Time, dwelling and educational disadvantage. Evidence from vocational education students in Italy, France and Greece

ABSTRACT

This paper argues that the consideration of educational disadvantage should go beyond the micro-scale contextual level of individual students, and explore eventual connections with hybrid forms of disadvantage in the social field. The paper draws on the capability approach and the concept of dwelling to introduce dwelling in time as functioning. This refers to students’ abilities to position themselves in an engaging and meaningful relationship within time, and depends on students first achieving the previous functioning of dwelling.

Research results from 222 14–15 year-old vocational education students from Italy, France and Greece using the Future Time Perspective Scale for Adolescents and Young Adults (FTPS-AYA), revealed four main findings: a measurement error in four factors of the scale, an almost systematic lack of correlation of the future-planning factor with the other factors, a significant percentage of students who do not have a clear position on the future and an invisible correlation between the future-positive and future-negative factors and students’ opportunities to choose their specialisations. The paper argues that these four findings should not be seen in isolation and concludes that widening the spectrum of the origins of educational disadvantage facilitates both more effective education policy making and localising conversion factors that can boost students’ resilience.

KEYWORDS:

• Time
• dwelling
• educational disadvantage
• capability approach
• resilience
• vocational education

1. Introduction

This paper argues that the consideration of educational disadvantage is quite often limited to students’ micro-scale social contexts without sufficient consideration for the eventual connections with hybrid forms of disadvantage emerging in the social field. This argument has emerged after observing the relationships between the education, social and policy making fields.

There is undoubtedly a strong relationship between the education and social fields when it comes to education’s role in promoting the building of reflective, cohesive and resilient societies (Reyes, 2013a, 2013b; The World Bank, 2016; Joint Research Center, 2015; OECD, 2018b). However, when it comes to the concept of disadvantage, the relationship between the two fields becomes weaker, as educational disadvantage seems to be limited to students’ micro-scale contexts. Research on educational disadvantage has focused on three main domains of concern: a) students’ motivations, expectations and school engagement (Appleton, Christenson, & Furlong, 2008; Bandura, 1997); b) schools’ and students’ ethnic and socio-economic characteristics (Bouhia, Garrouste, Lebrere, de Saint Pol, & Ricroch, 2011; Van Ewijk & Sleegers, 2010); and c) students’ socio-cultural capital (Bernstein, 1976/1996; Bourdieu, 1977; Bruner, 2008; Charlot, 1999; Dubet, 1997; Vygotski, 1985). In all three domains, attention has been focused on the impact students’ micro-scale characteristics have on their academic paths.

The extent of the above weak relationship is highlighted first by the proliferation of works in the sociological field underlining the emergence of hybrid forms of disadvantage, and second by the fact that these new forms of disadvantage point to a different direction. Smith (1983), Augé (1995), Urry (2003) highlighted the new forms of exclusion and access related to the new power dynamics caused by globalisation. Bauman (2003, 2013, 2016) addressed the new forms of disengagement and dissociation from traditional loyalties and places of belonging, in relation to new processes of exclusion and out-casting (2016, p. 3). Relph (1976), Augé (1995), De Gaujelac, Blondel, and Taboada Leonetti (1997), Wright (1997), Lussault (2009), Lefebvre (2009) related disadvantage with the opportunity, the difficulty and the right to have its place in the world. Sassen (2014, 2015, 2017) discussed the “expulsions at the systemic edges” to refer to radical and globally produced forms of disadvantage that go beyond the familiar idea of inequality, and which by being conceptually and analytically invisible, remain ungraspable.

These observations would become more tangible if applied to how time is perceived in relation to disadvantage in the education, social and policy making fields. In the education field, interest in time is directed mostly towards its future dimension and always within the limits of the school and academic context. Relevant scholarship has explored the impact students’ representations of the future have on their motivation, engagement, goal-setting and choices of academic paths (Peetsma, 2000; Simons, Vansteenkiste, Lens, & Lacante, 2004; Malka & Covington, 2005; Husman & Shell, 2008; Mello & Worrell, 2010; Ferrari, Nota, & Soresi, 2010, p. 62). However, in the sociological field, in the last two decades, an increasing number of references underlines how the increased pace of society and the decreased temporal distances has created new meaning regimes (Toffler, 1970; Augé, 1995, p. 22; Bauman, 1998, p. 18; Agier, 2016) and new types of marginalisation for populations that do not synchronise with the dominant culture of the time (Levine, 1976/2006; Hall, 1983, p. 25; Rosa, 2013, 2017). Meanwhile, at the education policy making level, emphasis is put on the value of students’ anticipation strategies. The OECD, in its position paper on Education 2030, introduced the Anticipation-Action-Reflection cycle, which explicitly correlates the function of anticipation with the quality of learning outcomes and the development of responsible citizenship (OECD, 2018a, 2019a, 2019b).

