Abstract

The Education and Learning Capital Approach (ELCA) has been widely used to investigate talent development. A research gap is the implicit consideration of the domain specificity of educational and learning capital. In an empirical study with 365 school students we investigated the domain specificity of the approach for the domains of school learning and learning to play a musical instrument. At the beginning of the school year, students filled out a version of the Questionnaire for Educational and Learning Capital (QELC) for both domains and also responded to other domain-related measures (self-efficacy, grades). Six weeks later, students filled out a learning diary for 1 week in which they reported their activities on an hourly basis and responded to questions concerning these activities. Based on the Sociotope Approach this procedure helped to identify times in which students actually practiced their musical instrument, times that students could potentially practice their musical instrument (objective action space), and times that students would be expected to practice their musical instrument (normative action space). Three hypotheses were tested and could be supported. First, the availability of educational and learning capital for school learning and learning an instrument differed. Second, a confirmatory factor analysis supported the factorial validity of the domain-specific capital measurements. Third, domain-congruent correlations were mostly higher than domain-incongruent correlations, i.e., the availability of educational and learning capital for school learning correlated more closely with variables related to school learning than with variables related to learning a musical instrument. Similarly, the availability of the capitals for learning a musical instrument correlated more closely with variables related to learning a musical instrument.

Highlights

  • Two key insights on talent development are that people can differ substantially in both the speed of skill acquisition and the level of performance achieved (VanLehn, 1996; Ericsson et al, 2006; Shavinina, 2009; Attri, 2019)

  • With the exception of cultural educational capital, students indicated that they had more educational capital for learning their musical instrument than for school learning. 2-tailed paired samples t-tests showed that the mean differences are statistically significant, economic educational capital, t(364) = 6.54, p < 0.001; didactic educational capital, t(364) = 19.35, p < 0.001; social educational capital, t(364) = 8.37, p < 0.001; infrastructural educational capital, t(364) = 10.22, TABLE 2 | Descriptive statistics, Cronbach’s alpha of Educational Capital (EC) and Learning Capital (LC) scales, and paired t-test results

  • This had been derived from the observation that clusters are observed on many levels of analysis (Ziegler and Baker, 2013). It was based on research studies which demonstrated the role of learning resources for talent development in general, and educational and learning capital in particular (Vladut et al, 2013, 2015; Paz-Baruch, 2015, 2020; Phillipson et al, 2017; Stoeger et al, 2017b; Vialle, 2017; Lafferty et al, 2020)

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Summary

Introduction

Two key insights on talent development are that people can differ substantially in both the speed of skill acquisition and the level of performance achieved (VanLehn, 1996; Ericsson et al, 2006; Shavinina, 2009; Attri, 2019). There has been a strong tendency in talent and giftedness research to explain these phenomena with domain-general concepts such as talents, Domain-Specificity of Educational and Learning Capital gifts, and IQ (Galton, 1883; Terman, 1925, 1954; Hollingworth, 1942; Howe et al, 1998). Gardner’s conception of multiple intelligences exerted a great influence. He postulated seven and later even more domain-specific intelligences (Gardner, 1983, 1986; Gardner and Moran, 2006). Other researchers like Tannenbaum (1986), Gagné (1993), and Heller et al (2005) or Subotnik et al (2011) postulated specific abilities, but rather specified and included domains in their models of giftedness and talent development. Heller et al (2005) mentioned mathematics, natural sciences, technology, computer science, art, languages, sports, and social relationships as domains

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