The Influence of Social Background on Participation in Adult Education

Abstract

In this article, we address the issue of participation in adult education building on the cultural capital framework. This theoretical framework suggests that (educational) practices are affected by one’s social background and, more precisely, by the cultural resources handed down in the family context. To examine the validity of this theoretical framework, we build on data from the Programme for the International Assessment of Adult Competencies from 23 countries (n = 120,789). The Programme data allow using the variables parents’ educational level (a proxy for social background), educational attainment, and readiness to learn as precursors of participation in adult education (both a proxy for cultural capital). Our findings suggest that the cultural capital framework is not fully suited to explain participation in adult education: Although social background has an (indirect) influence on participation, its effect does not concur with theoretical predictions, that is, mediated by the readiness to learn.

Keywords

participation in adult education social background cultural capital cultural reproduction cultural mobility PIAAC

International comparative research reveals recurrent patterns in participation in adult education. In particular, high-qualified, employed, and younger adults are more likely to participate (Desjardins, Rubenson, & Milana, 2006; Hefler et al., 2011). Since adult education is considered a key condition for individual and society development (Commission of the European Communities, 2000; Organisation for Economic Co-operation and Development [OECD], 1996; UNESCO, 1996), socially biased participation in adult education results in unequal allocation of related benefits in the population (e.g., income, productivity, health, life satisfaction, and social and civic engagement; Ferrer & Riddell, 2011; Schuller & Desjardins, 2011). Adult education, consequently, potentially enhances social disparities instead of reducing them.

In this study, we investigate the inequality in participation in adult education through the lens of cultural capital (Bourdieu, 1984; Bourdieu & Passeron, 1990). This framework is interesting since it relates social practices to socioeconomic and sociocultural backgrounds. In fact, the theory explains differences in current and future practices as the outcome of socially determined differences in dispositions (e.g., readiness to learn) handed down in the family context. In other words, the cultural capital framework is an evocative synthesis of individual agency and structural determinism.

Theoretical Background

In social research, the work of the French sociologist Pierre Bourdieu (1984; Bourdieu & Passeron, 1990) has generated considerable interest (and controversy) regarding the relationship between education, social stratification, and social reproduction (Goldthorpe, 2007; Kingston, 2001; Lamont & Lareau, 1988). His work is part of the larger tradition of social stratification research, that is, research on social inequality in modern society. Broadly speaking, this research tradition rests on two major tenets: first, the social stratification tenet investigating the division of society in social strata, and second, the social reproduction tenet investigating the persistence of social stratification. Bourdieu’s theory captures both tenets.

First, Bourdieu (1984, 1997; Bourdieu & Passeron, 1990) upholds the idea that society is socially stratified, and that strata can be distinguished based on possession of resources. Interestingly, Bourdieu’s ideas about social stratification are not uniquely inspired by economic determinism. In fact, Bourdieu argues that these resources are not only economic but can equally be cultural and social in nature. The possession of these combined resources determines social positions. Bourdieu, consequently, broadens the classical class dichotomy between proletariat/bourgeoisie, based on the possession of economic resources, to a more diversified and complex social field of “classes” and “class fractions,” based on the possession of primarily economic and cultural resources (Bourdieu, 1984). In this respect, Bourdieu states that classes can be distinguished by the “volume” of their capital (i.e., resources)—more as opposed to less capital—and class fractions can be distinguished by the “composition” of their capital—more economic than cultural capital and vice versa.

Economic resources (or “economic capital”) are resources “immediately and directly convertible into money and may be institutionalized in the form of property rights” (Bourdieu, 1997, p. 47). Cultural resources (or “cultural capital”) present themselves in three forms: an embodied state, an objectified state, and an institutionalized state (Bourdieu, 1997). In the embodied state, cultural capital refers to what Bourdieu has equally called the habitus, the dispositions (e.g., attitudes, intentions, expectations, beliefs, etc.) orientating one’s practices (Bourdieu & Wacquant, 1992). In the objectified state, cultural capital presents itself as cultural goods, such as books, paintings, or musical instruments. Finally, in the institutionalized state, cultural capital refers to cultural competence that has been officially sanctioned, such as educational qualifications.

Second, Bourdieu maintains that social positions are hereditary: They are passed down from parents to children. Consequently, the socially stratified nature of society is reproduced. But social reproduction is a largely hidden process and is strongly linked to the appropriation and transmission of cultural capital (Bourdieu, 1973; Bourdieu, & Passeron, 1990), that is, cultural reproduction. Although he does not dispute that social reproduction still operates through “classical” transfer of economic capital (e.g., inheritance), this process has lost part of its legitimacy. Instead, Bourdieu states that social reproduction in modern society is achieved and legitimated through cultural reproduction, and that this is a rather concealed form of transmission of economic capital. Both the family and the educational system are critical agents in this process. The family is the primary locus for cultural reproduction, in particular as the site of both conscious and unconscious cultivation of embodied cultural capital, that is, a system of dispositions, values, expectations, and so on. Through conscious and unconscious socialization, children and adolescents acquire dispositions that are class-specific. The educational system, as a secondary agent, selectively enforces cultural capital as acquired in the family. In fact, Bourdieu argues that the school reenforces the dominant culture in society, that is, the culture of the dominant—economic—class. This puts students from the dominant class in an advantageous position. By virtue of the acquired cultural capital in the family, the embodied cultural capital, these students possess the capital that makes them more likely to succeed in their academic career. Consequently, they acquire educational qualifications that unlock access to better labor market positions. In other words, they can convert their cultural capital into economic capital at a higher return rate. Furthermore, Bourdieu argues that this process is disguised. More precisely, embodied cultural capital is often not recognized as “capital,” as a resource that is acquired, but rather is depicted as an ability, something that is possessed. Therefore, the educational outcomes can be presented as the outcome of a “natural” selection/competition process, based primarily on intellectual ability, instead of the outcome of a “social” selection process based on social heritage. In sum, the cultural reproduction hypothesis (Bourdieu & Passeron, 1990) holds that the influence of social background on (educational) practices is operated through cultural capital, primarily the cultural capital acquired within the familial context, the embodied cultural capital. Consequently, it is hypothesized that (1) embodied cultural capital is a determinant of educational practices (e.g., participation in adult education) and that (2) embodied cultural capital is determined by one’s social background (e.g., parent’s educational level). More in particular, the effects of cultural capital are highest for those who, through early socialization in the family, have acquired the (embodied) cultural capital that predisposes and attunes them to the dominant discourse in educational contexts. Alternatively, DiMaggio (1982) proposed that the impact of cultural capital on educational practices is rather independent from the social background, thus putting forward the cultural mobility hypothesis. Cultural capital (and especially embodied cultural capital) is not considered to be solely determined by the family background (cf. Proposition 2 above) but is a malleable resource only partially determined by one’s background. The cultural mobility hypothesis challenges the social reproduction hypothesis of Bourdieu. DiMaggio still acknowledges the importance of cultural capital in determining opportunities (cf. Proposition 1 above), but he challenges the idea that its possession and its acquisition are imposed by social background. Rather, investments in cultural capital, such as the decision to participate in adult education, result from individual agency, and more precisely the individual cultural capital stock (e.g., educational attainment).

