized

as leading experts in their skill areas. Participating companies reported an interest in raising

their public profile and attractiveness as employers to hire a skilled and motivated workforce.

Government officers aimed to improve the attractiveness and quality of vocational studies and

were keen to see a more suitable match between vocational training (including “soft skills”

such as social, psychological, and ethical skills) and working life requirements.

The results also indicate that both corporate and governmental support for the vocational

skills competitions has impacted on the attractiveness and quality of vocational education at


the national level. For example, in 2008, for the first time in 30 years, more applicants applied

for vocational (60%) studies than for upper-secondary level (40%) studies in Finland. An

example related to quality improvement is the up-to-date (and very expensive) machinery

used in the national skills competitions that the sponsoring companies donate to the hosting

vocational institutions for teaching purposes.

In this paper, we report on the characteristics and future working life expectations of

young individuals who participate in international skills competitions. Further, we investigate

whether the characteristics of the most and least successful competitors differ. All the Finnish

WSC team members eligible to compete at the international level are considered vocational

experts, but only the most successful competitors represent vocational excellence in their skill

area.

To address these goals, we formulated the following research questions: (1) What

characteristics are specific to WSC competitors? (2) How do the characteristics of WSC

competitors differ during the training period, competitions, and working life? (3) What

characteristics are specific to WSC competitors' initial interest in the field, perseverance in

acquiring a vocational skill, and mastery of that skill? (4) What characteristics are specific to

the employers of WSC competitors? (5) What are WSC competitors’ most essential natural

abilities? (6) What are WSC competitors’ most essential self-regulatory abilities? and (7)

What is the influence of domain-specific and non-domain-specific factors on the talent

development of WSC competitors?

By answering these questions, we can draw some conclusions about the characteristics

typical of young talented workers in various skill areas. An understanding of the relationship

of these characteristics to success in international skills competitions enables us to highlight

the most important factors influencing the development of exceptional vocational talent.

This paper is organized as follows: First, the theoretical components of the

characteristics of vocational expertise and excellence are discussed alongside the existing

body of research. Second, the results are presented regarding the seven research questions.

Finally, the paper discusses the limitations of the study and provides directions for future

research.


Theoretical framework

In working life, all workers must possess cognitive skills and take part in decision-making

processes. With experts, such skills are linked to a high capacity to analyze domain-specific

information and to understand the basics and the meanings of different work tasks (Ruohotie,

2004). Their cognitive processes are characterized by the complexity of domain-specific

knowledge structures and a deep understanding of related concepts (Pillay, 1998).

However, knowledge structures differ from declarative knowledge. The former relate to

the analysis or parsing of information, whereas the latter describes the accumulated body of

knowledge or learned facts. The structure of knowledge stored in one’s memory may be more

important for learning than the amount of it. Knowledge structures affect subsequent

knowledge parsing and memory-retrieval processes. The retrieval of information from

memory speeds up and deepens understanding, helps in decision making and the anticipation

of future events, and makes it easier to find optimal solutions to problems. (Day, Arthur &

Gettman, 2001.)

In addition, metacognitive skills are necessary in working life. According to Driscoll

(2005, p. 107), “... metacognition refers to one’s awareness of thinking and the self-regulatory

behavior that accompanies this awareness.” Experts have the ability to analyze work-related

non-routine problems. They are able to anticipate the development of their field and take

responsibility for the effectiveness of their work practices. These higher-level thinking skills

are related to cognitive processes, such as critical thinking, problem solving and creative

thinking. Those processes advance professional knowledge, deepen one's understanding of

knowledge, and increase the transferability of knowledge and skills (Pillay, 1998). Thus,

individuals may have strong professional knowledge, but still have extensive shortcomings in

their thinking skills.

Attributions

Attributions are the reasons individuals give for an outcome, such as success or failure in a

task (Heider, 1958). Weiner (1974, 1980, 1986, 1994, and 2000) has shown that factors

involved in attributional thinking, such as specific reasons for success and failure, are related

to achievement settings. In his studies, Weiner found that the four most frequent reasons for

success and failure are ability, effort, task difficulty, and luck.


Dai, Moon, and Feldhusen (1998) classify attribution constructs into three groups. First,

attribution appraisals are online explanations assessed following actual or manipulated

success or failure in performing a specific task. Second, attribution beliefs are domain-specific

or domain-general beliefs about the causes of success or failure. Third, attribution styles are

generalized, stereotypical patterns of attributions and dispositional beliefs. Attribution styles

are assessed in a similar way to attribution beliefs, except that a certain typology is imposed

on the data using predetermined criteria.

In this paper, attribution styles are examined using Weiner’s (1992) classification of

reasons for success and failure: 1) Origin: Internal and external attributions, referring to

within or outside individual causes; 2) Stability: Stable and unstable attributions, referring to

consistent or inconsistent causes over time; and 3) Degree of control: Controllable and

uncontrollable attributions, referring to the extent to which an individual believes he or she

has control over the cause of an outcome.

Working life requires experts to possess strong professional knowledge, the ability to

transfer their skills and knowledge, and high metacognitive skills. In other words, they must

be both competent and qualified. Competence is the potential capacity of an individual to

successfully complete a certain task according to certain criteria set by someone else

(Ellström, 1994). An interesting point is that a competence may also be seen as an attribute of

an individual (i.e., ‘self-attribution’; see Heider, 1958), for example, referring to a human

resource that the person brings to a mathematical problem-solving situation (Nokelainen, Tirri

& Merenti-Välimäki, 2007). Thus, according to Weiner (1986), self-attributions may

emphasize potential competence as indicated by the capacity of an individual to successfully

complete tasks and to face new challenges on the basis of demonstrated personal attributes

and abilities (other than those obtained through formal training).

Ellström (2001) defines qualification as a competence that is actually required by a work

task or is implicitly or explicitly determined by individual qualities or both. He has noticed

that potential competence may vary greatly between individuals with the same formal

qualifications because they may possess very different levels of inherent ability and may have

learned different things outside of school or their studies through their working life or

recreational activities or both. Williams (2002, p. 103) has defined these efficiency beliefs as

follows: “Trust in one's abilities to plan and execute the activities that lead towards a skilful

accomplishment.” Thus, self-attributions affect subsequent performance expectations and, in


negative cases, the development or continuation of learned helplessness (Ruohotie &

Nokelainen, 2000).

Self-regulation

According to a social cognitive view of self-regulation, expertise develops from both external

support and self-directed practice sessions (Zimmerman, 2006). Bloom’s seminal study

(1985) clearly showed the importance of both of these factors for exceptional talent

development. Self-regulatory competence has three elements: The self-regulation of covert

personal processes, behavioral performance, and the environmental setting (Bandura, 1986).

Research has shown that successful learners can monitor and regulate these triadic elements

(e.g., Kitsantas & Zimmerman, 2002; Zimmerman, 1989, 1998; Zimmerman & Kitsantas,

2005).

In this study, we applied Zimmerman’s model of self-regulation such that the term ‘self-

regulation’ refers to the process through which self-generated thoughts, feelings, and actions

are planned and systematically adapted as necessary to affect one’s learning and motivation

(Schunk & Ertmer, 2000; Zimmerman, 2000). Zimmerman (1998) describes the self-

regulation of learning tasks as a cyclical, three-phase process. The phases in this learning

cycle are, in this order: forethought (motivation), performance (volitional control), and self-

reflection (attributions).

Analysis of both motivation and volition is necessary to understand why WSC

competitors engage in their required training and wish to become experts in their field. When

the aim is to investigate why some gifted young manual skill workers reach the top level of

vocational talent and others do not, we must also address the role of attributions, that is,

beliefs about success and failure.

Motivation, volition, and self-reflection

To understand individual differences between WSC competitors in the vocational skill

acquisition process, we examined the taxonomy of cognitive, affective, and conative

constructs developed by Snow, Corno and Jackson (1996). Cognition is a generic term for

those processes through which an organism recognizes and obtains information about a

certain object. Cognitive constructs include the following concepts: perceiving, recognizing,

conceiving, judging, and reasoning. Affect is the feeling response to a certain object or idea.


