Five years after the Global Financial Crisis (GFC), high levels of youth unemployment remain the focus of intensive debate in many developed countries (Brotherhood of St Laurence, 2014; Featherstone, Reference Featherstone2013; ‘Generation jobless’, 2013). Off-shore transfer of low-skilled jobs to developing countries has contributed to this problem (Down, Reference Down2006), forcing governments to pay closer attention to the educational attainment and career aspirations of young people (Tomlinson, Reference Tomlinson2012). Australia has not escaped this phenomenon. While the national unemployment rate at the end of 2014 was 6.3%, the rate of unemployed 15- to 19-year-olds who are not in full-time education was 15.6%. Australia's most populous state, New South Wales (NSW), had the highest rate of youth unemployment 17.8% (Australian Bureau of Statistics, 2014). The research literature suggests that students from disadvantaged backgrounds, low attainers and early school leavers are the most affected (Dockery, Reference Dockery2010; Sikora & Saha, Reference Sikora and Saha2011; Tomlinson, Reference Tomlinson2012), leading to increased policy focus on these groups (Graham, Van Bergen, & Sweller, Reference Graham, Bergen and Sweller2015).
Government Response
Governments at both state and federal levels have sought to address low attainment, early school leaving and youth unemployment through policies designed to raise student aspirations and to increase knowledge about post-school options (Gale, Reference Gale2010; Gale & Parker, Reference Gale and Parker2015). One such example is the National Partnership Agreement on Youth Attainment and Transitions (Department of Education Employment and Workplace Relations, 2011), which aims to increase educational engagement and post-school transition to higher education, training and employment for 15- to 24-year-olds (Keating, Savage, & Polesel, Reference Keating, Savage and Polesel2012). One of the objectives of the agreement between the federal and state and territory governments is to achieve 90% Year 12 (or its equivalent) attainment nationally by 2015 (Council of Australian Governments, 2009), as a means to improving young people's capacity to enter the workforce or to engage in further education and training. Another related objective has been to achieve uniformity across the states and territories in terms of school leaving ages, while also extending the compulsory years of schooling. The aim of this change has been to increase participation in senior schooling, to drive attainment and qualifications, and to reduce early school leaving. As a result of this agreement, the NSW state government increased the age at which students can legally leave school from 15 to 17 years (Reid & Young, Reference Reid and Young2012), with some exceptions. Consequently, students in NSW are now required to remain in school until they complete Year 10, after which they must — until the age of 17 — either be:
• in school, or registered for home schooling; or
• in approved education or training (e.g., TAFE, traineeship, apprenticeship); or
• in full-time, paid employment (average 25 hours/week); or
• in a combination of work, education and/or training.
Impact of School Completion
Although some studies have shown that students who complete school fare better in the labour market than early school leavers (Applied Economics, 2002; Lamb & McKenzie, Reference Lamb and McKenzie2001), the majority of these studies are based on ‘the implicit assumption that if those who currently do not finish school were instead to go on and complete Year 12, they would then achieve outcomes similar to those observed for youth who currently do complete Year 12’ (Dockery, Reference Dockery2005, p. 39). Such an assumption neglects the contribution to outcomes made by individual characteristics, such as academic ability and career preferences, as well as the quality of the school education those individuals have received (Lamb, Walstab, Teese, Vickers, & Rumberger, Reference Lamb, Walstab, Teese, Vickers and Rumberger2004). In other words, the research that shows positive benefits for additional years of schooling is predominantly based on the effects that additional years of schooling have for those with the desire and capacity to benefit from them (Dockery, Reference Dockery2005).
Government policies that have been developed on the back of such studies, such as changes to the compulsory school leaving age, in a bid to increase retention, are also problematic. Not only do such measures assume that additional years of schooling will be of benefit to all students, but they leave important questions as to why some young people have difficulty in schools or in adapting to changes in the current labour market unanswered (Down, Reference Down2006). Policy discourse often sweeps such complexities away, but in doing so, glosses over the contribution that has been made by the quality and type of education that these young people have received. This is evident in the way in which the New South Wales Department of Education and Communities (NSW DEC) conceptualises the problem of youth unemployment in their statement on youth attainment and transitions:
‘Studies here and overseas have found that teenagers who leave school early are two and a half times more likely to be unemployed, earn lower wages and have poorer health. And every year in Australia, around 50,000 young people aged from 15 to 19 drop out of education and training and are unemployed. There was a time — and not that long ago, too — when a teenager could finish school at 15 and walk straight into a job or start a trade. But the times have changed. Technology has changed the nature of work, and 21st century teenagers now compete for jobs and study opportunities in a globalised environment against other young people in other countries. But the more things change, the more opportunities open up. A better education, more training and work experience means more choices for life after school’ (New South Wales Government Education and Communities, 2014, para. 3–8).