Turning our attention to the subject of participation in adult education, the influence of social background on participation has been addressed earlier in the literature, although not always explicitly within the cultural capital framework. Nonetheless, some authors have studied the direct or indirect influence of social background on participation in adult education (see, e.g., Baert, De Rick, & Van Valckenborgh, 2006; Cookson, 1986; Cross, 1981; Darkenwald & Merriam, 1982; Rubenson, 1977). Common to these models is that they perceive participation in adult education as the outcome of the dispositions toward it (e.g., attitudes, intentions, expectations, perceived need), and often regard social background as affecting these dispositions in different ways. The advantage of the cultural capital framework is that it provides an integrated framework to model the influence of social background on participation in adult education, considering personal dispositions to do so (i.e., embodied cultural capital). Therefore, it allows formulating specific expectations regarding the role of social background in participation in adult education, and it reinterprets inequalities in participation as the outcome of socially determined differences in cultural capital.

Research Model

Figure 1 depicts the model representing the hypothetical influence of social background on participation in adult education. The model draws on both the cultural capital framework (cf. supra) and empirical findings (cf. infra). The objective of the model is to predict participation in adult education using a selective number of cultural capital indicators. More in particular, we employ three distinct variables to measure cultural capital: (1) parental education, a measure for parental (institutionalized) cultural capital and a proxy for social background; (2) educational attainment, a measure for actual (institutionalized) cultural capital; and (3) readiness to learn, a proxy for embodied cultural capital. Their combined effects are expected to affect participation in adult education.

Figure 1.

Hypothetical path model representing the causal link between cultural capital indicators and participation in adult education.

The first predictor, parental education, is essential in our model since it specifies the effect of social background on participation. Cultural capital theory asserts that the influence of social background on practices will primarily be the effect of cultural capital (sociocultural origin) rather than economic capital (socioeconomic origin) in the family. Several studies confirm the precedence of sociocultural origin on socioeconomic origin (e.g., De Graaf, De Graaf, & Kraaykamp, 2000; Kraaykamp & van Eijck, 2010; Sullivan, 2001). The cultural capital theory asserts that higher levels of cultural capital received from the family will have a positive influence on practices, that is, participation in adult education. But the theory asserts at the same time that this influence will be indirect because of the impact it has on embodied cultural capital. Indeed, for example, in their study using data from the International Adult Literacy Survey, Boudard and Rubenson (2003), found higher levels of parental education to—positively and indirectly—influence participation in adult education. However, in their model, Boudard and Rubenson envisioned this indirect impact to be mediated primarily by one’s own educational attainment. In our model, based on the cultural capital framework, we argue that the indirect effect is maintained through one’s own educational attainment (institutionalized cultural capital) and readiness to learn (embodied cultural capital).

The second important predictor in our model is a person’s own educational attainment. This variable captures a different aspect of the cultural capital framework, more precisely the actual cultural capital that can be immediately mobilized. In adult education, the strong link between educational attainment and participation is well-known (Houtkoop & Van der Kamp, 1992). Not only do certain adult learning programs demand educational degrees for admission but studies also observed unequal participation rates among high- and low-educated people, with highly educated people participating more (e.g., Desjardins et al., 2006; Saar, Ure, & Holford, 2013). Moreover, according to cultural capital theory, educational outcomes or educational attainment are strongly linked to sociocultural origins, and Boudard and Rubenson’s (2003) study confirms this viewpoint. Furthermore, in their exploratory study, Cincinnato, De Wever, and Valcke (2014) observed unequal participation rates between low- and high-qualified adults to be linked to differences in (familial) cultural capital.

Our final variable predicting participation in adult education is readiness to learn, as a proxy of the embodied cultural capital. According to cultural capital theory, this is a mediating variable for (educational) practices. As explained above, the cultural reproduction and cultural mobility tenet present different conceptions of which kind of cultural capital actually mediates practices. From a cultural reproduction perspective, embodied cultural capital will mediate the influence of sociocultural background (familial cultural capital). The cultural mobility hypothesis expects embodied cultural capital to mediate the influence of one’s own cultural capital. The fact that embodied cultural capital is a relevant mediating variable in educational practices has been confirmed by several studies (e.g., Dumais, 2002; Edgerton, Roberts, & Peter, 2013; Gaddis, 2013). Also, other authors observed differences in embodied cultural capital to be a possible explanation for unequal participation rates between low- and high-qualified adults (see, e.g., Cincinnato et al., 2014; White, 2012).

Aims of the Present Study

The main aim of this study is to validate the cultural capital theory when explaining participation in adult education. More in particular, we expect cultural capital acquired within the family to affect the decision whether or not to participate in adult education. Thus, our main research question is as follows:

Research Question 1: Can differences in participation in adult education be explained by differences in social background, and more precisely by differences in familial cultural capital?

However, the cultural reproduction and cultural mobility hypotheses allow for alternative interpretations. On one hand, cultural reproduction theory conjectures that the familial cultural background will rigidly affect practices. As a matter of fact, one’s sociocultural background determines embodied cultural capital from an early age on. Furthermore, this embodied cultural capital will remain more or less stable throughout life. Cultural mobility theory, on the other hand, still acknowledges sociocultural background as an important influence for practices but it allows for more agency on behalf of the individual. More precisely, familial cultural capital will positively affect investments in cultural capital but not in a deterministic way. In other words, cultural mobility theory holds that embodied cultural capital is not determined by the sociocultural background. Instead, familial cultural capital will facilitate the further acquisition of cultural capital, which in turn will affect the acquisition of certain dispositions. Thus, we further investigate following research questions:

Research Question 2: Does embodied cultural capital have a significant impact on participation in adult education?