Sometimes it means the energy resulting from an emotion or a general reaction to something

that one likes or dislikes. Affective constructs include feeling, emotion, mood, and

temperament. Conation refers to those mental processes that help an organism to develop; it is

a kind of intrinsic unrest (the opposite of intrinsic balance or homeostasis) or a conscious

tendency to act or strive for something. Conative constructs include impulse, desire, volition,

and purposeful striving.

Personality and intelligence are the top-level concepts in the taxonomy (Snow et al.,

1996). Personality is rarely limited to personal characteristics, temperament, or emotions

alone. In general, personality includes all those factors that make a person an individual.

Many personality traits are also linked to cognitive factors. Intelligence means, firstly, the

ability to undertake activities which are difficult, complex, abstract, demanding, goal oriented,

socially prestigious, and original; and, secondly, the ability to accomplish these activities in

situations which demand concentration and the control of one’s emotions (Barrow & Milburn,

1990).

The concepts at the next level in the taxonomy — cognition, affect, and conation — are

each further divided into two subcategories. Cognition includes a distinction between

declarative and procedural knowledge. Declarative knowledge is a sort of knowledge network

in which concepts and facts are linked. New knowledge emerges as a result of the

construction of knowledge; in short, analyzing the interdependencies of different pieces of

knowledge. Procedural knowledge can be expressed as a set of procedures or rules that help in

remembering and applying knowledge. This, however, presupposes and depends on

declarative knowledge. Affect is divided into temperament and emotion. Temperament refers

to biological traits that are independent of situational factors, whereas an emotion may be

strongly linked to a given situation. Conation includes both motivational and volitional

aspects of human behavior (Snow & Farr, 1987; Snow & Jackson, 1994).

Motivational aspects cover intrinsic and extrinsic goal orientations, fear of failure, need

for achievement, self-esteem, belief in one’s own abilities and potential (efficacy beliefs),

value of incentive (valence), and different attribution interpretations. Volition includes

persistence, the will to learn, endeavor/effort, mindfulness in learning, intrinsic regulation and

evaluation processes, as well as various control strategies (e.g., allocation and control of

resources as well as emotional attentiveness and motivational control strategies) and styles of

processing knowledge.


Self-judgment as a sub-component of self-reflection leads to attribution interpretations

in which an individual interprets the reasons for success or failure. The most widely applied

theoretical perspective on attribution interpretations is Weiner’s attribution theory (1974),

which is based on the principle that an individual is constantly searching for understanding of

why an event has occurred. Attribution interpretations can lead to both positive and negative

self-reactions. The individual may interpret the failure of a strategy as the result of too little

effort and then increase his or her subsequent efforts. But if he or she attributes the failure as

due to a lack of ability, the reaction will most likely be negative. Attribution interpretations

reveal the possible explanations for learning mistakes and help the learner to identify those

learning strategies which best suit a given situation.

The conative element of the preceding taxonomy of individual differences in intelligence

and personality is particularly important in the context of self-regulation. According to Corno

(1989), motivational processes help the learner to formulate decisions and to promote

decision-making, whereas volitional processes guide the subsequent enactment of the

decision. It is useful to distinguish between pre-decisional processes of motivation and post-

decisional processes of volition because even highly motivated young manual skill workers

may have problems setting clear goals and pursuing their intentions.

Volition and motivation (i.e., conation) can be explained using various dynamic cycles

(Snow et al., 1996) that connect volitional constructs, motivational factors, and learning

outcomes. These connections are strongly influenced by the context (Corno, 1993). One

cannot assume that all WorldSkill competitors function methodically and deliberately, and

that different processes or constructs will affect everyone in the same way. Volition and

motivation depend on the learning task as well as the learner's natural abilities and

reinforcement experiences (how learning has been reinforced previously).

The motivational expectancy model (Pintrich, 1994) offers one way to categorize and

integrate the central elements of self-regulation. This model includes different beliefs or

expectancies, such as perceived competence, test anxiety, perceptions of task difficulty, the

learner’s belief in his or her efficacy, and expectancy of success. The model also includes

concepts relating to volition, namely metacognitive strategies related to persistence

(concentration, determinance), and time and resource management strategies

(methodicalness). A young competitor on manual skills with a strong self-image and high

expectations will put more effort into the learning task and will persist longer, even on a

difficult task, than will a person with a low expectancy of success.


Although Pintrich’s model deals with learning goals, we complement our approach with

goal orientation theory, which further discriminates between mastery and performance goals,

approach and avoidance goals, and task and ego involvement (e.g., Ames, 1992; Nicholls,

Cheung, Lauer & Patachnick, 1989; Elliot & Harackiewicz, 1996). Mastery goal-oriented

competitors enjoy learning new skills because they find them inherently interesting. They

seek to develop their competence and to aim at achieving mastery and a deep understanding

of their skill area (“I practice a lot because I want to master the skill perfectly”). Their task

and ego involvement is directly related to mastery goal orientation, but in this case, the

attention focuses on the task (Midgley et al., 2000). Performance goal orientations are linked

to approach and avoidance goals, usually labeled performance-approach and performance-

avoidance goal orientations. The former is related to the demonstration of competence,

whereas the latter is related to avoidance of the demonstration of incompetence. In both forms

of performance goals, attention focuses on the self (“Others will see how good I am” or “I

won’t let others see that I can’t complete that task”). Mastery goal orientation could also be

labeled ‘mastery-approach goal orientation’, but there is no need to do so as our approach will

not utilize ‘mastery-avoidance goal orientations’.

An interesting link exists between attributions (self-reflection) and goal orientations

(motivation): Adaptive and maladaptive patterns of learning. The aforementioned attribution

interpretations promote an adaptation process in which the learner is able to apply new

learning strategies to cope with challenging tasks; consequently, self-regulated individuals are

more adaptive and evaluate their performance appropriately (Bandura, 1997). Positive

reactions (e.g., self-satisfaction) reinforce positive interpretations during vocational training

and enhance intrinsic interest in the task, thereby supporting mastery goal orientations.

Maladaptive individuals are unable to change their learning strategies when facing challenges

and may encounter negative feedback that leads to performance-avoidance goal orientations.

Research suggests that performance-approach goal-oriented individuals tend to apply both

adaptive and maladaptive patterns in their learning (Midgley et al., 2000).

We believe that goal orientations play a significant role in WSC training and

competitions and help us to understand more thoroughly why some competitors are unable to

utilize their full potential. It could be that they have more performance-avoidance goal

orientations (maladaptive patterns of learning) than do their peers, who perform more

effectively with the right combination of mastery and performance-approach goal orientations

(adaptive patterns of learning).


Natural abilities

Gagné (2004, 2010) has developed a Differentiated Model of Giftedness and Talent (DMGT)

which distinguishes the two frequently intertwined concepts of giftedness and talent. The

DMGT comprises of six components: 1) Chance (e.g., genes), 2) Gifts (i.e., intellectual,

creative, socio-affective, sensori-motorical, and other natural abilities), 3) Intrapersonal

characteristics (physical, motivation, volition, self-management, personality), 4)

Environmental conditions (milieu, important persons, provisions, events), 5) Developmental

processes (informal and formal learning and practicing), and 6) Talents (systematically

developed skills).

Gagné’s view of talent development is summarized in the C.GIPE acronym (Figure 1).

Chance (C) assumes a predominant role in the DMGT, as it includes both genetic and parental

endowments that affect one's natural abilities (G, gifts) and intrapersonal catalysts (I). Chance

represents the degree of control over talent emergence in a way similar the way in which

attributions of success and failure are classified within the three-dimensional system (origin,

stability, degree of control) of attribution theory (Weiner, 1986) discussed earlier.

-- Insert Figure 1 about here --

Natural abilities (G) precede intrapersonal catalysts (I), such as motivational constructs.