This research is an attempt to bridge the above-mentioned gaps. The paper a) draws on capability theory as developed by A. Sen (1983, 1984, 1985a, 1985b, 1999) and further developed by Wolf and De-Shalit (2013) to introduce the concept of dwelling in time as a form of a primarily social disadvantage that has repercussions in the education field, b) highlights the value of addressing the origins of disadvantage rather than the symptoms for effective education policy making and c) argues that this shift can provide new opportunities to boost students’ resilience.

In section 2, the paper draws on the capability approach and the concept of dwelling in order to theorise dwelling in time as a disadvantage. In sections 3 and 4, the paper presents the methodology and the results of quantitative research based on 222 14–15-year-old vocational education students in Italy, France and Greece based on the Future Time Perspective Scale for Adolescents and Young Adults (FTPS-AYA; Lyu & Huang, 2016). In section 5, the paper analyses research findings under four interrelated observations. Finally, in section 6, the paper discusses research findings from the perspective of functionings and capabilities and opens new directions for locating factors that promote students’ resilience.

2. Theoretical framework

In this section, the paper first draws on the capability approach to provide the theoretical basis of disadvantage. Second, it draws on definitions and properties related to the concept of dwelling in order to explicate its use in the proposed concept. Third, it defines “dwelling in time” as a disadvantage under the capability approach.

2.1. Capability approach

For its approach to disadvantage, the paper draws on the capability approach, which was first introduced by Sen (1983, 1984, 1985a, 1985b, 1999) before being developed by other scholars in and beyond the economic field, to define disadvantage in terms of functionings and capabilities. Functionings refer to what a person is able to be and to do; in other words, the functions a person actually achieves (Sen, 1985a, p. 10; 1985b, p. 198). Capabilities, which are closely related to functionings, refer to the opportunities available to a person to choose its beings and doings from a set of available functionings (Sen & Dreze, 1989, p. 13).

Four elements in Sen’s approach are of interest for the paper’s argument regarding disadvantage. All four are located in Sen’s famous example of riding a bicycle (1984, p. 334, 1985a, p. 10). Sen, in the action of riding a bicycle, distinguished three elements: a) the commodity/resource (the bicycle), b) the functioning (the act of riding) and c) the mental states/utilities accompanying this functioning (joy, satisfaction, boredom or practicality). In this example, by making the distinction between the resource/commodity and the functioning, Sen first recognised that the achievement of a functioning does not depend solely on the existence of the resource/commodity. Second, by making the distinction between the functioning and the capability, Sen highlighted the value of the opportunities available to the individual to achieve the chosen functionings. Third, by considering the mental states and utilities as distinguishable functionings with their own value and merit, Sen, in my view, made the distinction between primary and secondary functionings in the sense of chronological order, with the achievement of secondary functionings depending on the previous achievement of the primary ones. Furthermore, what is also interesting in Sen’s approach is that prioritising the means over the ends emphasises what Sen (1999, pp. 70–71) called “conversion factors”. These conversion factors, which may be personal (physical condition, skills, intelligence), social (social norms, policies, political conditions) or environmental (the physical or built environment), explain why people who have the same set of commodities or resources develop different functionings.

2.2. Dwelling

For the second axis of its theoretical framework, the paper draws on the concept of dwelling, which has been a recurrent concept in the sociological field in recent decades. Heidegger (1971), in his seminal work Building Dwelling Thinking , used the concept of dwelling to convey the idea of being-in-the-world, which goes beyond the idea of mere presence, indicating the way an entity shows itself within a world (Heidegger, 1962/1927, p. 84). In the case of dwelling, this being-in-the-world is materialised through its relationship with thinking and building. Heidegger’s position has been applied in different contexts and used as a point of reference by many scholars over the last two decades. Ingold (2000) introduced the concept of a “dwelling perspective” to underline a person’s active engagement with the surrounding context. Besse (2013, pp. 20, 29), associated dwelling with the delimitation of territory, the creation of habits and the investment in time. Most recently, Sennett (2018, p. 218) renewed interest in the Heideggerian conception of dwelling, arguing that the absence of commas itself in Heidegger’s title proves the indissociability of the three concepts.