In the above excerpt, both education and social background are unexamined: education is accepted as universally ‘good’, while social background is not raised at all. Yet we know that the effects of ‘dropping out’ are different for different student groups (McMillan & Marks, Reference McMillan and Marks2003), with those from advantaged backgrounds more able to access post-school opportunities through more extensive social networks (Lamb & McKenzie, Reference Lamb and McKenzie2001). We also know that educational experiences differ between student groups (Duke, Reference Duke2000) for varied and complex reasons. For example, individual student characteristics — such as gender, culture, geographic location, and social and economic capital — can influence students’ interpretation of and relationship to the curriculum, as can the content of the curriculum and the ways in which it is taught, by whom and how well (Anyon, Reference Anyon1981; Apple, Reference Apple1972, Reference Apple2004). As one arguably influences the other over time, neither children nor education are ‘things’ that can or should be analysed as though the other does not exist or has had no impact. In other words, what children bring to school matters, but so too does the quality of what they experience when they are there.
Conceptual Framework
To better conceptualise and encompass this interplay between the individual and the educational context, this study is informed by Sen's (1995, 1999, 2004) theory of human capability, with a specific focus on the kinds of educational resources available to various individuals. Sen (Reference Sen1995) recognises the differences between individuals in terms of their ability to make use of the resources they can access. He argues that even when persons are provided with equal resources it does not necessarily mean that they have access to equal opportunities (Saito, Reference Saito2003). For instance, a disabled person will usually require additional resources to be able to have equal opportunities to a person without disability.
Not only are there differences between individuals but there can be differences in resources as well. School education is one such case in point. Unterhalter (Reference Unterhalter2003), for example, suggests that education and schooling should be distinguished from each other. In her conceptualisation, education is an abstract good, a concept that is operationalised through the process of ‘schooling’. Differences in this process and in the quality of schooling can lead to differences in learning outcomes. Therefore, while ‘education’ might play a significant role in capability enhancement (Walker, Reference Walker2005), schooling, as a process, can in some cases provide a poor quality of education. Lower quality schooling can even limit a person's capability by, for example, discouraging students from desiring things that are perceived as unattainable or by limiting their exposure to key information and advice. Lower quality schooling can also affect students’ academic achievements over time, leading to a loss of confidence and difficulties accessing the academic school curriculum. Such difficulties may eventually impact how students perceive their suitability for careers requiring degree qualifications, negatively affecting their future career aspirations.
The ability to envision and enact a viable career choice is thus a developmental outcome, one that is influenced by personal characteristics and background, as well as educational experiences. Student career choice ‘capability’ should therefore be analysed by placing ‘equal emphasis on the interaction between individual students and the nature and quality of the education they receive’ (Galliott & Graham, Reference Galliott and Graham2014, p. 280). To do this, we examined students’ relative freedom to achieve career determination by measuring differences in the personal (individual background) and educational resources (educational experiences) that have been made available to them. We began this process by investigating which students reported the most difficulty in career determination and what personal factors were of most influence.
Method
Sample
The target population of our cross-sectional survey was secondary school students attending Years 9–12 in New South Wales, Australia. In 2011, there were nearly 159,000 students studying in Years 9–12, including full-time and part-time students in government and non-government schools in NSW (Australian Bureau of Statistics, 2010). Following the recommendations of Krejcie and Morgan (Reference Krejcie and Morgan1970), we required a sample size of not less than 384 respondents to reach a 95% confidence level with a 5% margin of error. However, as previous international research findings in the area of career choices counted non-response rates of up to 30% (Sikora & Saha, Reference Sikora and Saha2009), we aimed for a minimum of 500 respondents.
Participants
Participating schools were selected using stratified simple random sampling. NSW is Australia's largest state, comprising one third of the national population. In 2011, there were 1,128,317 school-aged students, 34% of whom attended non-government schools (Australian Bureau of Statistics, 2013). The government school sector educates 66% of the K–12 schooling population, with over 744,392 students (Australian Bureau of Statistics, 2013). In an effort to represent this split between government and non-government enrolments, we invited eight government schools and four non-government schools from both urban and rural areas of NSW to participate in the study.