The reason is drawn from the existing body of research, which shows how IQ scores ‘account

for’ on average five times or more variance in achievement than do measures of motivation

(Gagné, 2004). Further, because practice is based on the existence of self-regulatory

components (I), such as motivation, volition and self-reflection (including attributions),

intrapersonal catalysts (I) precede the practice (P) component. Gagné’s argumentation for the

P component’s position in the C.GIPE causal chain is that in order to excel, one really needs

more than practice alone, but also both gifts and ability to keep things under control, that is,

self-regulation. This leads to the conclusion that intrapersonal catalysts I causally precede

practice P. Environmental influences (E) have been placed in the last position because

differences in ‘normal’ environments does not explain the difference between average and

outstanding achievements.


Ericsson stresses the role of deliberate practice in the development of talent, stating that

in most fields, to become an expert requires ten years (Ericsson, Krampe & Tesch-Römer,

1993). Subsequent research has shown that the ten year rule is not absolute: in some fields

(e.g., chess, sports), total mastery of the skill requires about six years, and in other fields (e.g.,

music, science) reaching the top level requires 20-30 years of deliberate practice (Ericsson,

2006). His relative approach to the study of the characteristics of experts assumes that the

fundamental capacities and domain-general reasoning abilities of experts and non-experts are

virtually identical (Chi, 2006). The major difference between experts and novices is that the

former are more knowledgeable, through deliberate practice, than are the latter.

However, Gagné’s DMGT is based on a different approach to the study of the

characteristics of experts: the goal of the absolute approach is to understand how truly

exceptional people perform in their domain of expertise (Chi, 2006). In this study, we

differentiate the concepts of ‘expertise’ and ‘excellence’ by the level of natural abilities each

individual possesses. Gifted individuals with exceptionally high levels of natural abilities

(intellectual, creative, socio-affective, sensori-motorical), intrinsic characteristics (physical

characteristics, self-regulation, personality), and auspicious extrinsic conditions (physical,

cultural and sociological milieu, important individuals, programs, activities, awards,

accidents) may achieve vocational excellence through deliberate practice. Individuals who do

not meet all of these conditions may still become vocational experts through deliberate

practice. Not all Finnish WSC team members will achieve vocational excellence in their work

careers, but most will become vocational experts or at least skillful workers (professionals).

Because the DMGT emphasizes the role of natural abilities in the development of

talent, it is sensible to use one of the most well-know categorizations for individual

giftedness: Gardner’s Multiple Intelligence (MI) theory (1983, 1993, 1999). Sternberg (1991)

identifies MI theory as a systems approach similar to his own triarchic theory. MI theory was

first introduced in 1983 with seven dimensions: 1) Linguistic, 2) Logical-mathematical, 3)

Musical, 4) Spatial, 5) Bodily-kinesthetic, 6) Interpersonal intelligence, and 7) Intrapersonal

intelligence. Later, Gardner (1999) discussed the possibility of adding more dimensions, such

as naturalist, spiritual and existential, to the model. In the context of the current study, it is

interesting to observe how WSC competitors view the importance of different areas of

intelligence to their vocational talent development. Further, this study will explore how these

natural abilities are related to the actualization of their potential in the context of WorldSkills

competitions.


Method

Sample

Structured interviews (n = 30) were conducted in 2007 – 2009 with 14 young Finnish experts

who had participated in WorldSkills competitions in 2005 (Helsinki, Finland), 2007

(Shizuoka, Japan), and 2009 (Calgary, Canada). Eight of the competitors were males (Mage =

21.0, SDage = 1.000) and six were females (Mage = 22.0, SDage = 7.000). In addition, interviews

were also conducted with their trainers, working life representatives, and parents (n = 22).

The participants in the interview study represented the following WSC categories (their

most important connections to the MI theory appear in parentheses): IT/Programming (logical

mathematical, intrapersonal), Web Design (logical mathematical, spatial, interpersonal),

Plumbing & Heating (bodily-kinesthetic, spatial), Beauty Therapy (interpersonal, bodily-

kinesthetic, spatial), Hair Dressing (interpersonal, bodily-kinesthetic, spatial), Stonemasonry

(bodily-kinesthetic, spatial), Catering (bodily-kinesthetic, interpersonal), Robotics (logical

mathematical, spatial), Landscaping (bodily-kinesthetic, spatial), and Caring (bodily-

kinesthetic, interpersonal).

Numerical empirical data (n = 64) were collected from the Finnish WSC competitors in

2008 – 2009, who represented the 2007 (Shizuoka, Japan) and 2009 (Calgary, Canada) teams.

The response rate was 77 per cent of the total target population (N = 83). The sample

comprised 44 male (68.8%) and 20 female (31.2%) respondents. The average of the male

respondents was 20.9 years (SD = 1.676), and of the female respondents, 20.8 years (SD =

1.735). The participants of the survey study covered 23 of the 43 WSC categories, thus

accounting for most of the intelligence areas of the MI theory.

The concepts of vocational expertise and excellence used in the survey study were

defined as follows: All the Finnish WSC team members eligible to compete at an international

level were considered vocational experts and were coded into groups B (positions 8 – 11 in

international skills competitions, n = 17) and C (positions greater or equal to 12, n= 17). The

most successful competitors were coded into group A (positions 1 – 7, n = 27), as they

represented vocational excellence in the study. Total sample size in statistical analyses was

61, as competition performance index could not be defined for three competitors.


Instruments

The qualitative sample was collected to address the first four research questions. Interviews

lasted from 60 to 90 minutes and were recorded in digital video format. They were analyzed

using the content analysis method (e.g., Krippendorff, 2004). The interview framework was

based on the significance of self-regulation, cognitive, and social features in the development

of vocational talent (Greenspan, Solomon & Gardner, 2004; Nokelainen, 2008; Zimmerman,

1998). In addition, the significance of intrinsic and extrinsic goal orientations was examined

on three levels: initial interest in learning a vocational skill, perseverance during the learning

process, and mastery of the skill (Bloom, 1985).

The quantitative sample, addressing research questions 5 – 7, was collected with a

questionnaire containing 124 five-point Likert scale items and two open-ended statements.

The response options on the Likert scale ranged from 1 (totally disagree) to 5 (totally agree).

We also recorded the respondents' demographic (contact details, age, gender) and other

relevant background information (middle school and vocational study GPA, WorldSkills

Competition skill area, experience and success).

The fifth research question concerning natural abilities was investigated using the MIPQ

III (Multiple Intelligence Profiling Questionnaire, Tirri & Nokelainen, 2008). The MIPQ III

measured nine dimensions of Gardner’s Multiple Intelligence theory with 35 statements: 1)

Linguistic (e.g., “Writing is a natural way for me to express myself”), 2) Logical-

mathematical (e.g., “At school, I was good at mathematics, physics, or chemistry”), 3)

Musical (e.g., “I notice immediately if a melody is out of tune”), 4) Spatial (conceptualization

of space, e.g., “When I read, I form illustrative pictures or designs in my mind”), 5) Bodily-

kinesthetic (manual skills, e.g., “I was good at handicrafts at school”), 6) Interpersonal (social

skills, e.g., “I make contact easily with other people”), 7) Intrapersonal (self-knowledge, e.g.,

“I am able to analyze my own motives and ways of action”), 8) Spiritual (communality, e.g.,

“In the midst of busy everyday life, I find it important to contemplate”), and 9) Environmental

(valuation and knowledge of nature, e.g., “Protecting nature is important to me”) intelligence.