The above approaches share three common elements. First, they all associate dwelling with the action of taking care, and describe it as a profoundly identitary act. Second, they all underline that dwelling is not a straightforward process. Heidegger (1971, pp. 146, 148, 160) had clearly stated that dwelling has to be learnt. Ingold (2000, pp. 36, 166–167) underlined that in order to dwell, there is a need to learn to perceive the world and the affordances of the environment. Besse (2013) stated that a person needs to know the plan, the codes, the names and the language of the environment they want to dwell in. Sennett (2018), in the same direction, stated that dwelling is a “skill, the potentiality of which lies in most people” (p. 204). Third, the approaches all underline that dwelling, apart from space, is also constituted in time. Heidegger (1971), by anchoring the dynamics between building, dwelling and thinking in time as continuum, repeated his previous position in Being and Time (Heidegger, 1962/1927, p. 350) that the past, the present and the future are not chronologically distinct moments, but fused into the horizon of temporality. Ingold (2000, p. 192) argued that the time spent in a place is part of the relational context in which people engage in the world. Besse (2013, p. 7) argued that dwelling practices reveal how people anchor their existence in space and time. Finally, Sennett (2018), by making the distinction between the built environment (the ville) and the ways of living (the cité), introduced time as part of the relational processes the individual develops through dwelling.

2.3. Dwelling in time as disadvantage

Reconnecting with Sen’s approach, this paper argues that dwelling is a functioning depending on the opportunities available to students to find their places in the world through achieving the beings and doings related to dwelling as described above. Focusing on its inherent component of time, “dwelling in time” further refers to students’ abilities to position themselves in an engaging and meaningful relationship within time in order to be able to think and build their futures. The accompanying mental states and utilities of dwelling in time constitute chronologically secondary functionings which depend on the previous achievement of the primary functioning of dwelling. Given the relationship between chronologically primary and secondary functionings, dwelling in time can be described when achieved as “fertile functioning” (Wolf & De-Shalit, 2013), as this achievement has benefits elsewhere. If it is not achieved, however, it acts as a “corrosive disadvantage” that risks spreading its effects to other areas (Wolf & De-Shalit, 2013). From this perspective, the paper seeks to explore how students, as micro-scale actors, dwell in time, what mental states or utilities accompany this functioning, what the potential connections are with disadvantage and the subsequent implications for policy making.

3. Research methodology

3.1. Research tools

In order to explore students’ positioning in relation to time, this paper used the FTPS-AYA developed by Lyo and Huang (2016). The FTPS-AYA has a) good construct validity, with the absolute values of the correlation falling between 0.60 and 0.78 and being greater than the correlations between each factor, and b) good reliability with Cronbach’s alpha, ranging from 0.66 to 0.90 for the total scale. The FTPS-AYA is composed of the following six factors: F1 (future-negative), which refers to a predominantly negative vision of the future; F2, (future-positive), which embodies a generally positive attitude towards the future; F3, (future-confusion), which reflects general confusion and uncertainty about the future; F4, (future-perseverant), which explores long-term perseverance with regard to the future; F5, (future-perspicuity), which explores the existence of an explicit and clear attitude towards the future; and F6, (future-planning), which reflects a present anticipation of future planning and future goal-setting.

3.2. Sample selection

Given that this quantitative research was part of a larger mixed-methods research project, the sample is quite limited. The sample consists of 222 14–15-year-old vocational education and training (VET) students: 79 in Italy (67 boys and 12 girls), 64 in France (45 boys and 19 girls) and 79 in Greece (49 boys and 30 girls).

France, Italy and Greece were chosen because within the last decade, all three countries have faced, to varying degrees, different forms of economic, security and migration crises; furthermore, although they have differences in their socioeconomic and cultural profiles, they present similar profiles in terms of early school leaving (Education and Training Monitor, 2018, pp. 102, 125, 158) and students’ results in PISA tests (OECD, 2016,, pp. 69,151,179).

The particular students were chosen based on three parameters: age, type of school and degree of urbanisation. The age range of 14–15 years old was chosen because it is the most common dropout age in the three countries. The VET field was also chosen because of the high dropout rates. In France, data show that only 76% of VET students finish school compared to 93% of students in general education (Cedefop, 2016, p. 89). In Italy, the calculated risk of dropping out is 0.44% for students in general education and 2.36% in vocational schools (Cedefop, 2016, p. 92). In Greece, according to a report published by the Institute of Education Policy (IEP, 2017), although the percentage of early leavers is 1.92% for upper secondary general education, it climbs to 11.02% for upper secondary VET. Third, the degree of urbanisation was chosen because urbanisation seems to affect each country differently. In Italy, the highest proportion of early leavers is recorded in cities and rural areas; in France, it is in towns and suburbs. In Greece, however, the parameter of urbanisation does not significantly affect dropout rates (Eurostat, 2019).

