Participation in Adult Learning

Introduction

Participation research is concerned primarily with three overarching questions: What is the extent of participation? Who is participating? Why are certain people or groups participating either more or less, or not at all? Traditionally, the focus has been on adult education and training (AET), rather than a broader notion of adult learning. In this article, a distinction is made between AET and informal learning where possible.

What Is the Extent of Participation?

Adult Education and Training

Participation rates based on the International Adult Literacy Survey (IALS; 1994–98) are reported in Table 1. Tables 2(a) and 2(b) supplement with other international comparative data sources, and although the data from the alternative sources are not strictly comparable, the overall patterns are fairly consistent. In general, it can be inferred that rates vary substantially across countries, falling into four broad groups:

• close to or exceeding 50% – the Nordic countries include Denmark, Finland, Iceland, Norway, and Sweden;
• between 35% and 50% – countries of Anglo-Saxon origin such as Australia, Canada, New Zealand, the United Kingdom, and the United States as well as a few of the smaller Northern European countries such as Luxembourg, the Netherlands, and Switzerland;
• between 20% and 35% – this group features the remaining Northern European countries such as Austria, Belgium (Flanders), and Germany, some Eastern European countries such as Czech Republic and Slovenia, and some Southern European countries such as France, Italy, and Spain; and
• consistently below 20% – some Southern European countries such as Greece and Portugal, some additional Eastern European countries such as Hungary and Poland, and the only South American country where comparable data are available, Chile. 

However, according to the 2003 data from the Adult Literacyand Lifeskills Survey (ALLS),Canada, Switzerland, and the United States appear to have climbed into the exceeding 50% category.

The mean number of hours per adult is also reported in Table 1. This combines the incidence and volume of AET and thus offers a more comprehensive measure of the total effort. Denmark, Finland, and New Zealand report an average of over 100 h of AET per adult over a 12-month period – this is equivalent to every adult aged 16–65 years spending over 2.5 working weeks in AET per year. Countries featuring high participation but low average volume display comparatively lower adult learning per capita. Switzerland, the United Kingdom, and the United States have participation rates around 35–50% but, after adjusting for low volume, countries in the 20–35% range, such as Ireland and Slovenia, surpass them in their total AET effort. The former are considered to follow an extensive model, inwhich a fairly lowvolume is provided to a large number of adults, whereas the latter are considered to follow an intensive model, where provision is concentrated on fewer people (OECD, 2003a).

Changes in AET

Many national data sources point toward a general increasing trend in AETparticipation over the last 25 years.This is mostly attributed to the rising concern for human capital over the last decades since increases in AET for job-related reasons account for much of the rise since the early 1980s (Boudard and Rubenson, 2003: 267). More recent trend data from the European Union Labour Force Survey (ELFS) reveal a mixed pattern – participation rates appear to have generally increased between 1995 and 2000; however, this does not hold for all countries (OECD, 2003a: 39).

Participation in adult education and training and average number of hours of participation in the previous year, by type of training, population aged 16–65 years^a Table 1

 

 

^aAdults aged 16–19 years participating in full-time studies (4 or more days per week) toward ISCED 0-3, and who are not financially supported by an employer or union are excluded. Similarly, adults aged 16–24 years in full-time studies (4 or more days per week) toward ISCED 4-7, and who are not financially supported by an employer or union are excluded.

^bSweden and Portugal did not ask about job-related and nonjob-related training in a comparable way, nor did they ask about training durations. Germany is excluded because the survey did not ask about adult education and training in a comparable way.

^cMean number of hours per adult ¼ Mean number of hours per participant* Participation rate/100.

^dDuring the past 12 months, that is since . . ., did you receive any training or education including courses, private lessons, correspondence courses, workshops, on-the-job training, apprenticeship training, arts, crafts, recreation courses, or any other training or education?

–, indicates that data are not available.

Source: International Adult Literacy Survey, 1994–98; reprinted from Desjardins, R., Rubenson, K., and Milana, M. (2006). Unequal Chances to Participate in Adult Learning: International Perspectives. Paris: UNESCO.

Types of AET

It can be seen from the IALS data in Table 1 that jobrelated AET is dominant. Personal- and social-related AET can also play a substantial role. Comparatively high rates of participation in nonjob related AET are reported in Finland, the Netherlands, and Switzerland. It is, however, difficult to distinguish between different types of AET. Often, this is assessed on the basis of individuals’ reasons for participating. However, Rubenson (2001) showed that there are many reasons for participation and that these are interrelated. Further, Desjardins et al. (2006) demonstrated that the way in which questions are phrased has implications for interpreting the complex motivations associated with participation. Ideally, surveys should not only permit respondents to state a number of different reasons for participating, but also ask them to rank them according to importance.