The sixth research question regarding self-regulatory abilities was investigated with two

instruments: the APLQ (Abilites for Professional Learning Questionnaire, Nokelainen &

Ruohotie, 2002) and PALS (Patterns of Adaptive Learning Scales, Midgley et al., 2000). The

APLQ is a vocational education modification of the Motivated Strategies for Learning

Questionnaire by Pintrich and his colleagues (1991). The instrument consists of six


motivational dimensions measured with 12 statements: 1) Intrinsic goal orientation (e.g., “I

am very interested in my skill area as well as the new information related to it”), 2) Extrinsic

goal orientation (e.g., “I want to be number one in my skill area in the forthcoming World

Skills Competition”), 3) Meaningfulness of studies (e.g., “I believe that WSC training will be

of practical benefit to me in the future”), 4) Control beliefs (e.g., “I am able to learn even the

most difficult work methods if I practice hard enough”), 5) Efficacy beliefs (e.g., “I am

confident that I will master even the most difficult work methods in my WSC training”), and

6) Test anxiety (e.g., “While doing a routine task in the WorldSkills Competition, I am

concerned about the really challenging upcoming tasks”). The instrument also contains a

learning strategy scale with two dimensions related to volition: 1) Metacognitive strategies in

studies (perseverance, e.g., “I set clear goals for my learning”) and 2) Time and resource

management strategies (methodicalness, e.g., “I usually have enough time to practice before

the competition or other display of my skills”). PALS has three scales measured with nine

items: Mastery goal orientation (e.g., “I try to understand issues presented in the WSC

training as thoroughly as possible”), performance-approach goal orientation (e.g., “My aim is

to show others that I am in the top level in my skill area”) and performance-avoidance goal

orientation (e.g., “I avoid showing others if I am facing difficulties in WSC training

exercises”).

The seventh research question measured the influence of domain- and non-domain-

specific factors on the development of the WSC competitors’ talent. Thirteen questions were

developed on the basis of existing research (Campbell, 1996) and the interviews conducted in

the earlier stages of the study. These questions measured the influence of 1) the Home

atmosphere (“An encouraging home atmosphere”), 2) Friends (“The stimulating influence of

a particular friend”), 3) the School atmosphere (“The stimulating influence of a teacher or

trainer”), 4) Artifacts (“Seeing impressive demonstrations of skill, such as furniture or a

hairstyle”), 5) Work opportunities in the future (“Employment in the future”), and 6) the

Company of people sharing similar interests (“Team spirit amongst the WSC trainees”).

Statistical analyses

The questionnaire data were statistically analyzed with non-parametric techniques due to the

small size of the sample, the mixture of both discrete and continuous measures, and the

possible presence of both linear and non-linear dependencies between the variables. Research


questions 5 to 7 were investigated using Bayesian Dependency Modeling (BDM) and

Bayesian Classification Modeling (BCM).

BDM estimates the most probable Bayesian Network (BN) based on the available data,

thus allowing the researcher to examine statistical dependencies between observed variables

(Myllymäki, Silander, Tirri & Uronen, 2002; Nokelainen, 2008). The strength of a

dependency between two variables in a BN (i.e., a line connecting two nodes) is evaluated on

the basis of how much the probability of the model would drop if the connection is removed.

The design of the current study enables the investigation of naïve causality (the assumption

that latent causes are absent), as the research evidence is based on multiple data sources

collected over time: the characteristics of WSC competitors were measured during the

training period, and their competition success index (A, representing the highest performers,

and C, representing the lowest performers) was compiled later on the basis of their

performance in international skills competitions. BCM to some extent resembles Linear

Discriminant Analysis, but instead of using mechanistic predictor variable selection methods

(e.g., forward, backward), it uses genetic algorithms (Myllymäki et al., 2002; Nokelainen,

2008). This data mining approach derives the most probable set of predictor variables (BN)

for a given class variable (competition success in this study). The classification accuracy of

the model is provided and compared to the baseline classification accuracy (i.e., classifying

the cases without the BN). The advantage of using these discrete Bayesian multivariate

computation techniques is that they allow linear and non-linear statistical analysis of

continuous and non-continuous variables without the sample size limitation or assumption of

normality (for a more detailed discussion, see, e.g., Gill, 2002; Nokelainen, Silander,

Ruohotie & Tirri, 2007).

Results

Interviews

RQ 1. What characteristics are specific to WSC competitors?

The results of the interview study showed that the following are the essential characteristics of

the WorldSkills competitors (in order of importance): 1) Stress tolerance (calmness, good

nerves), 2) Perseverance (exactness, ability to concentrate, determination, carefulness), 3)

Development potential (manual, perceptive, and problem-solving skills), 4) Competitiveness


(ambition), 5) Interest in work, 6) Social skills, and 7) Time management skills

(methodicalness). There were no major differences between the views of WSC competitors,

their trainers, parents and working life representatives.

Their essential characteristics were further classified into the following dimensions

according to the theoretical framework (in order of importance): 1) Self-reflection (stress

tolerance), 2) Volition (perseverance, time management skills), 3) Cognitive skills

(development potential), 4) Extrinsic goal orientation (competitiveness, ambition), 5) Intrinsic

goal orientation (interest towards work), and 6) Social skills. (Figure 2).


The significant role of self-reflection can be justified theoretically: Experts stand out

from other professionals because they can recognize and control changes in their emotions

(Day, Arthur & Gettman, 2001; Pillay, 1998; Ruohotie, 2004). In addition, volition plays a

significant role in the development of vocational talent, since the cultivation of natural

abilities to the level of vocational excellence requires perseverance. The small role of social

skills could be predicted, since the WorldSkills competitors interviewed were mostly involved

in individual skill areas (IT/Programming, Web Design, Plumbing & Heating, Stonemasonry,

Catering, Robotics, Landscaping, and Caring), except for Beauty Therapy and Hair Dressing.

The above-mentioned essential characteristics are almost identical to those of American

Olympic medalists in sports (Gould, Dieffenbach & Moffett, 2001) and international academic

Olympians in mathematics, physics, and chemistry (Heller & Lengfelder, 2000; Nokelainen,

Tirri & Campbell, 2004; Nokelainen, Tirri, Campbell & Walberg, 2007; Wu & Chen, 2001).

This observation is supported by earlier studies (e.g., Ericsson, Krampe & Tesch-Römer,

1993) that emphasize the role of deliberate practice in the development of expertise.

RQ 2. How do the characteristics of WSC competitors differ during the training period,

competitions, and working life?

The interviews showed that characteristics related to volition and self-reflection, as well as

cognitive skills (development potential), played an important role in all three developmental

stages of vocational talent (training, competitions, and working life). According to the results

(see Figure 3), volition is the most important characteristic in a competitive situation, and the


significance of social skills increases when one enters working life. Because the role of social

skills is important to future career development, vocational training providers may wish to

address this issue in their pedagogy (collaborative learning tasks) and curriculum design

(social skills development courses).


Intrinsic (“I want to learn this thing because mastering it is a rewarding experience in

itself.”) and extrinsic (“I want to learn this thing because then, others will also see how good I

am.”) goal orientations had little significance in the three stages of vocational talent

development. It is important to remember, however, that as motivational factors, they have

considerable indirect significance preceding volition in the self-regulation process

(Zimmermann, 1998, 2000).

RQ 3. What characteristics are specific to WSC competitors' initial interest in the field,

perseverance in acquiring a vocational skill, and mastery of that skill?

The first level of the analysis examined external factors that were presumably to be connected

to the development of vocational talent: 1) Individuals directly related to the development of

talent (other members of the work group, trainer, audience, working life representatives), 2)

Individuals indirectly related to the development of talent (parents, other relatives, neighbors,

fellow students), and 3) Artifacts that have affected the development of talent (e.g., books,

films, music, and other works). The influence of each external factor was studied based on the

model of Greenspan, Solomon, and Gardner (2004) with a primary focus on the role of

intrinsic and extrinsic motivation in the operation of the aforementioned factors (Connell,

Sheridan & Gardner, 2004).

The second level of the analysis focused on the participants as individuals. On the basis

of Bloom’s (1985) model, competitors’ talent development was analyzed in three stages: 1)

Initial interest in the skill area and training, 2) Perseverance during training, and 3) Mastery of

the skill. In addition, competitors' intrinsic and extrinsic goal orientations were examined.

(Figure 4.)

The results indicated that the role of both teachers and trainers is important in all of the

early stages of vocational development (initial interest, training, mastery). Intrinsic goal


orientation proved to be more important than extrinsic goal orientation in the development of

interest in a vocational field and mastery of the skill. During the training, extrinsic goal

orientation was reportedly more important than intrinsic goal orientation. Greenspan,

Solomon, and Gardner (2004) have reported similar findings in their studies of the arts. The

results also showed that when the mastery level was achieved, securing future employment

and challenging job opportunities become the most essential factors. The importance of social

motivation (friends, training teammates) remained relatively low in all the stages of the

process.