The data from Greece and Italy were taken from two schools in each country, one urban and one rural. The data from France were taken from three schools in the suburbs of Paris. The schools in Greece and France are located in regions that are considered to be among the poorest regions of each country. Although the regions the Italian schools are located in are not considered poor, the socio-demographic characteristics of the students’ parents, in terms of employment status and education, are highly similar to those in Greece and Italy. This information will be further described in Section 3.3.

In order to have access to students, one university in each country was used as the point of reference for the national context. Each university indicated schools with high percentages of dropouts in their areas, and the research protocol was outlined and the ethics agreement was signed with each university. Then, contact was established with the headmasters of the indicated schools. After discussing the research with their teachers, the schools indicated classes with high percentages of weak students or high student dropout risks. Then, informed consent forms were created in compliance with the European Code of Conduct for Research Integrity (revised edition, 2017) and with the General Data Protection Regulation (GDPR, EU, 2016/679). The consent forms were translated into Italian, Greek and French.

3.3. Composition of the sample

In relation to the students’ socio-demographic characteristics, as described in Section 3.2, the students in France come from the poorest area in the country, the vast majority live in a household with one working parent who works in a low-paid job, and in the majority of cases, their parents have either finished lower secondary education only or have not received an education. Only one parent in the French sample completed tertiary education. In Italy, although the schools are not located in a poor region, the vast majority of the students live in a household with one working parent who works in a low-paid job, with the second parent having lost their job recently, and the vast majority of the parents have completed upper secondary education. Only two parents in the Italian sample had completed tertiary education. In Greece, only one parent had completed tertiary education; the vast majority of the parents had completed upper secondary education. The vast majority of the students live in households with one working parent working in a low-paid job, with the second parent having lost their job during the economic crisis.

In relation to the VET specialisation, in France, the students in the sample follow one of four specialisations: fashion, carpentry, boiler making and management/administration. In Italy, the students follow informatics, management/finance/marketing and electronics and mechanics. In Greece, they follow one of seven specialisations: mechanics, electronics, aesthetics, nursing, informatics, physiotherapy and childcare. However, it should be noted that the choice of specialisation does not follow similar patterns in the three countries. In France, this choice depends on the number of available places in each school, the number of schools offering this specialisation and student demand (Landrier & Nakhili, 2010). In Italy, it seems that students have freedom of choice in terms of specialities. The national qualifications register, created in 2011, organised VET programmes into modules, which allows learners to change areas of study through the recognition of credits (Cedefop, 2014). In Greece, the freedom of choice concerning specialities is constrained by the geographic morphology of the country and budget constraints. The country’s geographic dispersion across islands and rural areas results in smaller schools and therefore fewer available specialities.

Table 1 reflects these differences. In France, 46 students attend a specialisation which was not their choice, followed by 14 students in Greece and three in Italy. The boys who are not in a specialisation of their choice outnumber the girls in France. In Greece, this phenomenon seems to affect boys and girls equally. In Italy, only three boys are in an undesired specialisation, but boys outnumber girls in the sample population. The type of area is not important in France, as all schools are situated in the same region, and the type of area does not seem to be correlated in Italy and Greece.

Table 1. Composition of the sample in terms of gender, specialisation and type of area

	Gender		Specialisation		Not a choice by gender		Not a choice by area
	Boys	Girls	A choice	Not a choice	Boys	Girls	Urban	Rural
France N = 64	45	19	18	46	36	10
Italy N = 79	67	12	76	3	3	0	1	2
Greece N = 79	49	30	55	14	8	7	7	8

4. Data analysis

Five types of analysis were conducted for the data collected with the FTPS-AYA: a) the ANOVA on the inter-factor correlations of the scale, b) reliability analysis, c) descriptive statistics of the cumulative percentages for F1 and F2, d) the ANOVA for the correlations between F1 and F2 and the participants’ demographic characteristics (gender, age, specialisation and type of area) and e) a post-hoc Scheffe analysis for the Greek sample.

4.1. Findings

4.1.1. One-way ANOVA correlations between factors

Tables 2–4 present the results of the ANOVA conducted for the Italian, French and Greek populations of the sample. Table 2 reveals that the ANOVA for the French population group has good construct validity. However, it should be noted that the inter-factor correlation is weak between F3 and F4, between F6 and F2 as well as between F6 and F5.