Research Question 3: Is embodied cultural capital primarily determined by familial cultural capital, that is, in line with cultural reproduction, or rather determined by one’s own cultural capital, that is, in line with cultural mobility?

Research Question 4: To what extent does embodied cultural capital mediate the impact of one’s own and familial cultural capital on participation in adult education?

Methodology

Secondary Analysis of PIAAC Research Data

To study the research questions, we build on the, the Programme for the International Assessment of Adult Competencies (PIAAC) data. PIAAC is an OECD-coordinated, internationally comparative performance indicator study to directly measure people’s skills in literacy, numeracy, and problem solving in technology-rich environments (OECD, 2013a). Approximately 155,000 adults, aged 16 to 65 years, were interviewed in 23 countries and subnational regions between August 1, 2011, and March 31, 2012. An international consortium, led by the Educational Testing Service, in collaboration with the (sub-)national research teams, was responsible for the administration of a survey (OECD, 2014). Data in PIAAC have been calibrated, weighted, and corrected for nonresponse bias to make the data representative for its respective country (OECD, 2013b). This entails that PIAAC reflects a complex survey design that needs to be accounted for when performing subsequent data analysis.

In addition, the PIAAC survey studied—next to the mastery of literacy performance indicators—an extensive set of background variables. The resulting data set contains a variety of information, such as adults’ engagement in (formal and nonformal) education, measurements on dispositions (e.g., readiness to learn, social trust, political efficacy), socioeconomic status (e.g., employment status, occupational status), and cultural capital (e.g., parental education level, own educational attainment).

Data Analysis

The analysis is conducted using the structural equation modeling (SEM) method (cf. Bollen, 1989; Kline, 2005). SEM is an interesting technique because it enables us to model causal relationships between both latent and observed variables. SEM helps verifying the empirical grounds of the hypothesized model (Figure 1), and consequently formulates answers to our research questions. However, we would like to stress that prove of validity of a model in SEM does not necessarily equate with prove of its causality. Usually, causality is claimed on grounds of the research design. In our model, the causality is rather implied based on theoretical arguments. We conduct three related analyses to examine the above research questions.

In the first analysis (Analysis 1), we examine the overall validity of the hypothesized model, building on critical goodness-of-fit indices. In general, next to a model chi-square statistic (χ²), SEM software calculates a number of fit indices—such as the comparative fit index, the root mean square error of approximation, and the standardized root mean square residual—reflecting the goodness of fit of the model (Hu & Bentler, 1999; Kline, 2005). However, due to the complex survey design and nature of the data in PIAAC, SEM software calculates only the weighted root mean square residual (WRMR), which is a badness-of-fit index. Values ≥0.90 tend to indicate worse fit. WRMR is an experimental measure that should be interpreted in conjunction with other fit indices (Yu, 2002). Since the software does not calculate additional fit indices, we interpret the WRMR in conjunction with the proportion of explained variance in participation in adult education (R²) to evaluate the explanatory power of our model (Research Question 1).

Goodness-of-fit statistics do not give information about the significance of the relationships between predictors and outcome variables. Therefore, in the second part of the analysis (Analysis 2), we investigate the effects of the predictors on each other and on participation in adult education—regression coefficients. SEM software helps report direct effects and their significance level—testing for p < .05, p < .01, and p < .001 significance levels. These parameters can be used to examine how the different predictors in our model are related to one another and to participation in adult education in particular. Furthermore, the magnitude of predictor effects can be compared by assessing the standardized regression coefficients. Therefore, when carrying out Analysis 2, we will investigate not only whether dispositions toward learning have a significant impact on participation in adult education but also whether these dispositions are primarily determined by familial cultural capital or one’s own cultural capital (Research Questions 2 and 3).

In the final part of the analysis (Analysis 3), we examine whether embodied cultural capital mediates the impact of both familial and one’s own cultural capital (Research Question 4). In fact, a structural equation model implies not only direct effects but also indirect effects (e.g., the effect of familial cultural capital on embodied cultural capital mediated by one’s own cultural capital). The sum of the direct and indirect effects is the total effect (Kline, 2005). Through the indirect effects, we can assess to which extent embodied cultural capital mediates the impact of the familial and one’s own cultural capital. This can be done by comparing the indirect effect with the total effect.

The analyses are performed using Mplus (Muthén & Muthén, 2012). Because the estimated model builds on both categorical and nonnormally distributed variables (cf. infra), the model is estimated using a robust weighted least squares estimator using a diagonal weight matrix (weighted least squares mean and variance). Furthermore, the primary outcome variable, that is, participation in adult education, is estimated as a probability using probit regression. To reflect the complex survey design of PIAAC, the model was estimated using the sample weights. We adopted listwise deletion of observations with missing values on one or several of the variables in the model.

Research Variables in the PIAAC Data Set

First, the primary dependent/endogenous variable in our model is participation in adult education. The PIAAC survey questioned respondents about their participation in both formal and nonformal education (e.g., participation in open or distance education, on-the-job training, private lessons) during the 12 months preceding the survey. We confine our analysis to the population aged 25 to 65 years. We exclude the population aged 16 to 24 years because we want our results to reflect the trends for people no longer enrolled in compulsory education. Note, however, that this exclusion also excludes young adults with an educational qualification not higher than secondary, for example, young adults for whom higher education may be unaffordable or whose parents may have low educational attainment. Results should thus be interpreted accordingly. This variable is of the nominal type with two categories, participation (1) and nonparticipation (0). Second, familial cultural capital is measured by the highest qualification level of the parents. This is an ordinal variable with three levels: lower secondary education or less—International Standard Classification of Education (ISCED) 1997 1, 2, and 3C (short); higher secondary education—ISCED 1997 3A, 3B, 3C (long), and 4; and tertiary education—ISCED 1997 5 and 6.¹ Since SEM does not allow for exogenous variables to be categorical, we enter it in the model as an integer variable. Third, one’s own cultural capital is measured by educational attainment. Since educational attainment is measured in years necessary to obtain the highest educational qualification, it is entered as an integer variable. Fourth, disposition toward learning or readiness to learn is a entered as a scale with higher values indicating a higher readiness to learn. More precisely, we used a predefined OECD scale that summarizes a total of six 5-point scaled items to a single score using item response theory (Cronbach’s α = .84; minimum = −1 and maximum = 5; OECD, 2013b, Chapter 20).²

Results

In Table 1, we summarize descriptive statistics of the measures entered in the structural equation model. The correlation matrix can be found in Appendix A. First, the descriptive statistics clearly show that participation in adult education varies considerably by country, with Finland and Denmark attaining participation rates of 66% and Russia 20%. Also in educational attainment, we can observe considerable differences, up to 4 years of difference—10.63 years in Italy and 14.65 years in Ireland. In most countries, the average educational attainment is 12 or 13 years of education. Third, parents’ average educational attainment is mostly between less than higher secondary and higher secondary, and lowest in Italy (1.27) and highest in Germany (2.19). Finally, most countries score average on the readiness to learn scale, with Korea scoring lowest (1.20) and Finland scoring highest (2.37).