To ensure participation of students of different socioeconomic backgrounds, we drew on the Index of Community Socio-Educational Advantage (ICSEA), developed by the Australian Curriculum Assessment and Reporting Authority (2012). Each school in Australia has an ICSEA value on a scale ranging from 500 ‘extremely educationally disadvantaged backgrounds’ to 1300 ‘very educationally advantaged backgrounds’. The scale has a mean of 1000 and a standard deviation of 100 (Australian Curriculum Assessment and Reporting Authority, 2012) with the majority of Australian schools in the 801–900, 901–1000, 1001–1100 and 1101–1200 bands. Potential schools with ICSEA values within these bands were invited to participate (see Table 1).
Percentage of Participants Completing the Survey by Type of School
The final sample included 706 students in Years 9–12 from 12 schools: six schools were within +/− one standard deviation of the mean (three schools just below and three schools just above) and six were within +/− two standard deviations (two schools below and four schools above) of the mean (see Table 1). Students responded to the survey in the period from October 2012 to December 2012 during the final term of the school year. The sample was representative of enrolment share between the government and non-government sectors, with 65.3% of respondents from government schools and 34.7% of participants from non-government schools. Two of the government schools were academically selective,Footnote 1 comprising 28.6% of respondents. All four non-government schools were single sex (13.7% from boys’ schools and 21% from girls’ schools). The remaining schools were co-educational (see Table 1).
Figures 1 and 2 demonstrate age and year grouping compositions of the sample. The majority of the respondents were 15–17 years old with the largest proportions of them being in Years 10 and 12 and the least involved students being from Year 11. The majority of the respondents were female (64.4%), were born in Australia (81%), and had one or both parents born overseas (60.9%). The most popular language spoken in the respondents’ homes was English, with 59.1% reporting speaking English only and 26.8% speaking English and other language(s). The main language groups other than English were Asian (24.5%), Middle Eastern (9.5%), European (3.4%) and Pacific (3.3%).
Age of respondents in years.
Survey respondents by year grouping.
Instrument
The online survey contained an information cover sheet, 29 main questions that were compulsory for all participants, and 37 ‘skip/display logic’ questions that were displayed (or not) to students depending on their previous response. A variety of scales, such as nominal, interval and ratio, were used. In addition, some open-ended response formats were added. Variety in the response scales and creativity of visual illustrations (when appropriate) increased the overall quality of the survey instrument, making it more interesting and engaging for participants (Burns & Bush, Reference Burns and Bush2010), with the intention of positively affecting the participation rate and reducing drop-outs. This article aims to provide analysis of the survey variables related to participants’ career decisiveness and their personal circumstances in relation to the following constructs:
Career certainty was assessed by two variables. Students were first asked ‘What would you like to do after finishing school?’ The participants could select from get a job, get an apprenticeship, go to TAFE/college, go to university, not sure, nothing, or other with the comment box. Later in the questionnaire students were asked an additional question ‘How much certainty do you have in choosing your future career?’ and could respond on a sliding bar: ‘I’m this much sure of what I want to do: 0%–100%’.
Demographics of participants were examined through a number of questions related to students’ home language, age, school grade, gender, participants’ and their parents’ place of birth, as well as parents’ occupational status (whether each parent had a job and if ‘yes’, what he/she did in their job).
Self-assessment of own academic achievements was assessed through a single item question ‘How would you rate your achievements in studying compared to other students of your year/grade?’ Students were asked to self-report whether they were at the top, middle or bottom of their year/grade in terms of their achievements in studying comparing to other students.
Perceived problem-solving and goal orientation abilities (as part of perceived self-efficacy) were assessed utilising three career determination related variables of the larger General Self-Efficacy Scale (Schwarzer & Jerusalem, Reference Schwarzer, Jerusalem, Weinman, Wright and Johnston1995). The variables were selected based on Alkire's (Reference Alkire2005) recommendations for human agency measurement and included factors associated with effort investment (‘I usually can solve difficult problems if I make enough effort’), creativity in problem solving and persistence in face of barriers (‘I can usually think about several possible solutions when dealing with problems’), and self-determination (‘Usually it's easy for me to follow my goals and work towards achieving them’). Responses ranged from strongly disagree (1) to strongly agree (6).
Procedure
Following ethics approval by the sponsoring university and approval to conduct research in NSW government schools from the NSW Department of Education and Communities, principals of 34 schools within the chosen strata were contacted.Footnote 2 Consenting principals arranged for their career advisers to liaise with the research team and to pass information about the study to students and their parents or guardians. Students and their families received written information about the survey via school letters and emails. Involvement in the study was voluntary and parents could withdraw their child/children from participation at any point.