Table 2. Correlations between factors, France

FRANCE	F1	F2	F3	F4	F5	F6
Future-negative (F1)	1	-,393**	,477**	-,166	-,359**	,362**
Future-positive (F2)	-,393**	1	-,273*	,612**	,612**	,047
Future-confusion (F3)	,477**	-,273*	1	-,089	-,260*	,245
Future-perseverant (F4)	-,166	,612**	-,089	1	,492**	,202
Future-perspiquity (F5)	-,359**	,612**	-,260*	,492**	1	-,020
Future-planning (F6)	,362**	,047	,245	,202	-,020	1

**. Correlation is significant at the 0.01 level (2- tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 3. Correlations between factors, Italy

ITALY	F1	F2	F3	F4	F5	F6
Future-negative, (F1)	1	-,542**	,480**	-,299**	-,451**	,112
Future-positive (F2)	-,542**	1	-,306**	,401**	,516**	,169
Future-confusion, F3)	,480**	-,306**	1	-,277*	-,217	-,113
Future-perseverant, (F4)	-,299**	,401**	-,277*	1	,403**	,130
Future-perspiquity, (F5)	-,451**	,516**	-,217	,403**	1	-,049
Future-planning, (F6)	,112	,169	-,113	,130	-,049	1

**. Correlation is significant at the 0.01 level (2- tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 4. Correlations between factors, Greece

GREECE	F1	F2	F3	F4	F5	F6
Future-negative (F1)	1	-,554**	,740**	-,469**	-,509**	,032
Future-positive (F2)	-,554**	1	-,493**	,540**	,607**	,155
Future-confusion (F3)	,740**	-,493**	1	-,369**	-,483**	-,021
Future-perseverant (F4)	-,469**	,540**	-,369**	1	,635**	,338**
Future-perspiquity (F5)	-,509**	,607**	-,483**	,635**	1	,163
Future-planning (F6)	,032	,155	-,021	,338**	,163	1

**. Correlation is significant at the 0.01 level (2- tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 3 reveals that the ANOVA for the Italian population group also has good construct validity. However, it should be noted that the correlation between factors is systematically weak between F6 and all the other factors of the scale.

Table 4 reveals that the ANOVA for the Greek population group has good construct validity. However, although the correlation between factors is medium to strong for nearly all the factors of the scale, the exception is the F6 factor, which presents a medium correlation with F4 and a weak correlation with all the other factors of the scale.

4.1.2. Reliability analysis using Cronbach’s alpha

Table 5 presents the results of the internal consistency reliability analysis. These results for the total scale reveal that although the reliability of the questionnaire could be acceptable for France (0.65), it is unacceptable for Italy (0.37) and Greece (0.42). In order to further explore the significantly low Chronbach’s alpha levels, the scale was broken into six parts, one per each factor, and the internal consistency reliability analysis was conducted by factor. In relation to the second reliability analysis, results showed that: a) the internal consistency reliability for F1 and F2 is good, ranging from 0.74–0.81 for all three population groups, b) the Chronbach’s alpha levels are poor for F4 in France and for F3 and F5 in Greece, c) F6 is unacceptable (<0.5) in all three population groups and d) there are equally unacceptable alpha levels for F3 and F5 in Italy and France. The low reliability of the F3, F4, F5 and F6 factors suggests a measurement error that excludes them from being taken into consideration. However, it should be noted that the reliability analysis conducted by the authors of the scale for China found that not only was the reliability good, but the alpha levels were significantly higher for the four factors in question.

Table 5. Cronbach’s alpha coefficient of the FTPS-AYA for Italy, France, Greece and China

Reliability Cronbach’s α	Future - Negative F1	Future- Positive F2	Future – Confusion F3	Future – Perseverant F4	Future – Perspicuity F5	Future – Planning F6	Total Scale
Italy	0.81	0.76	0.22	0.19	0.56	0.20	0.37
France	0.78	0.74	0.24	0.57	0.18	0.34	0.65
Greece	0.74	0.74	0.61	0.38	0.59	0.05	0.42
China	0.88	0.83	0.76	0.70	0.74	0.66	0.90

4.1.3. Cumulative percentages of the F1 and F2 factors of the FTPS-AYA

Given the results of the reliability analysis, this paper will focus only on the results concerning the F1 and F2 factors. Table 6 presents the cumulative percentages of F1 and F2 for the three population groups. Based on the percentages, three remarks can be made. First, percentages are quite similar across the three population groups in terms of students agreeing and strongly agreeing (51.9%–57.9%) with F2. Second, percentages are quite similar across the three population groups in terms of students neither agreeing nor disagreeing with F2 (27.8%–32.4%). Third, among the three countries, it seems that the above similarity is higher between Italy and Greece.