Table 1.

Descriptive Sample Statistics, by Country.

Country	N	Parents’ education		Educational attainment		Readiness to learn		Participation
Country	N	M	SD	M	SD	M	SD	Threshold^a	Proportion^b
Austria	4,105	1.86	0.69	12.09	2.63	1.76	0.94	.05	.48
Belgium (Flanders)	3,954	1.76	0.78	12.63	2.90	1.76	0.91	.03	.49
Canada^c	21,168	2.05	0.79	13.63	2.67	2.32	1.02	−.22	.59
Cyprus, Republic of	3,720	1.46	0.72	12.50	3.28	2.08	0.95	.31	.38
Czech Republic	4,559	2.00	0.51	13.29	2.60	1.92	0.93	.05	.48
Denmark	6,183	1.94	0.79	12.95	2.69	2.29	0.96	−.40	.66
Estonia	6,108	1.99	0.79	12.35	2.69	1.78	0.87	−.07	.53
Finland	4,535	1.68	0.73	12.61	3.11	2.37	0.82	−.40	.66
France	5,376	1.62	0.73	11.40	3.70	2.07	0.86	.35	.36
Germany	4,181	2.19	0.64	13.21	2.74	1.89	0.83	−.08	.53
Great Britain^d	6,116	1.94	0.73	13.25	2.30	2.18	1.03	−.25	.60
Ireland	5,084	1.62	0.77	14.65	3.34	2.09	0.97	−.02	.51
Italy	4,042	1.27	0.54	10.63	4.01	2.09	0.97	.69	.24
Japan	4,211	2.01	0.75	13.25	2.34	1.25	0.80	.18	.43
Korea	5,412	1.54	0.73	12.97	3.23	1.20	1.02	−.02	.51
Netherlands	4,136	1.68	0.81	13.49	2.66	1.74	0.92	−.36	.64
Norway	3,965	1.99	0.78	14.40	2.51	2.16	0.85	−.34	.63
Poland	4,843	1.79	0.63	12.79	3.08	1.97	1.01	.38	.35
Russia	2,583	1.96	0.74	13.67	3.35	1.71	1.10	.85	.20
Slovak Republic	4,516	1.78	0.62	13.26	2.78	1.97	1.23	.44	.33
Spain	4,890	1.36	0.68	11.40	3.65	2.18	0.99	.10	.46
Sweden	3,576	1.84	0.87	12.36	2.56	2.24	0.91	−.39	.65
United States	3,526	2.15	0.73	13.71	3.01	2.45	1.10	−.24	.59

The threshold is the cutoff value on a latent outcome variable at which an individual observation shifts from the value 0 to the value 1 on a binary outcome variable—that is, the cutoff between nonparticipation (0) and participation (1). ^bProportion of adults aged 25 to 65 years that participated in adult education. ^cThe large Canadian sample is due to deliberate oversampling. ^dIn Great Britain, only England and Northern Ireland participated in the Programme for the International Assessment of Adult Competencies survey.

Analysis 1: Explanatory Power of the Research Model (Research Question 1)

The model fit index (Table 2) shows a rather ambiguous picture. It ranges from 0.47 (Czech Republic) to 3.40 (Canada), with an average of 1.36. As mentioned earlier, values ≥0.90 tend to indicate worse fit. Only six countries meet this criterion of not exceeding 0.90, that is, Cyprus, the Czech Republic, Ireland, Italy, Russia, and Spain, implying a weak model fit in the other countries. However, since the WRMR is an experimental measure and since we cannot compare it to other fit indices (cf. supra), this should not necessarily be taken as proof of the invalidity of our model. It does imply, however, that we should be cautious when interpreting the results. Additionally, we summarize the proportion of explained variance in participation in adult education (pseudo R²). It ranges from 10% (Russia) to 32% (Poland), with an average of 20% explained variance. These results indicate that the cultural capital framework has rather limited power in explaining differences in participation in adult education.

Table 2.

Model Fit Statistics, by Country.

Country	WRMR	R ^2a
Austria	1.08	.18
Belgium (Flanders)	1.45	.22
Canada	3.40	.16
Cyprus, Republic of	0.82	.26
Czech Republic	0.47	.14
Denmark	1.23	.16
Estonia	2.37	.25
Finland	1.87	.24
France	1.06	.14
Germany	1.30	.17
Great Britain^b	1.50	.14
Ireland	0.80	.20
Italy	0.60	.25
Japan	0.93	.16
Korea	0.99	.24
Netherlands	1.39	.18
Norway	1.90	.14
Poland	2.10	.32
Russia	0.55	.10
Slovak Republic	1.67	.22
Spain	0.86	.25
Sweden	1.28	.17
United States	1.68	.25
Average	1.36	.20

Note. WRMR = weighted root mean square residual.

Explained variance of the participation in adult education, building on parent’s education, educational attainment, and readiness to learn. ^bIn Great Britain, only England and Northern Ireland participated in the Programme for the International Assessment of Adult Competencies survey.

Analysis 2: Cultural Reproduction or Cultural Mobility (Research Questions 2 and 3)?

An important aim of this study was to ascertain whether readiness to learn has a significant influence on participation in adult education in order to test the value of the cultural reproduction or mobility hypothesis. The results in Table 3 clearly indicate that readiness to learn has a significant positive impact on participation in adult education, with bs ranging from .10 in Ireland and the Slovak Republic to .29 in Estonia. Consequently, people more positively disposed to learning are also more likely to participate in adult education. However, the effect is not strong. Based on the standardized regression coefficients (β), we can observe the effect of readiness to learn to be (rather) small—ranging from .09 in France to .25 in Estonia. Therefore, the significance of our results should be put into perspective: Although readiness to learn affects participation in adult education, the impact is rather limited.

Table 3.

Parameters Estimates (Unstandardized and Standardized) and Standard Errors of Path Analysis, by Country.