RQ 4. What characteristics are specific to the employers of WSC competitors?

Both the WorldSkills competitors and their trainers emphasized the importance of challenging

work assignments from their employer. The freedom and responsibility in work assignments

were considered significant to the development of self-esteem and maintaining the motivation

to work. The responses stressed the importance of good leadership even though its content

was not specifically defined. Interviewees pointed out that their superior’s relaxed and no-

nonsense leadership style was especially encouraging.

The competitors expected their future employer to encourage them to develop their skills

and to reward them for it. This finding is related to the valuation of competitive activities that

both the competitors and their trainers mentioned. Based on the interviews, an employer who

really values vocational excellence was expected to pay a decent salary. However, it is worth

noting that salary was regarded as one of the least important features of future work.

Survey

The survey results indicate that middle school GPA is no predictor of vocational skill

competition success. Non-significant but negative correlations were found between skill

competition success and one's grades in middle school math, native language, first foreign

language, religion, music, sports, and handicrafts. Those individuals in group C had slightly

higher certificates of graduation from middle school (MA group = 7.9, SDA group = .773; MC group

= 8.1, SDC group = .820), as well as higher school achievement in mathematics, foreign


language, religion, physical education, and handicrafts than did those in group A. However,

none of these differences reached statistical significance. The results are plausible, as subject

domains in middle school are general rather than specific to vocational skills.

Success in vocational studies proved to be related to vocational skill competition

success: A positive correlation (r = .41, p = .012) was found between vocational school GPA

and skills competition success. The most successful competitors did better in their preceding

vocational studies (M = 4.8, SD = .428) than did the least successful competitors (M = 4.4, SD

= .518). Because the skills assessed in vocational competitions represent real working life

skills, one may further argue that education provided by vocational schools supports the

development of vocational talent.

RQ 5. What are the WSC competitors' most essential natural abilities?

We applied Howard Gardner’s theory of Multiple Intelligences (MI, see Gardner, 1983, 1993,

and 1999) to answer the fifth research question. Our first hypothesis postulated that bodily-

kinesthetic intelligence would be the most important natural ability in most skill areas, except

for IT/Programming and Web Design. Secondly, we hypothesized that high logical-

mathematical thinking abilities would benefit competitors in most skill areas.

According to the Bayesian Network produced with BDM, the WorldSkills competitors’

main intelligence areas were (in order of importance): Manual skills (bodily-kinesthetic),

logical-mathematical thinking skills (logical-mathematical), social skills (interpersonal),

spatial skills, and self-knowledge (intrapersonality). The least important intelligence areas

were environmental, musical, spiritual, and linguistic. However, when examining the

differences in intelligences across skill areas, we found that the competitors showed the

greatest differences in linguistic skills and spiritual sensitivity (e.g., the ability to contemplate

in busy everyday life situations). The skill areas that most emphasized these intelligences

were Floristry, Graphic design, Caring, and Beauty therapy.

BCM served to derive the most probable network of predictors of success in

international skills competitions. Classification variables fell into three categories: 1 = group

C (the least successful competitors), 2 = group B, and 3 = group A (the most successful

competitors). Predictor variables in the analysis were the nine Multiple Intelligence

dimensions: Linguistic, mathematical-logical, spatial, bodily-kinesthetic, musical,

interpersonal, intrapersonal, spiritual, and environmental intelligence areas. BCM yielded a

satisfactory classification accuracy of 62.7 per cent (baseline was 44.1%) that the individuals


in group A were more skilled than members of group C in linguistic, spatial, bodily-

kinesthetic, interpersonal, intrapersonal, spiritual, and environmental intelligence areas.

Group C was more skilled than group A in mathematical-logical and musical intelligence

areas.

RQ 6. What are the WSC competitors' most essential self-regulatory abilities?

Based on theory, our highly selective sample, and previous interviews, we hypothesized that

the motivational level of these young manual skill workers would be exceptionally high. The

most important motivational factor was the meaningfulness of their studies (M = 4.5, SD =

.573), indicating that the young competitors believed that the WSC training would benefit

their future work careers. This result supports the current pedagogical view, which promotes

inquiry and problem-based learning (e.g., Hmelo-Silver, 2004). However, it is worth noting

that the above-mentioned methods, which are based on authentic learning tasks, expect the

students to have strong theoretical basic knowledge about the subject. The application of a

scientific inquiry through trial and error does not necessarily create a clear general view of the

subject and transferable knowledge structures.

As expected, the other motivational factors – with the exception of test anxiety (M = 2.8,

SD = .903) – were also considered important: interest in showing others (extrinsic goal

orientation, M = 4.1, SD = .773) and studying (intrinsic goal orientation, M = 4.0, SD = .606),

belief in one’s own ability (efficacy beliefs, M = 4.1, SD = .666), and effort as a contributor to

success (control beliefs, M = 4.0, SD = .628).

We applied BCM to resolve the most probable network of predictors of success in

international skills competitions (A, B, and C groups). Predictor variables in the analysis were

the six motivational dimensions: Intrinsic goal orientation, Extrinsic goal orientation,

Meaningfulness of studies, Control beliefs, Efficacy beliefs, and Test anxiety. BCM showed

with a classification accuracy of 52.5 per cent (baseline was 44.1%) that only one variable,

meaningfulness of studies, was selected into the most probable network. This finding

indicates that a positive attitude towards vocational training contributes to success in

international skills competitions.

The results showed that there were no differences in motivation or attribution factors

between different skill areas. This finding corresponds with the results of the interview study:

The essential self-regulatory characteristics (motivation, volition, self-reflection) of the

WorldSkills competitors were comparable to those of high achievers in sports (Olympic-level


athlete study, see Gould, Dieffenbach & Moffett, 2001) and science (academic Olympians

study in mathematics, physics and chemistry, see Heller & Lengfelder, 2000; Nokelainen et

al., 2007; Wu & Chen, 2001).

The most important goal orientation in the sample was mastery goal orientation (M = 4.6,

SD = .442) followed by performance-approach goal orientation (M = 4.2, SD = .666). This

result indicates that respondents understand the importance of competence development, find

learning interesting, and are focused on the task while practicing a skill. Respondents

recorded the lowest levels in performance-avoidance goal orientation (M = 3.5, SD = .683),

thus indicating avoidance of embarrassment and focusing on the self during training instead

of focusing on the task. This finding was expected in our highly selective sample of manual

workers on the basis of the existing body of research in goal orientation theory (for a review,

see Kaplan & Maehr, 2007). Using BDM to compare the goal orientations of the most (group

A) and least (group C) successful competitors, we found that group A was more performance-

approach goal oriented and less performance-avoidance oriented than was group C.

We investigated the volitional aspects of talent development using two dimensions:

perseverance and time management. In the entire sample, perseverance (M = 3.6, SD = .525)

was reportedly slightly more important than time management (M = 3.5, SD = .657). When

the most and least successful WSC competitors were compared using BCM, a higher level of

perseverance and more effective time management skills were linked to the highest

performing competitors. However, the classification accuracy of only 47.5 per cent suggests

that this finding should be interpreted with caution (baseline was 44.1%).

RQ 7. What is the influence of domain- and non-domain-specific factors on the talent

development of WSC competitors?

Our results showed that the home (M = 4.2, SD = .988) and middle school atmospheres (M =

4.6, SD = .673) were considered similarly supportive in terms of the development of

vocational talent; very few negative experiences were reported.

Analysis with a sub-sample (n = 23) comprising of the responses of both competitors and

their parents revealed that, unlike their children (Mhome = 3.2, SDhome = .647 and Mschool = 3.3,

SDschool = .780), the parents considered both atmospheres to be more supportive (M = 3.7, SD

= .714 and M = 3.5, SD = 1.015, respectively). Previous studies on academic mathematics,

physics, and chemistry Olympians found a similar pattern (Nokelainen, Tirri, Campbell &

Walberg, 2007).