Data Analyses
The survey was conducted online using Qualtrics software, which is similar to Survey Monkey, and is designed for online survey design and administration. Data were exported into IBM SPSS Statistics 22 software for further statistical analysis. Out of 706 respondents, 80 participants dropped out of the questionnaire prior to its full completion and their responses were not included in the statistical analysis. Responses to three open-ended questions were coded categorically: (1) postcodes were coded using the Socio-Economic Indexes for Areas (SEIFA) developed by the Australian Bureau of Statistics (Pink, Reference Pink2013); (2) fathers’ and mothers’ occupations were coded using the Australian Bureau of Statistics’ occupational categories (Australian Bureau of Statistics, 2011) and the Australian Socioeconomic Index 2006 (AUSEI06) (McMillan, Beavis, & Jones, Reference McMillan, Beavis and Jones2009); and (3) responses to the question ‘What would you like to do when you finish school’ were coded as ‘certain’ or ‘uncertain’. We then performed descriptive statistics as well as inferential tests to examine the association between career certainty and a variety of other variables. Chi-square tests of independence were used when variables were categorical, and independent samples t tests were used when variables were continuous. When significant differences were found for the overall chi-square tests, follow-up tests comparing the column proportions were conducted, using a z test with Bonferroni adjustment for multiple comparisons (Hochberg, Reference Hochberg1988). This procedure allowed us to control the false discovery rate at alpha = .05 and to identify on which questions the proportions of career certain versus career uncertain participants differed for each response category. Measures of effect size are given by Cohen's d for t tests and by Cramér's V for chi-square tests. Odds ratios are given for significant chi-square tests.
Results
From the total 626 participants who fully completed the questionnaire, 582 expressed a clear intention regarding their post-school career plans and 44 did not. The relationship between the certain/uncertain grouping and students’ own articulation of certainty in career decision making was examined further using an independent samples t test comparing participant responses on the question ‘How much certainty do you have in choosing your future career? Please indicate on a sliding bar . . . (0%–100%)’. Students in the uncertain group had significantly lower certainty in their career choice, M = 38.73%, SD = 31.14, compared with those who said they had made a decision about what they would like to do after finishing school, M = 64.68%, SD = 29.27, t(624) = 5.65, p < .0005, d = 0.86 (a large effect; Cohen, Reference Cohen1988).
Demographics
Characteristics of participants
Relationships between career certainty and participant characteristics were examined using chi-square and independent samples t test analyses. Table 2 gives the results of the chi-square tests, where the only significant relationship was found between language spoken at home and career indecisiveness. Follow-up tests showed that a higher proportion of career uncertain than career certain students spoke only English at home, whereas a higher proportion of career certain than career uncertain students spoke English and another language at home (both p < .05, small effect size). Based on the odds ratio, the odds (or risk) of being career uncertain was 3.22 times higher for students speaking English only at home compared with those using English and another language. There was no difference between the proportion of career certain and career uncertain students who spoke only a language other than English at home (p > .05).
Number (and Percentage) of Career Certain and Career Uncertain Participants by Various Students’ Characteristics
Note: *p < .05.
Students’ age, year group, gender, location of birth (both participants’ location of birth and that of their parents), and employment status of father and mother were not significantly associated with career (un)certainty (see Table 2). Note that five participants who indicated they did not know where their parents were born were excluded from the analysis of parents’ birth country.
In order to examine socioeconomic status (SES), we used students’ postcodes and parents’ occupations as proxy indicators. We drew on the coded postcode data to examine the relationship between the SES of the area where participants lived and their career certainty using independent samples t test; however, the relationship between these variables was not statistically significant, t(616) = 1.905, p = .057.
Relationships between career certainty and parental occupation SES were also examined with independent samples t tests. Students in the career uncertain group tended to have lower mothers’ SES scores, M = 56.19, SD = 24.47, compared with career certain participants, M = 66.08, SD = 22.18, t(624) = 2.013, p = .045, d = 0.24 (small effect size). There was an even stronger association between fathers’ SES scores and career (un)certainty, whereby uncertain students had fathers with lower SES scores, M = 43.24, SD = 24.52, compared with career certain students, M = 58.34, SD = 25.30, t(624) = 3.161, p = .002, d = 0.61 (medium effect).