Country	Causal variables	Endogenous variables
		Educational attainment			Readiness to learn			Participation
		B	SE	β	B	SE	β	B	SE	β
Austria	Parents’ education	1.43***	0.05	.38	0.16***	0.02	.12	—	—	—
	Educational attainment	—	—	—	.09***	.01	.24	0.12***	0.01	.32
	Readiness to learn	—	—	—	—	—	—	0.21***	0.02	.20
	R ²	.14			.09			.18
Belgium	Parents’ education	1.68***	0.06	.45	0.13***	0.02	.11	—	—	—
	Educational attainment	—	—	—	0.09***	0.01	.27	0.14***	0.01	.41
	Readiness to learn	—	—	—	—	—	—	0.16***	0.03	.14
	R ²	.21			.11			.22
Canada	Parents’ education	1.25***	0.03	.37	0.11***	0.02	.09	—	—	—
	Educational attainment	—	—	—	0.07***	0.01	.18	0.13***	0.01	.35
	Readiness to learn	—	—	—	—	—	—	0.13***	0.02	.14
	R ²	.14			.05			.16
Cyprus	Parents’ education	2.03***	0.06	.44	0.05	0.03	.04	—	—	—
	Educational attainment	—	—	—	0.08***	0.01	.28	0.15***	0.01	.46
	Readiness to learn	—	—	—	—	—	—	0.13***	0.02	.12
	R ²	.20			.09			.26
Czech Republic	Parents’ education	1.93***	0.10	.38	0.15***	0.04	.08	—	—	—
	Educational attainment	—	—	—	0.08***	0.01	.23	0.13***	0.01	.32
	Readiness to learn	—	—	—	—	—	—	0.14***	0.03	.13
	R ²	.14			.07			.14
Denmark	Parents’ education	1.20***	0.04	.36	0.10***	0.02	.08	—	—	—
	Educational attainment	—	—	—	0.09***	0.01	.25	0.14***	0.01	.35
	Readiness to learn	—	—	—	—	—	—	0.12***	0.02	.12
	R ²	.13			.08			.16
Estonia	Parents’ education	1.16***	0.04	.34	0.22***	0.02	.20	—	—	—
	Educational attainment	—	—	—	0.08***	0.00	.26	0.14***	0.01	.36
	Readiness to learn	—	—	—	—	—	—	0.29***	0.02	.25
	R ²	.12			.14			.25
Finland	Parents’ education	1.51***	0.06	.35	0.06***	0.02	.05	—	—	—
	Educational attainment	—	—	—	0.05***	0.01	.19	0.14***	0.01	.44
	Readiness to learn	—	—	—	—	—	—	0.19***	0.02	.16
	R ²	.12			.05			.24
France	Parents’ education	2.28***	0.05	.45	0.10***	0.02	.09	—	—	—
	Educational attainment	—	—	—	0.05***	0.00	.23	0.10***	0.00	.38
	Readiness to learn	—	—	—	—	—	—	0.12***	0.02	.10
	R ²	.20			.08			.17
Germany	Parents’ education	1.73***	0.08	.41	0.16***	0.02	.12	—	—	—
	Educational attainment	—	—	—	0.07***	0.01	.22	0.13***	0.01	.35
	Readiness to learn	—	—	—				0.18***	0.03	.15
	R ²	.17			.09			.17
Great Britain^a	Parents’ education	1.22***	0.06	.42	0.14***	0.03	.10	—	—	—
	Educational attainment	—	—	—	0.10***	0.01	.22	0.15***	0.02	.32
	Readiness to learn	—	—	—	—	—	—	0.12***	0.03	.12
	R ²	.17			.08			.14
Ireland	Parents’ education	1.84***	0.06	.43	0.06**	0.02	.05	—	—	—
	Educational attainment	—	—	—	0.07***	0.01	.23	0.13***	0.01	.41
	Readiness to learn	—	—	—	—	—	—	0.10	0.02	.09
	R ²	.18			.06			.20
Italy	Parents’ education	3.69***	0.10	.50	0.09	0.04	.05	—	—	—
	Educational attainment	—	—	—	0.08***	0.00	.33	0.11***	0.01	.44
	Readiness to learn	—	—	—	—	—	—	0.14***	0.03	.13
	R ²	.25			.13			.25
Japan	Parents’ education	1.22***	0.05	.39	0.09***	0.02	.08	—	—	—
	Educational attainment	—	—	—	0.07	0.01	.20	0.13***	0.01	.30
	Readiness to learn	—	—	—	—	—	—	0.27***	0.03	.21
	R ²	.15			.06			.16
Korea	Parents’ education	1.76***	0.05	.40	0.14***	0.02	.10	—	—	—
	Educational attainment	—	—	—	0.09***	0.01	.29	0.12***	0.01	.39
	Readiness to learn	—	—	—	—	—	—	0.20***	0.02	.20
	R ²	.16			.12			.24
Netherlands	Parents’ education	1.24***	0.05	.37	0.17***	0.02	.15	—	—	—
	Educational attainment	—	—	—	0.10***	0.01	.28	0.13***	0.01	.33
	Readiness to learn	—	—	—	—	—	—	0.20***	0.02	.19
	R ²	.14			.13			.18
Norway	Parents’ education	1.15***	0.05	.36	0.14***	0.02	.13	—	—	—
	Educational attainment	—	—	—	0.06***	0.01	.17	0.14***	0.01	.34
	Readiness to learn	—	—	—	—	—	—	0.12***	0.03	.10
	R ²
Poland	Parents’ education	2.47***	0.07	.51	0.20***	0.03	.12	—	—	—
	Educational attainment	—	—	—	0.10***	0.01	.32	0.16***	0.01	.48
	Readiness to learn	—	—	—	—	—	—	0.18***	0.02	.17
	R ²	.26			.15			.32
Russia	Parents’ education	1.76***	0.16	.39	0.06	0.05	.04	—	—	—
	Educational attainment	—	—	—	0.11***	0.01	.32	0.07***	0.01	.24
	Readiness to learn	—	—	—	—	—	—	0.13***	0.03	.14
	R ²	.15			.12			.10
Slovak Republic	Parents’ education	2.28***	0.07	.51	0.25***	0.03	.13	—	—	—
	Educational attainment	—	—	—	0.14***	0.01	.31	0.15***	0.01	.41
	Readiness to learn	—	—	—	—	—	—	0.10***	0.02	.12
	R ²	.26			.15			.22
Spain	Parents’ education	2.19***	0.08	.41	0.07***	0.02	.05	—	—	—
	Educational attainment	—	—	—	0.08***	0.00	.29	0.13***	0.01	.45
	Readiness to learn	—	—	—	—	—	—	0.13***	0.02	.13
	R ²	.17			.10			.25
Sweden	Parents’ education	1.05***	0.04	.36	0.16***	0.02	.15	—	—	—
	Educational attainment	—	—	—	0.05***	0.01	.14	0.13***	0.01	.34
	Readiness to learn	—	—	—	—	—	—	0.20***	0.03	.18
	R ²	.13			.06			.17
United States	Parents’ education	1.94***	0.07	.47	0.08*	0.03	.05	—	—	—
	Educational attainment	—	—	—	0.09***	0.01	.26	0.15***	0.01	.45
	Readiness to learn	—	—	—	—	—	—	0.11***	0.02	.12
	R ²	.22			.08			.25