Table 6. Cumulative percentages of those who replied “I disagree & I strongly disagree”, “neither agree nor disagree” and “I agree & I strongly agree” in the F1 and F2 factors of the scale in Italy, France and Greece

	I disagree & I strongly disagree	Neither agree nor Disagree	I agree & I strongly agree
ITALY
Future-negative	72.3%	16.1%	11.5%
Future-positive	10.9%	32.4%	56.7%
FRANCE
Future-negative	53.1%	22.5%	24.4%
Future-positive	20.3%	27.8%	51.9%
GREECE
Future-negative	55.7%	28.8%	15.5%
Future-positive	12.9%	29.1%	57.9%

4.1.4 One-way ANOVA: correlation with demographic characteristics

The ANOVA was conducted for each country in order to explore the correlations between F1 and F2 and the four characteristics of our sample: sex, age, specialisation and type of area.

Table 7 presents the following three results of the one-way ANOVA in relation to gender. First, in France, there are statistically significant differences between boys and girls in the future-negative factor F_(1,63) = 4,565, p <.05)with boys having a negative perception of the future and girls not taking a clear position (neither agree nor disagree). Second, in Italy, there are no statistically significant differences between boys and girls. Third, in Greece, there are statistically significant differences in the future-positive factor (F_(1,78) = 8,291, p < .05)with boys having both a positive perception and a clearer image of the future and girls not taking a clear position (neither agree nor disagree).

Table 7. One-way ANOVA between gender and the F1 and F2 factors

	FRANCE		ITALY		GREECE
	Future-negative	Future-positive	Future-negative	Future-positive	Future-negative	Future-positive
F	4,324	1,383	,099	3,065	2,502	8,291
Sig.	,042*	,244	,753	,084	,118	,005*

*p < .05

Table 8 reveals that in relation to age, there are no statistically significant differences between the age of the students and the F1 and F2 factors for all three countries.

Table 8. One-way ANOVA between age and the F1 and F2 factors

	FRANCE		ITALY		GREECE
	Future-negative	Future-positive	Future-negative	Future-positive	Future-negative	Future-positive
F	,361	1,879	,873	,246	,879	1,362
Sig.	,782	,143	,422	,783	,351	,247

*p < .05

Table 9 reveals that in relation to specialisation, there are no statistically significant differences between the future-positive factor and the type of specialisation. However, in Greece, the ANOVA showed (F_(7,78) = 3,456, p < .05) that there are statistically significant differences between the specialisation and the future-positive factor. A post-hoc Scheffe analysis was conducted to determine which pairs of means are significant. The Scheffe Test revealed that students attending the specialisation of mechanics have a more positive perception of the future compared to students attending other specialisations.

Table 9. One-way ANOVA between specialisation and the F1 and F2 factors

	FRANCE		ITALY		GREECE
	Future-negative	Future-positive	Future-negative	Future-positive	Future-negative	Future-positive
F	2,240	1,964	2,544	2,284	,696	3,456
Sig.	,076	,112	,062	,086	,675	,003*

*p < .05

Table 10 reveals that in relation to the type of area, for France, there are no statistically significant differences, as all three schools are situated in nearby suburbs of Paris. The results for Italy also showed that there are no statistically significant differences between areas. It should be noted that although one school is situated in a rural area and the other is in an urban area, both schools are located in the same region. Schools in Greece are not only diversified in terms of their urbanity or rurality, but also because they are located in different regions. Results showed that the rural/urban area variable affects students’ responses in relation to the future-positive factor, with the students from rural areas reporting a more positive perception of the future (F_(1,78) = 7,728, p < .05).

Table 10. One-way ANOVA between the type of area and the F1 and F2 factors

	FRANCE		ITALY		GREECE
	Future-negative	Future-positive	Future-negative	Future-positive	Future-negative	Future-positive
F	,058	8,726	,119	,327	,990	7,728
Sig.	,811	,972	,731	,569	,323	,007*

*p < .05

5. Analysis of findings

The reliability analysis revealed high Cronbach’s alpha levels only for the future-negative (F1) and the future-positive (F2) factors. The levels were low or unacceptable for the future-confusion (F3), future-perseverant (F4), future-perspicuity (F5) and future-planning (F6) factors in all three population groups, as Chronbach’s alpha was not only below 0.6, but in some cases, significantly lower than 0.5. The extremely low Chronbach’s alpha levels indicate a measurement error that could indicate the scale’s lack of unidimensionality, as the poor interrelatedness between factors suggests either the existence of latent variables or that there are not enough questions to test the relevant factors.