Participation in adult education and training in the previous year, population aged 16–65^a Table 2

 

^aAdults aged 16–19 years participating in full-time studies (4 or more days per week) toward ISCED 0–3, and who are not financially supported by an employer or union are excluded. Similarly, adults aged 16– 24 years in full-time studies (4 or more days per week) toward ISCED 4–7, and who are not financially supported by an employer or union are excluded.

^bDuring the last 12 months. . . did you take any education or training? This education or training would include programs, courses, private lessons, correspondence courses, workshops, on the-job-training, apprenticeship training, arts, crafts, recreation courses, or any other training or education.

^cHave you done any studies or training in the past 12 months? Please choose the three answers that best describe your own situation: yes, to meet new people; yes, to be less likely to lose my job/to be less likely to be forced into retirement; yes, to better enjoy my free time/retirement; yes, to be able to do my job better; yes, to obtain a certificate, diploma or qualification; yes, to be able to take greater responsibilities/increase my chances of promotion; yes, to better manage my everyday life; yes, to change the type of work I do altogether, including starting my own business (for retraining, etc.); yes, to achieve more personal satisfaction; yes, to get a job; yes, to improve my chance of getting another job, including one which would suit me more; yes, to increase my general knowledge; yes, for other reasons (spontaneous); no, I have not, but I would like to; no, I am not particularly interested, no, for other reasons (spontaneous); and don’t know.

Source: Adult Literacy and Lifeskills Survey, 2003; EU Barometer, 2003; reprinted from Desjardins, R., Rubenson, K., and Milana, M. (2006). Unequal Chances to Participate in Adult Learning: International Perspectives. Paris: UNESCO.

Definitions and Measurement

Most AET data sources focus on measuring formal provision; however, increasingly, there is an interest in nonformal and informal activities. This poses some challenges, especially in defining what counts as adult learning. Increasingly, it is difficult to distinguish adult learners from first-time students attending regular school or university. Pragmatic solutions are to consider all the learning activities of the adult population aged 25–65 years or, if possible, to consider the population aged 16–65 years, but exclude full-time students aged 16–24 years. A more sophisticated definition, if the data allow for it, is to count the studies of the following groups as participation: fulltime students aged 16–24 years who are sponsored by an employer or union/association; full-time students over the age of 19 years who are enrolled in primary or secondary programs; and full-time students older than 24 years who are enrolled in postsecondary programs. The last group is significant because there can be substantial overlap between what is considered adult learning and higher education, especially in countries where the higher education system features a high degree of openness to nontraditional adult students.

Another issue is the reference period for which participation rates are based on. For example, the IALS uses a 12-month reference period, whereas the ELFS uses a 4-week period. Shorter reference periods are adequate for reporting participation rates of populations, but inadequate for an in-depth understanding of adult learning pathways, both in terms of understanding the takeup of adult learning and its interaction to the provision of opportunities.

Informal Learning

Informal learning encompasses a broad range of learning activities (Livingstone, 1999). Patterns of engagement thus depend on what is being measured. Of the nine informal learning measures in the ALLS (2003), two dominate. Learning by doing is mentioned by around 90%, while learning by watching ranges from a high of 87% in Switzerland to a low of 77% in Canada. These former measures cover a broad range of nonspecific experiences; therefore, it is difficult to interpret their significance or value. More specific informal learning activities, related to work and culture, are particularly prevalent in Switzerland and less common in Canada and the United States with Norway somewhere in between. A vast majority of the Swiss (86%) report that they read manuals or other materials compared to 55% for Canada and the United States. The Swiss more often note that they learn by being sent around their organization or attending special talks. Further, they more frequently (44%) go on guided tours at museums or galleries than Canadians, Americans, or Norwegians (30%). Comparative measures of learning through the interactive use of information technology reveal only small differences in the use of computers or the Internet, while learning with the help of video, television, and tapes varies from a high of 52% in the United States to a low of 35% in Switzerland.

Who Is Participating?

Observed differences in participation have been linked to inequalities of opportunity and living conditions; therefore, the demographic and social make-up of participation becomes an important issue.