In Great Britain, only England and Northern Ireland participated in the Programme for the International Assessment of Adult Competencies survey.

p < .05. **p < .01. ***p < .001.

Second, this study aims to determine whether the influence social background on participation in adult education is more in line with the cultural reproduction or with the cultural mobility thesis. To verify this issue, one needs to assess the standardized regression coefficients that summarize the effect of parents’ education and one’s own educational attainment on readiness to learn. We can discern the same pattern across the different nations, namely, that readiness to learn is rather affected by one’s own educational attainment than by parents’ education. In some countries, that is, Cyprus, Italy, and Russia, parents’ education does not significantly affect readiness to learn at all. In this respect, the results appear to confirm the cultural mobility thesis. Nevertheless, we should be cautious when interpreting these results. In fact, if we assess the explained variance in readiness to learn, ranging from 5% in Canada and Finland to 15% in Poland and the Slovak Republic, we must conclude that both parents’ education and one’s own educational attainment can only marginally explain differences in readiness to learn. However, this can be interpreted as further evidence for the cultural mobility thesis, especially since the (direct) effect of educational attainment on participation in adult education is considerable—with β’s ranging from .24 in Russia to .48 in Poland. In sum, embodied cultural capital, that is, readiness to learn, is more malleable than Bourdieu considers it to be, because it is rather determined by one’s own cultural capital, that is, educational attainment, than by parental educational capital—parents’ education. However, it should be taken into account that embodied cultural capital is only marginally determined by both other types of cultural capital.

Analysis 3: Readiness to Learn as a Mediator (Research Question 4)?

In the final analysis, we focus on the question whether readiness to learn mediates the effect of parents’ education and one’s own educational attainment on participation in adult education. The results of this analysis are straightforward (Appendix B). First, the effect of parents’ education is clearly more mediated by one’s own educational attainment than by readiness to learn. More precisely, 63% (Estonia) to 93% (Ireland) of the total effect of parents’ education on participation in adult education is mediated by a person’s own educational attainment. Second, 84% (Estonia and Russia) to 95% (France, Ireland, and Norway) of the total effect of educational attainment on participation in adult education is a direct effect, not mediated by readiness to learn. In sum, we have to conclude that embodied cultural capital, that is, readiness to learn, only secondarily mediates the influence of social background, that is, parents’ education, on participation in adult education. Stated differently, differences in dispositions scarcely serve as an explanation for the influence of social background on participation in adult education. Differences in educational level, however, do serve as a stronger explanation for the influence of social background on participation in adult education. Yet it confronts us with a new problem: “Why are high-qualified adults more likely to participate in adult education?” Although we cannot answer this question, our analyses show that differences in readiness to learn between low- and high-qualified adults only marginally explain differences in participation.

General Discussion

Discussion

Our main research questions put forward the cultural capital theory to explain differences in participation in adult education. In the PIAAC data, we find only limited support for the validity of this theoretical framework as to the issue of participation in adult education. To be more precise, we find evidence for the influence of social background on participation in adult education, but this effect does not work as predicted, that is, mediated by embodied cultural capital. The cultural capital framework holds that social background should affect practices due to the different embodied cultural capital, that is, dispositions. The latter are cultivated in each social class, which makes higher classes—and especially class fractions with higher cultural capital—more positively disposed toward investments in cultural capital, such as participation in adult education. This explanation, however, does not hold according to our analyses. First, because we found one’s own cultural capital to be a stronger predictor of embodied cultural capital than social background. Second, and foremost, because we found embodied cultural capital to only marginally mediate the influence of social background on participation in adult education.

Nevertheless, both parental cultural capital and one’s own cultural capital are relatively strong predictors of participation in adult education: cultural capital as a direct predictor of participation in adult education, and parental cultural capital as a predictor of one’s own cultural capital. This, however, introduces a new question: “If not because of different embodied cultural capital, why do higher qualified adults participate more in adult education than lower qualified adults?” This presents a direction for future research: to study the relationship between social background, embodied cultural capital, and participation in adult education. Indeed, our results do not necessarily imply that the cultural capital framework has no explanatory power. The results rather suggest that the scope of embodied cultural capital included in the present study and operationalized in the PIAAC study should be broadened to include others than just “readiness to learn.” In this respect, for example, attitudes toward education (Azjen & Fishbein, 1980; Cross, 1981), autonomous and controlled motivation (Deci & Ryan, 1985, 2000), perceived utility and value (Darkenwald & Merriam, 1982; Rubenson, 1977), cultural participation (e.g., DiMaggio & Mukhtar, 2004), and aspirations (e.g., Dumais, 2002) can be considered. However, since we build on the secondary analysis of available PIAAC data, this confined the scope of the embodied cultural capital variables used in the present empirical investigation.

A second direction for future research is to investigate between-country differences in participation in adult education. Although we did not elaborate on this issue, our results show considerable differences in both participation rates and applicability of the cultural capital framework across countries. Boeren, Holford, Nicaise, and Baert (2012), for example, established that motives to participate in adult education depend on the education system and social policy, and that countries can be “clustered” around welfare state regime types. Rubenson and Desjardins (2009) also found barriers to participation in adult education to be correlated with welfare state regime types.