However, the fact that Lyu and Huang (2016) reported a high reliability level (0.90) for the total scale suggests observing all the parameters in the findings that could be relevant to this measurement error. The first observation, which is related to the significance of the above measurement error, has to do not only with the number but mostly with the nature of the implicated factors. Besides the fact that it concerns four out of six factors of the scale, F3 (future-confusion) and F5 (future-perspicuity) concern the absence of a clear image of the future, and F4 (future-perseverant) and F6 (future-planning) concern properties related to the attainment of goals in the future. Considering that prospection refers to the ability to “pre-experience” the future by simulating it in the mind (Gilbert & Wilson, 2007; Suddendorf, Addis, & Corballis, 2009), it becomes evident that the measurement error in F3 and F5 is an indication that calls for further research, as it could reveal a problematicity related to the students’ abilities to carry out this simulation. Moreover, considering that the anticipation refers to the ability to project the short- and long-term consequences of actions and choices and to understand issues of the surrounding context (OECD, 2018a, 2019a, 2019b), the measurement error in F4 and F6 is also an indication that calls for further research, as it could reveal a problematicity related to the students’ abilities to anticipate.

The second observation is relevant to the parameter of prospection. The problematicity regarding the difficulty of prospection is, in my view, also reflected in a different form in the non-negligible percentages of students who responded “neither agree nor disagree” for F1 and F2 (Table 6). More specifically, although cumulative percentages indicated similar percentages of students having positive views about the future in all three countries (51.9%–57.9%), a far-from-negligible 27.8%–32.4% of our sample exhibited no clear perception of the future-positive factor, and 10.9%–20.3% exhibited a non-clear perception of the future-negative factor.

The third observation is relevant to the parameter of anticipation. The almost systematic lack of correlation of the F6 (future-planning) factor for all three population groups could also be one more indication of problematicity in relation to the function of anticipation. These three observations should not be considered as isolated symptoms, given the interwoven character between the functions of prospection and anticipation (Pezzulo & Rigoli, 2011; Setton, Fisher, & Spreng, 2019).

The fourth observation is related to the results of the ANOVA between F1 and F2 and the students’ socio-demographic characteristics. The findings presented in Tables 7-10 show that in reality, these socio-demographic characteristics, which are typical categories of correlation in the education field, do not describe systematic differentiations between students. However, if Tables 7-10 are read in relation to the data presented in Table 1, three observations can be made. First, Italy, which is the only country in which there is no significant correlation with the four socio-demographic characteristics, is also the only country in which 96% of the students attend the specialisations of their choice. Second, in France, the gender correlation (boys) with the F1 (future-negative) factor can be explained by considering that 80% of the boys in the sample are in a specialisation that they have not chosen. Third, in Greece, all three correlations reveal that the boys who have chosen the specialisation of mechanics and who live in rural areas are correlated with the F2 (future-positive) factor.

Two findings emerge if Tables 1 and Tables 7-10 are compared. First, in Italy, where the context provides students with the opportunity to choose their specialisations, there is no correlation with the socio-demographic characteristics. In France and Greece, due to contextual constraints, students have fewer opportunities to choose their specialisations. However, the correlations that emerge do not reveal a correlation between F1 and F2 and the socio-demographic characteristic; rather, they reveal a correlation between F1 and F2 and the opportunity to choose the specialisation. The importance of the availability of choice becomes more significant if it takes into consideration a) the academic profiles of students who choose VET and b) the attractiveness of VET itself.

In France, the VET status is low, and VET students are primarily students which overwhelmingly come from lower social classes and which have learning difficulties which emerged in primary school (Palheta, 2012; Jellab, 2016). In Italy, according to the National Institute for the Analysis of Public Policies (Istituto Nazionale per l’Analisi Delle Politiche Pubbliche [INAPP] (Franzosi et al., 2016, p. 10), VET also seems to attract academically “weaker” students, which creates a significant stigma for VET schools, which are considered the last option for those who failed in all the other schools (Cedefop, 2016, p. 99). In Greece, VET has always held little appeal for young people; it is associated with “laborious” and “inferior” manual labour, contrary to general education, which is associated with expectations of improved social standing (Cedefop, 2014, p. 17). Moreover, the latest Education and Training Monitor (2019) published by the European Commission confirmed that although important reforms have been adopted, the attractiveness of the sector is still low. Therefore, if we take into consideration that in all three national contexts, VET attracts the weaker students and is the less-attractive educational structure, having the freedom to choose their specialisations may be one of the few opportunities VET students have to position themselves in a relational and meaningful way in context and to achieve.