Adult Education and Training

For a range of countries (18 Organisation for Economic Co-operation and Development (OECD) ones and two non-OECD ones), IALS data show that those who are women, older, from low socioeconomic backgrounds (as reflected in their parents’ level of education), low educated, low skilled, in low-skill jobs, unemployed, or immigrants are the least likely to participate in adult learning (see Table 3). In many cases, people belong to more than one group at the same time, which exacerbates observed differences (see Desjardins et al., 2006: 74).

Percent of adults participating in AET and adjusted odds ratios^a,b showing the likelihood of participating in AET during the year preceding the interview, by various classification variables, 1994–98 Table 3

^aOdds ratios reflect the relative likelihood of an event occurring for a particular group compared to a reference group. An odds ratio of 1 represents equal chances of an event occurring for a particular group vis-a`-vis the reference group. Coefficients with a value below 1 indicate that there is less chance of the event occurring for a particular group compared to the reference group, and coefficients greater than 1 represent increased chances. From Hosmer, D. W. and Lemeshow, S. (1989). Applied Logistic Regression. New York: Wiley.

^bOdds are adjusted for: age, gender, parents’ education, education, prose literacy skill level, employment status, occupation, immigrant status, minority language status, and size of community.

*p < 0.10, statistically significant at the 10% level.

**p < 0.05, statistically significant at the 5% level.

***p < 0.01, statistically significant at the 1% level.

Source: International Adult Literacy Survey, 1994–98; reprinted from Desjardins, R., Rubenson, K., and Milana, M. (2006). Unequal Chances to Participate in Adult Learning: International Perspectives. Paris: UNESCO.

Informal Learning

Some research has stressed that the law of inequality does not apply to informal learning (e.g., Livingstone, 1999). However, this depends on the measure of informal learning used. Measures that are all inclusive or very general and refer to nonspecific situations of learning by doing or learning by watching show that informal learning is more or less a universal activity (see above). In contrast, measures which are context specific and reflect learning that is likely to enable the creation of or access to resources tend to reveal a clear pattern of inequality. Rubenson et al. (2007) find that groups with low levels of educational attainment report a substantially lower engagement in reading or using computers to learn. There is limited research on the extent to which different forms of informal learning contribute to strengthening resources that have economic and social value.

Why Are Certain People or Groups Participating More than Others?

Determinants

Different characteristics can be used to explore and reveal patterns of participation in greater detail. As described above, this can be done by distinguishing among salient groups such as those delineated by: age, sex, social class, level of education, level of skill, and occupational, employment, and minority status. This broadens our empirical understanding of who participates. However, a determinants analysis goes a step further by including a range of factors that are thought to be relevant in explaining the observed patterns. This may include the same characteristics used to define a group; however, the difference in a determinants analysis is that there are underlying theories that link the characteristics to the observed pattern, and thus have explanatory value. Participation research has explored various explanatory factors.

Disciplinary Perspectives

Different explanations have been put forth, ranging from those rooted in psychological to sociological to economic perspectives (Tuijnman and Fa¨gerlind, 1989). The most appealing ones have an interdisciplinary character since they are more useful for building a comprehensive understanding of adult learning participation. It is rare, however, that different disciplinary perspectives are brought together. There is no unified or comprehensive theoretical perspective guiding participation research. One downside to complex inclusive models is that they can inhibit empirical testing and limit the usefulness of results, especially with regard to specific situations or needs. For example, practitioners may specifically be interested in knowing what information is best suited for altering adult beliefs about the likely outcomes of participating. In this situation, a psychological perspective may provide the necessary depth. Ideally, a portfolio of models, which can be applicable in different contexts for different purposes, needs to be built up.

Further research requires explanatory models or theories that are disciplinary, multidisciplinary, and multilevel. Rubenson (1987) mentions three approaches to model building. The first focuses on the individual’s psychological factors – the micro-level. The second emphasizes external factors and their structural conditions which influence the individual – the macro-level. The third approach looks into the interaction between individual and social forces. Each type of explanation is essential, but neither is sufficient in isolation from each other.

Explanations Based on Psychological Perspectives

While motivation can equally be a social phenomenon that is driven by external expectations which are placed on individuals – such as family, workplace, and community demands for competencies – many explanations focus solely on individual psychological factors. The psychological perspective focuses primarily on personality traits, intellectual abilities, and other behavioral dispositions that center on attitudes, expectations, intentions, and other motivational attributes.