Limitations

Performing secondary analysis on existing data always comes at a price. In this case, we identify two resulting limitations. The first limitation of this study involves the use of proxy measures and, more specifically, the use of “readiness to learn” as a proxy for embodied cultural capital. The results clearly show that this measure has limited explanatory power, as evidenced by the small (standardized) regression coefficients and explained variance (see Table 3). However, this should not be interpreted as an inadequacy of embodied cultural capital as an explanatory reference. Though readers might judge the proportion of average explained variance by the variable readiness to learn as limited, we stress that this percentage is a first step toward developing a broader explanatory framework to link cultural capital to participation in adult education. Next to this variable, the PIAAC data offer additional variables that—though they also reflect limitations—might help explain larger proportions in variance dispositions (e.g., social trust, political efficacy). Furthermore, the scope of dispositions affecting participation in adult education should be investigated more in depth. An interesting line of research is reflected in the research tradition on the “what” and the “why” of goal pursuit (Deci & Ryan, 2000). More precisely, in studying learning dispositions, it would be interesting to differentiate between the aims (task-, self-, or other-based) and reasons (autonomous or controlled) underlying the learning behavior (Vansteenkiste, Lens, Elliot, Soenens, & Mouratidis, 2014) and to investigate whether and how they are differentially affected by the learners’ social background. This might allow examining to what extent learning dispositions are qualitatively (instead of quantitatively) different according to social background.

A second limitation to this study is linked to the statistical method SEM. Though the method helps test the nature of structural relationships between the variables, the method does not result in testing causal relations in the model. Also, the cross-sectional design of the PIAAC survey makes it impossible to (dis)prove the causality implied in models. Other research designs, such as a longitudinal or (quasi-)experimental design, are better suited in this respect. Additionally, such research designs would allow investigating the rigidity/flexibility of cultural capital by tracking changes over time.

Conclusion

Participation in adult education is important in view of individual and society development. Given the observed—worldwide—unequal participation in adult education, there is a cause for concern, and research should help unravel conditions explaining this phenomenon. As such, research can become helpful in developing supportive policies. In addition, this research helps test available frameworks and drive the development of up-to-date theoretical conceptions.

Our study reveals that social background indirectly influences participation in adult education because it affects initial educational attainment. Thus, if policy would like to improve equal participation in adult education, a strong focus should be on undermining the link between social background and initial educational attainment; so equal participation in adult education is (also) achieved through a sustained striving for equal opportunities in compulsory education. Interestingly, the effect of social background appears to be less rigid than assumed by cultural reproduction interpretation of cultural capital theory. Dispositions are not set into stone—that is, determined by social background—but are malleable beyond the influence of the family. This means that also negative (learning) dispositions, acquired earlier in life, are still subject to change, as long as the change-conducive conditions are readily present. Research into the development and change of (learning) dispositions over time, especially for those individuals from disadvantaged backgrounds, is thus essential for social policies geared toward change.

Footnotes

Appendix

Appendix B.

Direct and Indirect Effects of Parents’ Education and Educational Attainment on Participation in Adult Education, as a Percentage of Total Effect, by Country.