6. Dwelling in time and concluding remarks

The above section a) described possible explanations for the reported measurement error that could be related to students’ difficulties prospecting and consequently anticipating, b) argued that the F6 factor’s systematic lack of correlation with the other factors of the scale should not be considered random, c) related these two observations with the significant percentages of students who do not have a clear position on F2 in all three population groups, and d) revealed that neither the absence nor presence of correlations between F1 and F2 and students’ socio-demographic characteristics actually reflect the socio-demographic characteristics. Instead, there is an invisible correlation with students’ opportunities or lack thereof to choose their specialisations.

In terms of education scholarship, these findings, as described in the theoretical framework, would be considered from the perspective of the consequences the representations of the future have on students’ motivations, expectations, long-term planning, goal-setting and decision making. However, they take on a different relevance if they are explored from the perspective of “dwelling in time”.

If the above findings are considered in relation to the fact that mental representations of the future depend on the individual’s knowledge of the world and on how they perceive their place in it (Prabhakar, Coughlin, & Ghetti, 2016), the difficulty of prospecting and anticipating may reflect a difficulty of understanding and place-making. This idea, which is inherent in the definitions of prospection and anticipation described in the previous section, was previously addressed by Mills (1959, p. 5), who posited that people can understand and position themselves in the world only if they are able to position themselves in the present. Mill’s position was later repeated by Lynch (1960, pp. 2–3) in the urban context. Lynch introduced the term “legibility” to refer to the extent to which a user can “read” an urban environment and organise it in a coherent pattern. What Lynch added to Mills’ position is that this legibility does not depend solely on the user but also on the environment’s ability to convey a clear and recognisable mental image. It is this mental image, according to Lynch, that helps the individual to find their way in the city, to organise the information they receive and to guide their actions.

The invisible correlations of F1 and F2 with the choice of specialisation are a strong indication towards this direction. If considered that students of the sample are located in contexts of crisis and adversity, it becomes evident that the choice of specialisation is significant because it represents one of the few choices they can make in order to organise their immediate environment, to guide their actions through their choice and position themselves in the world. The opportunity to choose the specialisation summarises the five processes of resilience reported by Reyes (2013a, p. 14). Based on Reyes’ categories, students’ capability to choose their specialisation could contribute to their a) cognitive engagement, through making sense and finding purpose for being at school, b) emotional engagement, through engaging with something meaningful to them, c) proactive engagement, through developing a sense of control and competence in an area of their interest, d) connected engagement, through connecting better with their school environment, and e) committed engagement, though the opportunity to pursue their choice to develop responsibility to themselves.

In sum, under the approach of dwelling in time, the above-described four observations do not originate from difficulties related to students’ future time perspective, but from students’ difficulties achieving the functioning of dwelling in time. The manifestations of disadvantage are the same. However, placing the origins of disadvantage primarily in the difficulty to dwell in time brings changes to both the interpretation of disadvantage and the nature of the compensation measures to be taken. The invisible correlation of F1 and F2 with the choice of specialisation is a strong indication towards this direction. This invisible correlation indicates that a possible origin of educational disadvantage for some students would be either the lack of resource/commodity (in Greece) or the lack of capability (in France), to achieve the function of dwelling. Most importantly, this invisible correlation is valuable because it provides directions towards possible conversion factors that could be used in order to reverse this disadvantage and boost students’ resilience.

Under the perspective of dwelling in time, manifestations of educational failure are not interpreted as chronic accumulation of educational disadvantage. They are interpreted instead as a possible manifestation of a wider disadvantage related to students’ difficulties finding their place in the world, which starts as withdrawal from the immediate micro-context.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

Konidari Victoria is a former Marie Curie fellow in the FISPPA department of the University of Padua, in Italy, in charge of the Re-mapping research project funded by the European Commission (grant number 750405, http://remapping.upatras.gr https://re-mapping.eu/).

Notes on contributors

Konidari Victoria

Konidari Victoria is currently assistant teaching staff (EDIP) in the Department of Education and Social Work at the University of Patras. She has completed her PhD in the University Aix-Marseille I in France on the quality and knowledge management in educational institutions. Having received a Marie Sklodowska Curie scholarship  funded by the the European Commission (grant number 750405), she has worked as an autonomous post-doctoral researcher in the FISPPA department of the University of Padua, in Italy, in charge of the RE-mapping research project (http://remapping.upatras.gr). Her areas of expertise are educational quality, teachers' training, educational disadvantage, early school leaving, human geography in educational institutions and management of schools as learning organisations.