Personality traits and abilities

Individuals have a degree of agency; therefore, cognitions, beliefs, and psychosocial capabilities feature as crucial elements that can explain participation. Participatory behavior is the result of diverse interactions between individuals’ beliefs, skills, capabilities, and values. Rubenson (1987) stresses the importance of self-concepts such as self-esteem and self-efficacy in predicting participation. He suggests that adults who feel good about themselves are more likely to succeed in achievement-oriented situations. Conversely, an important benefit of adult learning is improved beliefs about self-efficacy (Hammond, 2003), pointing to a cycle of recurrent learning.

Previous learning experiences are key factors predicting further learning (Tuijnman, 1989; Boudard, 2001; Desjardins, 2004). Independent of educational attainment, Tuijnman (1989) finds that cognitive ability also exerts a positive influence on the accumulation of learning experiences over the lifespan. Educational attainment reflects accumulated knowledge, skills, and other traits that are associated with the probability of continued learning.

A common trait shared by early school leavers is a lack of self-confidence with regard to learning because of bad pedagogical experiences (Illeris, 2004a). Adults with low levels of education are less likely to participate because they lack readiness both in terms of knowledge and skills as well as their motivation to learn. A low readiness to learn is a substantial dispositional barrier to participation.

Motivational orientations and reasons

Motivational orientations toward learning can be important predictors of participation. Houle (1961) proposed a typology which suggests that participants are either goal oriented (use learning to accomplish objectives), activity oriented (find meaning in the circumstances of learning), or learning oriented (seek knowledge for its own sake). Boshier (1971, 1982) and Boshier and Collins (1985) developed the Education Participation Scale (EPS) to assign motivational orientation scores to individuals which allowed for in-depth investigation into the relationships among orientations and various demographic variables and other characteristic variables. Although the amount of variance explained by sociodemographic variables was small, research by Boshier and colleagues revealed that those with low education, low occupational status, and low income were most likely to participate for social contact, social stimulation, community service, and external expectation reasons. In contrast, those with higher levels of education, occupational status, and income were more often enrolled for professional advancement and cognitive interest reasons.

These findings partly corroborate with Maslow’s (1954) hierarchy of needs theory which suggested that the motivating forces behind participation are conditional on whether subordinate needs are satisfied. Individuals with lower-order needs for survival which remain to be satisfied are deficiency motivated, whereas those working toward higher-order needs are growth motivated.

Arguably, obtaining secure employment is a lowerorder need and, thus, the implication by Boshier’s finding that professional advancement is associated with a higherorder need is puzzling. A more broadly based measure of job-related AETreveals that most adults participate for job-related reasons, whether associated with low socioeconomic status or not (e.g., OECD, 2003a; Desjardins et al., 2006).

Learning for job-related reasons is linked to goals of finding a job, finding a better job, being promoted at work, keeping a job, and/or becoming more efficient in one’s current job. It is a dominant reason reported (at least 60%) in recent surveys such IALS, ALLS, and the 2000 European Union Barometer, and reaches as high as 90% in Australia, Denmark, Norway, the United Kingdom, and the United States. The divide between job- and non-jobrelated reasons, however, is not so clear-cut (Courtney, 1992: 50), which may be a reason why attempts to link distinct motivational orientations to participation explain little variation. Further, adults may find their reasons for participating difficult to articulate and they might not always be aware of them all (Darkenwald and Merriam, 1982: 136; Rubenson, 2001). Even a temporary lack of a specific reason may be seen as a reason for participating since the activity itself can be a way to obtain or rediscover new goals that could be pursued (Courtney, 1992: 87).

Attitudes and Intentions

Research has also pointed to the importance of attitudes toward learning. Houle (1961) claimed that every adult has an underlying conviction about the nature and value of learning which influences their opinion and, hence, the decision to participate. Darkenwald and Hayes (1988) constructed the Adult Attitudes toward Continuing Education Scale (AACES) to investigate the relationships among attitudes and participation. They found that the importance attributed to learning appears to be the most decisive factor in predicting participation; however, importance in relation to what needs further attention (see below).