Country	Variable	Type	Mediator	Β	SE	β	%^a
Austria	Parents’ education	Indirect	RDL	.03***	.01	.02	14
			ED. AT. → RDL	.03***	.00	.02	11
			ED. AT.	.18***	.01	.12	75
		Total		.24***	.02	.16
	Educational attainment	Direct		.12***	.01	.32	87
		Indirect	RDL	.02***	.00	.05	13
		Total		.14***	.00	.37
Belgium (Flanders)	Parents’ education	Indirect	RDL	.02***	.01	.02	7
			ED. AT. → RDL	.02***	.00	.02	8
			ED. AT.	.24***	.02	.19	85
		Total		.29***	.02	.22
	Educational attainment	Direct		.14***	.01	.41	91
		Indirect	RDL	.01***	.00	.04	9
		Total		.16***	.01	.45
Canada	Parents’ education	Indirect	RDL	.02***	.00	.01	8
			ED. AT. → RDL	.01***	.00	.01	6
			ED. AT.	.17***	.01	.13	86
		Total		.19***	.01	.15
	Educational attainment	Direct		.13***	.01	.35	94
		Indirect	RDL	.01***	.00	.02	6
		Total		.14***	.01	.37
Cyprus, Republic of	Parents’ education	Indirect	RDL	.01	.00	.01	2
			ED. AT. → RDL	.02***	.00	.02	7
			ED. AT.	.29***	.02	.21	91
		Total		.32***	.02	.23
	Educational attainment	Direct		.15***	.01	.46	94
		Indirect	RDL	.01***	.00	.03	6
		Total		.16***	.01	.50
Czech Republic	Parents’ education	Indirect	RDL	.02**	.01	.01	7
			ED. AT. → RDL	.02***	.01	.01	8
			ED. AT.	.24***	.02	.12	85
		Total		.28***	.02	.14
	Educational attainment	Direct		.13***	.01	.32	92
		Indirect	RDL	.01***	.00	.03	8
		Total		.14***	.01	.35
Denmark	Parents’ education	Indirect	RDL	.01***	.00	.01	6
			ED. AT. → RDL	.01***	.00	.01	7
			ED. AT.	.16***	.01	.13	87
		Total		.19***	.01	.15
	Educational attainment	Direct		.14***	.01	.35	93
		Indirect	RDL	.01***	.00	.03	7
		Total		.15***	.01	.38
Estonia	Parents’ education	Indirect	RDL	.06***	.01	.05	26
			ED. AT. → RDL	.03***	.00	.02	11
			ED. AT.	.16***	.01	.12	63
		Total		.25***	.01	.19
	Educational attainment	Direct		.14***	.01	.36	84
		Indirect	RDL	.02***	.00	.07	15
		Total		.16***	.01	.42
Finland	Parents’ education	Indirect	RDL	.01**	.00	.01	5
			ED. AT. → RDL	.02***	.00	.01	6
			ED. AT.	.22***	.02	.15	89
		Total		.24***	.02	.17
	Educational attainment	Direct		.14***	.01	.44	94
		Indirect	RDL	.01***	.00	.03	7
		Total		.15***	.01	.47
France	Parents’ education	Indirect	RDL	.01***	.00	.01	5
			ED. AT. → RDL	.02***	.00	.01	6
			ED. AT.	.24***	.01	.17	90
		Total		.26***	.01	.19
	Educational attainment	Direct		.10***	.00	.38	95
		Indirect	RDL	.01***	.00	.02	5
		Total		.11***	.00	.40
Germany	Parents’ education	Indirect	RDL	.03	.01	.02	11
			ED. AT. → RDL	.02***	.00	.01	8
			ED. AT.	.22***	.02	.14	82
		Total		.27***	.02	.17
	Educational attainment	Direct		.13***	.01	.35	91
		Indirect	RDL	.01***	.00	.03	9
		Total		.14***	.01	.38
Great Britain^b	Parents’ education	Indirect	RDL	.02**	.01	.01	7
			ED. AT. → RDL	.02***	.00	.01	7
			ED. AT.	.18***	.03	.13	86
		Total		.21***	.03	.16
	Educational attainment	Direct		.15***	.02	.32	93
		Indirect	RDL	.01***	.00	.03	7
		Total		.16***	.02	.34
Ireland	Parents’ education	Indirect	RDL	.01*	.00	.01	2
			ED. AT. → RDL	.01***	.00	.01	5
			ED. AT.	.23***	.02	.18	93
		Total		.25***	.02	.19
	Educational attainment	Direct		.13***	.01	.41	95
		Indirect	RDL	.01***	.00	.02	5
		Total		.13***	.01	.43
Italy	Parents’ education	Indirect	RDL	.01*	.01	.01	3
			ED. AT. → RDL	.04***	.01	.02	8
			ED. AT.	.42***	.03	.22	89
		Total		.47***	.02	.25
	Educational attainment	Direct		.11***	.01	.44	91
		Indirect	RDL	.01***	.00	.04	9
		Total		.13***	.01	.48
Japan	Parents’ education	Indirect	RDL	.02***	.01	.02	11
			ED. AT. → RDL	.02***	.00	.02	11
			ED. AT.	.16***	.01	.12	77
		Total		.20***	.02	.15
	Educational attainment	Direct		.13***	.01	.30	87
		Indirect	RDL	.02***	.00	.04	13
		Total		.15***	.01	.34
Korea	Parents’ education	Indirect	RDL	.03***	.01	.02	10
			ED. AT. → RDL	.03***	.00	.02	12
			ED. AT.	.22***	.01	.15	79
		Total		.28***	.01	.20
	Educational attainment	Direct		.12***	.01	.39	87
		Indirect	RDL	.02***	.00	.06	13
		Total		.14***	.01	.45
Netherlands	Parents’ education	Indirect	RDL	.03***	.01	.03	16
			ED. AT. → RDL	.02***	.00	.02	11
			ED. AT.	.16***	.01	.12	72
		Total		.21***	.01	.17
	Educational attainment	Direct		.13***	.01	.33	87
		Indirect	RDL	.02***	.00	.05	14
		Total		.15***	.01	.38
Norway	Parents’ education	Indirect	RDL	.02**	.01	.01	9
			ED. AT. → RDL	.01***	.00	.01	4
			ED. AT.	.16***	.01	.12	86
		Total		.18***	.01	.14
	Educational attainment	Direct		.14***	.01	.34	95
		Indirect	RDL	.01***	.00	.02	5
		Total		.14***	.01	.36
Poland	Parents’ education	Indirect	RDL	.04***	.01	.02	7
			ED. AT. → RDL	.05***	.01	.03	9
			ED. AT.	.40***	.03	.24	83
		Total		.48***	.03	.29
	Educational attainment	Direct		.16***	.01	.48	90
		Indirect	RDL	.02***	.00	.05	10
		Total		.18***	.01	.53
Russia	Parents’ education	Indirect	RDL	.01*	.01	.01	5
			ED. AT. → RDL	.02***	.01	.02	15
			ED. AT.	.13***	.03	.09	80
		Total		.16***	.03	.12
	Educational attainment	Direct		.07***	.01	.24	84
		Indirect	RDL	.01***	.00	.05	16
		Total		.09***	.01	.28
Slovak Republic	Parents’ education	Indirect	RDL	.03***	.01	.02	6
			ED. AT. → RDL	.03***	.01	.02	8
			ED. AT.	.35***	.02	.21	86
		Total		.41***	.02	.24
	Educational attainment	Direct		.15***	.01	.41	92
		Indirect	RDL	.01***	.00	.04	8
		Total		.17***	.01	.45
Spain	Parents’ education	Indirect	RDL	.01***	.00	.01	3
			ED. AT. → RDL	.02***	.00	.02	7
			ED. AT.	.27***	.01	.18	90
		Total		.31***	.01	.20
	Educational attainment	Direct		.13***	.01	.45	93
		Indirect	RDL	.01***	.00	.04	7
		Total		.14***	.00	.48
Sweden	Parents’ education	Indirect	RDL	.03***	.01	.03	17
			ED. AT. → RDL	.01***	.00	.01	6
			ED. AT.	.14***	.01	.12	77
		Total		.18***	.01	.16
	Educational attainment	Direct		.13***	.01	.34	93
		Indirect	RDL	.01***	.00	.03	7
		Total		.14***	.01	.37
United States	Parents’ education	Indirect	RDL	.01**	.00	.01	2
			ED. AT. → RDL	.02***	.00	.01	6
			ED. AT.	.30***	.02	.21	91
		Total		.33***	.02	.23
	Educational attainment	Direct		.15***	.01	.45	94
		Indirect	RDL	.01***	.00	.03	6
		Total		.16***	.01	.48

Note. RDL = readiness to learn; ED. AT. = educational attainment; ED. AT RDL = path from educational attainment to readiness to learn.

As a percentage of the total effect. ^bIn Great Britain, only England and Northern Ireland participated in the Programme for the International Assessment of Adult Competencies survey.

p < .05. **p < .01. ***p < .001.

Acknowledgements

We would like to thank Professor Yves Rosseel for his invaluable assistance in elaborating the methodological and analytical part of this study.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Sebastiano Cincinnato is a PhD candidate currently working at the Department of Educational Sciences (Vrije Universiteit Brussel, Belgium) and affiliated to the Department of Educational Studies (Ghent University, Belgium). His research focuses on participation and study success in adult and higher education.

Bram De Wever, PhD, is an associate professor at the Department of Educational Studies (Ghent University), where he teaches a course on adult education. His research is focusing on technology enhanced learning and instruction, peer assessment and feedback, computer-supported collaborative learning activities, blended learning, and inquiry learning in higher education and adult education settings.

Hilde Van Keer is Professor at the Department of Educational Studies (Ghent University, Belgium). In her work, she focuses on three main themes: (1) peer learning, with an emphasis on peer and student tutoring; (2) reading and writing education, with an emphasis on teaching writing, reading comprehension and reading motivation; and (3) self-regulated learning in pupils and higher education students.

Martin Valcke is currently head of the Department of Educational Studies (Ghent University; Belgium). His main research field is the innovation of higher education with a strong emphasis on the integrated use of information and communication technologies, quality indicators, collaborative learning, and innovative assessment and evaluation approaches.

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