The best predictors were then classified based on success in international skills

competitions (A, B and C groups). Predictor variables in the analysis were: Encouraging

home atmosphere, Stimulating influence of a particular friend, Stimulating influence of a

teacher or trainer, Seeing impressive demonstrations of skill (e.g., furniture, hairstyles),

Interest in a professional field, Desire to learn new things, Interest in finding one’s limits,

Interest in competing with others in vocational skills, Desire to succeed in skills competitions,

Desire to succeed in future working life, Employment in the future, Team spirit amongst the

WSC trainees, and Interest in the company of people sharing similar interests. BCM showed

with a classification accuracy of 67.6 per cent (baseline was 48.7%) that the best predictors

for success in international skills competitions were (in order of importance): Home

atmosphere (a non-domain specific factor), interest in a particular field of work (domain-

specific intrinsic motivation), and interest in competing with others in vocational skills

(domain-specific extrinsic motivation). All were positive predictors for competition success.

Interestingly, the least successful competitors in group C had the highest ratings for the role of

teachers or trainers in the development of their vocational talent.

Characteristics of Vocational Excellence

Based on the survey results, we constructed a model describing the characteristics and

influential external factors on the development of exceptional vocational talent (Figure 5).

The left-hand side of the figure summarizes the characteristics of the members of group C

(representing vocational expertise), and the right-hand side of the model shows comparable

information about the members of group A members (representing vocational excellence). A

weighted line indicates the importance of each characteristic or factor to the development of

vocational talent. The thin dashed line indicates the weakest form of dependency. An

indicator or external factor with no connecting line is, on the basis of research evidence,

considered to have no effect on the development of vocational talent.


Figure 5 shows the significance of the WSC trainee’s personality traits (especially natural

abilities and self-regulation) and external factors (especially support from parents, teachers,

and trainers) in the development of exceptional vocational talent. Competitors who were


unable to cultivate a strong desire to display their skills (both mastery-approach and

performance-approach goal orientations) and, in addition, had difficulty concentrating on the

task (performance-avoidance goal orientation) were unable to realize their full potential in

competitive situations. Focused mental training in these areas may improve results in the

future. The results indicate that the highest performing competitors have a higher level of

perseverance and more effective time management skills (volition) than do their lower

achieving peers. Further, competitors who rely heavily on domain-specific external support

(teachers, trainers) are most likely to underperform in controlled environments, such as

competitions.

Discussion

An individual’s characteristics – such as intelligence and abilities – stem from the interaction

between heredity and the environment. Although genes can account for individual differences

in the development of competences and expertise, it is impossible to measure their effect on

intelligence. Thinking skills are a fruit of the synergy between heredity and the environment,

including the recognition and definition of problems, creating strategies to solve those

problems, the representation of information, and the allocation of resources, as well as the

observation and evaluation of problem-solving. Sternberg (2005) notes that if we call the

above-mentioned metacomponents of thinking 'intelligence', we must still remember to

acknowledge that intelligence is a form of developing competences which determines the

level of talent development.

The current understanding of giftedness and talent clearly distinguishes the

aforementioned concepts (Gagné, 2004). Giftedness refers to natural characteristics, such as

intellectual, creative, social, and sensori-motorical skills. Talent means systematically

developed skills, such as academic skills, skills related to entrepreneurship, hobbies (music,

sports), command of social situations, and technology. This conceptual separation is hugely

significant to the present study, because while everyone presumably has vocational skills,

only a few will become vocational experts, and even fewer will achieve the level of

vocational excellence.

The starting point for our study was the assumption that top vocational talents would

differ from other skillful persons because of their higher level of domain-specific knowledge

and domain-specific skills (Ericsson, Krampe & Tesch-Römer, 1993). This approach seems


promising, especially in the eyes of vocational education providers: by investing in the quality

of teaching and learning, it is possible to train a majority of students to become vocational

experts. However, before making further conclusions, we must first address two important

prerequisites. First is the assumption that all students are equal in their natural ability to learn

new things, and second is that all the students have an equal opportunity to participate in goal-

oriented and instructed training (Chi, 2006).

The tenability of the first assumption is questionable, since even if individuals in the

same skill possess equal natural abilities, the development of their potential into

systematically developed skills is a lengthy and complex process. As discussed earlier,

Gagné’s (2004) C.GIPE illustrates how natural abilities (gifts) develop into vocational skills

(talents). In the model, chance and natural abilities, together with goal-oriented, deliberate

practice, play an essential role. Figure 1 shows that chance affects natural abilities

(intelligence, creativity, social skills, sensori-motorical skills), intrapersonal characteristics

(personal traits, self-regulation), and factors related to the environment (available hobby,

leisure, and educational opportunities). Thus, one's natural abilities determine one’s eligibility

to successfully practice a profession. For example, a plumber must have bodily-kinesthetic

abilities such as muscular strength, a programmer must have logical-mathematical thinking

abilities, and a hairdresser must have both bodily-kinesthetic and social skills. Intrapersonal

characteristics regulate both one's interest in a certain vocational skill area and one's

commitment to deliberate practice to become an expert in the field. The amount and quality of

practice carried out on one’s own time (informal) and in instructed (formal) practice

(development process) affect the level of professional field-specific knowledge and skills one

will be exposed to. Environmental factors, such as one's parents’ educational background,

family, friends, and place of residence, affect one's choice of profession.

The other assumption, that all students enjoy equal opportunity to participate in goal-

oriented and instructed training, can be fulfilled more easily than the first one. The key to

success is to encourage vocational training institutions to participate in national-level skill

competitions. This will inevitably lead teachers in participating organizations to seek higher

competence in their field (professional development) through different roles (trainers, experts)

in the process. Their knowledge of new innovations in vocational training and skill-specific

working methods would not only benefit WSC competitors and their non-participating

students in vocational institutions, but would also challenge their colleagues to update their

professional knowledge and, thus, create a more forceful transfer of knowledge. Vocational


education students and their teachers in various institutions around the world will also benefit

from new ideas and support for their professional self-esteem by visiting both national and

international competitions.

Further, we would like to emphasize the fact that all the characteristics of vocational

expertise and excellence discussed in this paper, except for natural abilities, are controllable,

at least to some extent, and, thus, are manageable through educational policies. Since the

development of vocational talent is a life-long learning process, any of the competitors in

group C may achieve the level of vocational excellence later in their work careers. The

recognition of hindering factors to talent development in the early stages of formal education

will help the future work force to fulfill its development potential. Examples of such factors

appear in this paper: 1) attributing success mainly to uncontrollable instead of controllable

factors, 2) using maladaptive instead of adaptive patterns of learning, and 3) focusing on the

self instead of focusing on the task. WSC training programs offer one viable environment to

put these research findings to use.

Limitations of the study

It is worth asking whether the WSC competitors are true representatives of top talents in their

skill areas in Finland. We believe they are for two reasons. Firstly, all the governmental

operations related to both national (Taitaja) and international (WSC) skills competitions are

professionally organized through a specialized organization, WorldSkills Finland. The

organization provides training in collaboration with universities of applied sciences for

teachers who train the competitors. Thus, most of the Finnish vocational educational

institutions acknowledge the importance of the WSC program and encourage their students to

participate in the regional competitions. This ensures that most top level talents have true

access to the WSC training program. Secondly, the selection process of the Finnish WSC

team is rigorous. Only the winners of each skill category in regional competitions are

permitted to participate in the national skill competition. Then, only the gold and silver

medalists of the national skill competition for each skill category are eligible to apply for

Team Finland membership. Initial selection of the team is based on interviews with the team

leaders as well as both manual skill and mental trainers. Members of the team are monitored

during the one-year training period, and the final composition of the team is announced about

three months prior to the international competition on the basis of their performance in the

training program.


The second limitation of this study relates to the generalizability of the small (n = 64)

survey sample, which will inevitably lead to low power (Cohen, 1988). Although we applied

Bayesian statistical techniques designed to deal with small samples, non-normal distributions,

non-linear dependencies, and missing data, it is computationally impossible to add power to

the design. Fortunately, because this study used a mixed-method design, the interviews (n =

30) provided complementary research evidence .