Explanations Based on Social Perspectives

Individualistic perspectives have had serious consequences for how inequalities in participation are understood and what measures are deemed adequate to support lifelong learning for all. Individuals face several constraints in acting independently and making their own free choices. Shortcomings to the individualistic psychological and economic perspectives can be addressed by turning to the various external (and structural) influences on participation, such as social and economic institutions (government policy, organizations, industries, markets, and classes) at a macro level, and work structures at a micro level. Separately, life history approaches to studying participation have broadened the structuralist approach by embracing much of the criticism of the individualistically oriented theories. This is done by situating the role of individual subjective experiences and actions as well as collective ones in their wider social and cultural contexts. A discussion regarding the relationships between life situation and participation as well as institutional barriers and participation, which are an elaboration of explanations based on social perspectives, is given elsewhere in the encyclopedia.

Explanations Based on Economic Perspectives

Most, but not all, explanations based on economic perspectives have tended to be dominated by individualistic approaches to the decision to participate. A common assumption is that individuals make a rational choice to participate or not, and that this decision is based on the information they have regarding the costs and benefits of participating. However, individuals are substantially limited by the imperfect information they have regarding the costs and benefits. There are also risks inherent with realizing the benefits which rational agents may not want to undertake.

Cost limitations and credit constraints

Desjardins et al. (2006) underscored the point that a lack of external economic support (from employers and governments) and credit constraints, especially for disadvantaged groups, is a significant barrier to participation. Even if individuals would like to participate because the future benefits outweigh the immediate costs, they may not have the financial means to do so because of credit constraints and imperfect capital markets. When individuals are not capable of borrowing money to invest in learning, because they do not have any collateral, which would otherwise be profitable, then market failure occurs.

Financial support and incentive-to-invest factor

The long arm of the job is becoming longer and stronger in terms of financing as well as motivation. IALS data show that about two-thirds of participants receive employer support (OECD and Statistics Canada, 2000). There are indications that participation may not always be voluntary and there may be increasing pressure to participate in job-related AET (Hight, 1998; Carre´, 2000). Employer-supported AET is often suggested by employers, although a large portion that is suggested by employees is also supported by the employer (Tuijnman and Hellstro¨m, 2001). Many adults also participate for job-related reasons even though they do not receive financial support fromtheir employer, which reveals the strength of the long arm of the job. Self-financing is, on average, the second most common source of financial support, and, in some countries, the dominant source.

Firms represent a large portion of the training market (OECD, 2003a: 51–53). The Second Continuing Vocational Training Survey (CVTS2) data show that over 70% of firms in the majority of European countries provide support for AET (European Commission, 2002). Adults who work in large firms, especially those that compete on global markets and undergo significant technological change and/or changing work practices, appear to receive more AET.

The supply of employer-supported opportunities appears to be primarily targeted at prime-age employees who are highly educated and skilled. Plausibly, employers consider adults with higher levels of education more trainable. Older workers, women, and immigrants tend to face reduced opportunities for employer-supported AET (Desjardins et al., 2006). These tendencies are consistent with the overall patterns of participation (see Table 3).

There is much debate about whether current levels of investment in AETare adequate. The potential for underinvestment arises due to several market failures, and can be linked to both employer and employee behavior (see Desjardins et al., 2006). Overall, evidence shows that an underprovision of AET is likely to occur in all OECD countries (see OECD, 2003b: 248).

Government support, the least common source of financial support, tends to benefit those who already display high rates of participation, namely younger adults, the higher educated, and those who are in white-collar, high-skill occupations, rather than vulnerable groups. This is likely due to pressures for government policies to seek increases in efficiency through the adoption of a more market-oriented approach and outcomes-based funding. This increases the likelihood that AET initiatives/ programs will target those easiest to recruit and most likely to succeed. Initiatives to reach disadvantaged groups often correspond better to the demands of the advantaged (Rubenson, 1999: 116). Few countries have effective public policies and structures in place to help those who are hard to reach. The Nordic countries are among the few and, accordingly, they tend to show comparatively higher rates of participation among the low educated (see Table 3).

Nature of work and skill-requirements factor

Recent research suggests that industrial and occupational structures of countries are instrumental in structuring participation. This perspective moves beyond the narrow individualistic one and encompasses a social and structural perspective. The world of work places substantial demands on individuals which necessitates continuous learning and periodic upgrades of competencies, acting as a substantial motivating force for many adults.

Recent research suggests that literacy practices at work – in particular the frequency and variety of reading practices – is one of the most significant determinants of participation in job-related AET (Desjardins et al., 2006). More generally, the workplace is a learning space (Illeris, 2004b: 77–89); however, opportunities to learn new things on the job vary with the characteristic and position of the job, which exacerbates inequalities in adult learning (A° berg, 2002). Workers who are already better positioned in the labor market have more opportunities and incentives to acquire and develop competencies. Further, the structure of occupations and production in a particular country are likely to bear a strong influence on the distribution of work-related adult learning.

Explanations Based on Individuals’ Interactions with Social Influences

Explanatory models of participation that include social and individual forces as well as their interaction have been proposed by a number of researchers. Some of the above explanations build on these, but a brief review of the more well-known ones is provided here. These models include most of the proximal and distal variables that are hypothesized as relevant to participation and are thus useful for building a comprehensive understanding of participation. The underlying reasons why adults participate (or not) are complex, featuring dynamic and interactive feedback effects that occur at multiple levels.

McClusky (1963) presented an individual–social interactive model called the power–load–margin model. Load represents the internal and external demands placed on the individual; power represents the agency of the individual to carry the load; and margin is the ratio of load to power which signifies the likelihood to participate. By linking Maslow’s (1954) hierarchy of needs model with Lewin’s (1947) force field analysis model, Miller (1967) suggested that when individual needs and social forces both point to a commonly perceived demand for participation, then participation will be high. Similarly, Boshier’s (1971) congruence model suggested that when actual and perceived notions of self, others, and educational environment diverge, it is less likely that adults will participate.

Rubenson’s (1975) expectancy valence model made a link between an individual’s expectations about the value of participating, their attitude toward participating, and the likelihood of actual participation. According to this theory, participation will occur and persist if the learning activity is consistent with the learner’s needs and expectations. According to Rubenson, the outcomes will depend on class since attitude and readiness are conditioned by structural and cultural factors. A model by Pryor (1990) and Pryor and Pryor (2005), which applied Ajzen and Fishbein’s theory of reasoned action to participation, focuses on more proximal variables. He suggested that participation is determined by the intention to participate and that the intention is determined by the attitude toward learning and perceptions of social pressures to participate. The latter is a subjective norm that is based on inferences about behavioral expectations of others. According to Pryor (1990), attitude tends to dominate over the subjective norm, and attitudes toward learning are primarily driven by a set of beliefs regarding the outcomes associated with learning.

Cross (1981) developed a psychosocial interaction model called the chain-of-response model, which suggested that participation relates to a complex chain of responses made by the individual vis a` vis social circumstances. Beginning with a self-evaluation and a formation of attitudes toward learning, the importance of learning and the expectations associated with it are evaluated in relation to current needs and, in turn, this is influenced by available information, available opportunities, and institutional barriers.

Variants of these and other models exist and have been tested empirically. Based on a Swedish longitudinal study, Tuijnman (1989) puts an advanced set of structural models, which include psychological, sociological, and economic variables, to rigorous empirical testing. He reported that collectively, social origins, socioeconomic status, cognitive ability, initial levels of education, attitudes toward education, and specific interests in adult learning explain no more than 10–26% of the variance in the participation variable. Boudard (2003) more or less confirmed these patterns for a range of IALS countries.

Further Research

Major research questions that need to be addressed deal with the outcomes of adult learning and how this is linked with motivations to participate. Which mechanisms are more relevant for early development compared to the possible impact of later interventions? Is later intervention merely compensatory, with little chance of making a difference? Or could there be possibility for good timing later on? A better understanding of several relationships is needed:

• Substitutes and complements to more traditional schooling contexts.
• Formal education structures and adult learning, such as the degree of stratification and vocational specificity of pathways, and the extent and distribution of AET among different countries. This requires comparative data at both the system and individual levels.
• Occupational/industrial structures and adult learning. The structure of occupations and production in a particular country are likely to bear a strong influence on the distribution of job-related adult learning.
• Market failures and inequalities in participation. Market imperfections that may relate to learning outcomes are likely to have complex implications that spill over into other policy sectors; thus, reforms should not be undertaken without careful consideration of the relevant trade-offs. More specific information is necessary to devise viable strategies to overcome market failures and hence optimize the allocation and distribution of resources invested in the total learning effort.
• Government policy, governance, and adult learning. How are government policies regarding adult learning formed and coordinated at the intersection of various stakeholders and how does this shape the provision, purpose, and content of adult learning as well as participation?

See also: Barriers to Participation in Adult Education.

R Desjardins, Danish University of Education, Copenhagen, Denmark

© 2010 Elsevier Ltd. All rights reserved.