Future research

Future research directions regarding the characteristics of young manual skill workers should

include examination of their mindsets (Dweck, 1999; Yeager et al., 2010). Our current

understanding is that those individuals with an incremental mindset are more likely to cope

successfully with traumatic experiences (or experiences of success and failure) during

vocational education, WSC training and competitions, as well as working life than are those

with an entity mindset. Future research should also aim to address issues involving the health

(lifestyle), hobbies, safety, and media skills of WorldSkills competitors, and also focus more

on the WSC skill areas that require teamwork, such as Caring and Mecatronics.

Research has shown that it takes about 6 to 30 years to achieve the highest level of

knowledge and skills in the fields of science, art, and sports (Ericsson, 2006; Ericsson,

Krampe & Tesch-Römer, 1993). Since the maximum age limit for competitors of the

WorldSkills Competition is 23 years, those individuals who participated in our study will

have, after their basic training, spent five to seven years developing their professional skills.

These young competitors will reach, at most, a pre-stage of vocational expertise or excellence

during the competition training process. The model presented in Figure 5 fails to describe the

characteristics or development of vocational excellence after formal education and coaching,

even though it is evident that talent development is a life-long learning process (e.g., Lawler,

1994; Pazy, 2004). An important question for future studies is how young manual skill

workers will develop their talents further and succeed in working life.

All of the above-mentioned questions call for longitudinal research designs with control

groups (students in vocational institutions who have not participated in WSC training), which

will allow us to examine the long-term impact of natural abilities, intrinsic characteristics, and

extrinsic conditions (e.g., whether they have or have not participated in skills training and

competitions) on the career development of young workers. The issues discussed in this paper


should be placed into international context by constructing research designs that will allow

cross-cultural comparisons.

Acknowledgements

The author would like to thank Professor Pekka Ruohotie and the Helsinki 2005, Shizuoka

2007, and Calgary 2009 team members as well as SkillsFinland (http://www.skillsfinland.fi)

for their cooperation in this study. This study was funded by the Finnish Ministry of

Education and Culture (http://www.minedu.fi).


References

Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of

Educational Psychology, 84(3), 261-271.

Bandura, A. (1997). Self-efficacy: The exercise of control. Upper Saddle River, NJ: Prentice

Hall.

Barrow, R., & Milburn, G. (1990). A Critical Dictionary of Educational Concepts. Second

edition. New York, NY: Teachers College Press.

Bloom, B. S. (Ed.) (1985). Developing talent in young people. New York, NY: Ballantine

Books.

Campbell, J. R. (1996). Developing cross-national instruments: Using cross-national methods

and procedures. International Journal of Educational Research, 25(6), 485-496.

Chi, M. T. (2006). Two Approaches to the Study of Experts’ Characteristics. In K. A.

Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge

Handbook of Expertise and Expert Performance (pp. 21-30). Cambridge: Cambridge

University Press.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Second edition.

Hillsdale, NJ: Lawrence Erlbaum Associates.

Connell, M., Sheridan, K., & Gardner, H. (2004). Experts, generalists, and expert generalists:

On the relation between general competence and expertise in a domain. In R. Sternberg,

& E. Grigorenko (Eds.), Perspectives on the psychology of abilities, competencies, and

expertise (pp. 126-155). New York, NY: Cambridge University Press.

Corno, L. (1989). Self-regulated learning: A volitional analysis. In B. Zimmerman, & D.

Schunk (Eds.), Self-regulated Learning and Academic Achievement: Theory, Research

and Practice (pp. 111-142). New York, NY: Springer.

Corno, L. (1993). The Best-laid plans. Modern conceptions of volition and educational

research. Educational Researcher, 22(2), 14-22.

Dai, D. Y., Moon, S. M., & Feldhusen, J. F. (1998). Achievement Motivation and Gifted

Students: A Social Cognitive Perspective. Educational Psychologist, 33(2/3), 45-63.


Day, E. A., Arthur, W., & Gettman, D. (2001). Knowledge structures and the acquisition of a

complex skill. Journal of Applied Psychology, 86(5), 1022-1033.

Driscoll, M. (2005). Psychology of learning for instruction. Third edition. Toronto, ON: Allyn

& Bacon.

Dweck, C. (1999). Self theories: Their role in motivation, personality and development.

Philadelphia, PA: Psychology Press.

Elliot, A. J., & Harackiewicz, J. M. (1996). Approach and avoidance achievement goals and

intrinsic motivation: A mediational analysis. Journal of Personality and Social

Psychology, 70(3), 461-475.

Ellström, P.-E. (1994). Kompetens, lärande och utbildning i arbetslivet. Problem, begrepp

och teoretiska perspektiv. [Skills, learning, and education in working life. Problems,

concepts, and theoretical perspectives]. Stockholm: Publica.

Ellström, P.-E. (2001). The many meanings of occupational competence and qualification. In

W. J. Nijhof & J. N. Streumer (Eds.), Key Qualifications in Work and Education (pp.

39-50). Dordrecht: Kluwer Academic Publishers.

Ericsson, K. A., Krampe, R., & Tesch-Römer, C. (1993). The role of deliberate practice in the

acquisition of expert performance. Psychological Review, 100(3), 363-406.

Ericsson, K. A. (2006). The Influence of Experience and Deliberate Practice on the

Development of Superior Expert Performance. In K. A. Ericsson, N. Charness, P. J.

Feltovich, & R. R. Hoffman (Eds.), The Cambridge Handbook of Expertise and Expert

Performance (pp. 683-703). Cambridge: Cambridge University Press.

Gagné, F. (2004). Transforming gifts into talents: the DMGT as a developmental theory. High

Ability Studies, 15(2), 119-147.

Gagné, F. (2010). Motivation within the DMGT 2.0 framework. High Ability Studies, 21(2),

81-99.

Gardner, H. (1983). Frames of mind. New York, NY: Basic Books.

Gardner, H. (1993). Multiple Intelligences: The Theory in Practice. New York, NY: Basic

Books.

Gardner, H. (1999). Intelligence reframed: multiple intelligences for the 21st century. New

York, NY: Basic Books.


Gill, J. (2002). Bayesian methods. A Social and Behavioral Sciences Approach. Boca Raton:

Chapman & Hall/CRC.

Gould, D., Dieffenbach, K., & Moffett, A. (2001). Psychological talent in Olympic medal

winning athletes. US Olympic Committee Sport Science and Technology Final Grant

Report. Colorado Springs, CO.

Greenspan, D. A., Solomon, B., & Gardner, H. (2004). The development of talent in different

domains. In L. V. Shavinina & M. Ferrari (Eds.), Beyond knowledge (pp. 119-135).

Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

Heider, F. (1958). The psychology of interpersonal relationships. New York, NY: Wiley.

Heller, K., & Lengfelder, A. (2000, April). German Olympiad study on mathematics, physics

and chemistry. Paper presented at the annual meeting of American Educational

Research Association, New Orleans, USA.

Hmelo-Silver, C. E. (2004). Problem-Based Learning: What and How Do Students Learn?

Educational Psychology Review, 16(3), 235-266.

Kaplan, A., & Maehr, M. L. (2007). The Contributions and Prospects of Goal Orientation

Theory. Educational Psychology Review, 19(2), 141-184.

Kitsantas, A., & Zimmerman, B. J. (2002). Comparing self-regulatory processes among

novice, non-expert, and expert volleyball players: A microanalytic study. Journal of

Applied Sport Psychology, 14(2), 91-105.

Krippendorff, K. (2004). Content analysis: an introduction to its methodology. Second

edition. Thousand Oaks: Sage.

Lawler, E. E. (1994). From job-based to competence-based organizations. Journal of

Organizational Behaviour, 15(1), 3-15.

Midgley, C., Maehr, M. L., Hruda, L. Z., Anderman, E., Anderman, L. H., Freeman, K. E.,

Gheen, M., Kaplan, A., Kumar, R., Middleton, M. J., Nelson, J., Roeser, R., & Urdan,

T. (2000). Manual for the Patterns of Adaptive Learning Scales (PALS). Ann Arbor,

MI: University of Michigan.

Myllymäki, P., Silander, T., Tirri, H., & Uronen, P. (2002). B-Course: A Web-Based Tool for

Bayesian and Causal Data Analysis. International Journal on Artificial Intelligence

Tools, 11(3), 369-387.


Nicholls, J. G., Cheung, P. C., Lauer, J., & Patashnick, M. (1989). Individual differences in

academic motivation: Perceived ability, goals, beliefs, and values. Learning and

Individual Differences, 1(1), 63-84.

Nokelainen, P. (2008). Modeling of Professional Growth and Learning: Bayesian approach.

Acta Universitas Tamperensis 1317. Tampere: Tampere University Press.

Nokelainen, P., Silander, T., Ruohotie, P., & Tirri, H. (2007). Investigating the Number of

Non-linear and Multi-modal Relationships Between Observed Variables Measuring

Growth-oriented Atmosphere. Quality & Quantity, 41(6), 869-890.

Nokelainen, P., & Ruohotie, P. (2002). Modeling Student’s Motivational Profile for Learning

in Vocational Higher Education. In H. Niemi & P. Ruohotie (Eds.), Theoretical

Understandings for Learning in the Virtual University (pp. 177-206). Hämeenlinna:

RCVE.

Nokelainen, P., Tirri, K., & Campbell, J. R. (2004). Cross-cultural Predictors of Mathematical

Talent and Academic Productivity. High Ability Studies, 15(2), 230-242.

Nokelainen, P., Tirri, K., Campbell, J. R., & Walberg, H. (2007). Factors that Contribute or

Hinder Academic Productivity: Comparing two groups of most and least successful

Olympians. Educational Research and Evaluation, 13(6), 483-500.

Nokelainen, P., Tirri, K., & Merenti-Välimäki, H.-L. (2007). The Influence of Self-

attributions and Parental Attitude to the Development of Mathematical Talent. Gifted

Child Quarterly, 51(1), 64-81.

Pazy, A. (2004). Updating in Response to the Experience of Lacking Knowledge. Applied

Psychology: An international review, 53(3), 436-452.

Pillay, H. (1998). Adult learning in a workplace context. In P. Sutherland (Ed.), Adult

Learning: a Reader (pp. 122-136). London: Kogan Page.

Pintrich, P. R. (1994). Continuities and discontinuities: Future directions for research in

educational psychology. Educational Psychologist, 29(3), 137-148.

Pintrich, P. R., Smith, D., Garcia, T., & McKeachie, W. J. (1991). A Manual for the Use of

the Motivated Strategies for Learning Questionnaire. Technical Report 91-B-004. Ann

Arbor: University of Michigan, National Center for Research to Improve Postsecondary

Teaching and Learning.


Ruohotie, P. (2004). Self-regulatory Abilities in Professional Learning. In J. R. Campbell, K.

Tirri, P. Ruohotie, & H. Walberg (Eds.), Cross-cultural Research: Basic Issues,

Dilemmas, and Strategies (pp. 159-184). Hämeenlinna: RCVE.

Ruohotie, P., & Nokelainen, P. (2000). Beyond the Growth-oriented Atmosphere. In B.

Beairsto & P. Ruohotie (Eds.), Empowering Teachers as Lifelong Learners (pp. 147-

167). Hämeenlinna: RCVE.

Schunk, D. H., & Ertmer, P. A. (2000). Self-regulation and academic learning. In M.

Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of Self-Regulation (pp. 631-

650). San Diego, CA: Academic Press.

Snow, R. E., Corno, L., & Jackson, D. N. (1996). Individual differences in affective and

conative functions. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of Educational

Psychology (pp. 243-310). New York, NY: Simon & Schuster MacMillan.

Snow, R. E., & Farr, M. J. (Eds.) (1987). Aptitude, Learning, and Instruction: Vol. 3.

Conative and Affective Process Analyses. Hillsdale, NJ: Lawrence Erlbaum Associates.

Snow, R. E., & Jackson, D. N. (1994). Individual Differences in Conation: Selected

Constructs and Measures. In H. F. O´Neil & M. Drillings (Eds.), Motivation: Theory

and Research (pp. 71-99). Hillsdale, NJ: Lawrence Erlbaum Associates.

Sternberg, R. J. (1991). Death, taxes, and bad intelligence tests. Intelligence, 15(3), 257-269.

Sternberg, R. J. (2005). Intelligence, competence, and expertise. In A. J. Elliot & C. S. Dweck

(Eds.), Handbook of Competence and Motivation (pp. 15-30). New York, NY: The

Guilford Press.

Tirri, K., & Nokelainen, P. (2008). Identification of multiple intelligences with the Multiple

Intelligence Profiling Questionnaire III. Psychology Science Quarterly, 50(2), 206-221.

Weiner, B. (1974). Achievement motivation and attribution theory. Morristown, NJ: General

Learning Press.

Weiner, B. (1980). The role of affect in rational (attributional) approaches to human

motivation. Educational Researcher, 9(7), 4-11.

Weiner, B. (1986). An attributional theory of motivation and emotion. New York, NY:

Springer.


Weiner, B. (1992). Human motivation: Metaphors, theories and research. Newbury Park,

CA: Sage.

Weiner, B. (1994). Integrating social and personal theories of achievement striving. Review of

Educational Research, 64(4), 557-573.

Weiner, B. (2000). Intrapersonal and Interpersonal Theories of Motivation from an

Attributional Perspective. Educational Psychology Review, 12(1), 1-14.

Williams, R. S. (2002). Managing Employee Performance. London: Thomson Learning.

Wu, W., & Chen, J. (2001). A follow-up study of Taiwan physics and chemistry Olympians:

The role of environmental influences in talent development. Gifted and Talented

International, 16(1), 16-26.

Yeager, D. S., Trzesniewski, K. H., Tirri, K., Nokelainen, P., & Dweck, C. S. (2010).

Adolescents' Implicit Theories Predict Desire for Vengeance After Remembered and

Hypothetical Peer Conflicts: Correlational and Experimental Evidence. Developmental

Psychology, 47(4), 1090-1107.

Zimmerman, B. J., & Kitsantas, A. (2005). The hidden dimension of personal competence:

Self-regulated learning and practice. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of

Competence and Motivation (pp. 509-526). New York, NY: The Guilford Press.

Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning.

Journal of Educational Psychology, 89(3), 397-410.

Zimmerman, B. J. (1998). Developing self-fulfilling cycles of academic regulation: An

analysis of exemplary instructional models. In D. H. Schunk & B. J. Zimmerman

(Eds.), Self-Regulated Learning: From Teaching to Self-Reflective Practice (pp. 1-19).

New York, NY: The Guilford Press.

Zimmerman, B. J. (2000). Attaining self-regulation. A social cognitive perspective. In M.

Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-

39). San Diego, CA: Academic Press.

Zimmerman, B. J. (2006). Development and Adaptation of Expertise: The Role of Self-

Regulatory Processes and Beliefs. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R.

R. Hoffman (Eds.), The Cambridge Handbook of Expertise and Expert Performance

(pp. 705-722). Cambridge: Cambridge University Press.


Figure 1. Causal order of components in the Development of Vocational Excellence (adapted

from Gagné, 2004, p. 121).


Figure 2. Characteristics specific to WorldSkills competitors (interview data, n= 30).


Figure 3. Characteristics specific to WorldSkills competitors' during the training period, competitions, and working life (interview data, n= 30).


Figure 4. Characteristics specific to WorldSkills competitors' initial interest in the field, perseverance in acquiring a vocational skill, and mastery

of that skill (interview data, n= 30).


Figure 5. Characteristics of cocational expertise and excellence (survey data, n= 64). MAP = Mastery-approach goal orientation, PAP =

Performance-approach goal orientation, PAV = Performance-avoidance goal orientation.

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