Self-Assessment of Academic, Problem-Solving and Goal-Orientation Abilities
The relationship between students’ beliefs regarding their academic achievements and career certainty was analysed using a chi-square analysis comparing participants’ ranking of themselves as being in the top, middle or bottom of their year/grade academically. Being career uncertain was significantly related to self-evaluation of academic achievement in comparison with other students, χ2(2) = 19.25, p < .0005, Cramér's V = .175, small to medium effect size. While 49.5% of career certain students ranked themselves in the top third of their year, only 22.7% of career uncertain students did the same (p < .05). Conversely, a significantly higher proportion of career uncertain students (18.2%) ranked themselves in the bottom third of their year, compared to just 5.2% of students in the career certain group (p < .05). The risk of being career uncertain was therefore 7.68 times higher for students ranking themselves in the bottom third of their year academically than for students ranking themselves in the top third of their year. There was no difference in the proportion of career certain versus career uncertain students who ranked themselves in the middle third of their year (45.4% and 59.1% for career certain and uncertain respectively, p > .05).
The relationship between career certainty and self-assessed problem-solving skills and goal-orientedness were assessed via responses to three items (listed in Table 3) relating to effort, ingenuity and determination. After comparing levels of agreement on a six-step agreement scale, group responses were then examined using independent samples t tests. Students in the career uncertain group were significantly less likely to agree with all three statements than students in the career certain group (medium size effect).
Self-Assessment of Problem-Solving Skills and Goal Orientedness of Career Certain and Career Uncertain Students
Note: *p < .05. **p < .0005.
Discussion
The goal of the study on which this article was based was to investigate the influences of individual backgrounds and educational experiences on students’ career choice capabilities. This article engages with the first half of the project aims by examining the influence of personal factors, including students’ socio-demographic backgrounds, self-assessment of own academic achievements, and perceived problem-solving and goal-orientation abilities (as part of self-efficacy). Findings revealed significant differences between students in terms of career readiness. When compared, career uncertain students were more likely to speak English as their only home language, while career certain participants were more likely to speak other languages when at home. There were no differences in parents’ participation in employment; however, a significantly higher proportion of career uncertain students reported parental occupations associated with lower SES than career certain participants. Another significant difference between the two groups is that career uncertain students provided lower self-ratings of academic, problem-solving and goal-orientation abilities. We will discuss each of these findings in turn.
Language Background
This is not the first study to find an association between students’ language background and post-school aspirations. For example, research into attitudes to school and post-school intentions that draws on data from the Longitudinal Survey of Australian Youth (LSAY) has found that students from a language background other than English were more likely to participate in Year 12 and more likely to continue on to university (Khoo & Ainley, Reference Khoo and Ainley2005). One explanation for this is provided by Dockery (Reference Dockery2005), who explains that ‘increases in the proportion of migrants entering Australia under the skilled migration category has resulted in a pool of recent settlers who have relatively strong preferences for higher educational attainment for their children’ (p. 17). Therefore, while it might be assumed that English language-learner status is a disadvantage, in some cultural groups this disadvantage is more than outweighed by a highly effective disposition towards academic learning (Mu, 2014).
For example, in their analysis of the relation of ethnicity and disposition to academic achievement, Noble and Watkins (Reference Noble and Watkins2009) found that various socio-cultural groups have different approaches to learning. They argue that different cultures encourage certain home and school practices, such as self-discipline and ‘stillness’, which can foster academic achievement and goal-orientation. Watkins and Noble (Reference Watkins and Noble2008) found that Chinese students were required to do more academic work outside the school and were more physically disciplined or ‘still’ during class, in comparison with Anglo and Pacifica children (the other two ethnic groups in their research project). This highlights that schooling does not necessarily provide the same benefit to everyone and that students with certain educational practices promoted at home may receive more value from their school education than children who do not receive the same level of support.
Our results resonate with these findings. The largest percentage of students from a Language Background Other Than English (LBOTE) in our sample were students from an Asian language background (24.44%); however, only 4.55% of students in this group were uncertain as to what they want to do when they leave school, compared to 14.13% of students who only spoke English. Following Watkins and Noble's (2008) recommendation that researchers focus on the educational practices encouraged by different ethnic groups rather than ethnicity itself, it is important that future research investigates the kinds of career-related educational experiences that students of various cultural backgrounds have at home and school.
SES of Students’ Families
When we looked at students’ SES, defined by their residential postcodes, there were no differences between the two groups. Therefore, instead of using a geographical approach, we identified the SES of students’ families based on parental occupations. There was a significant relationship between career uncertainty and having parents with occupations associated with lower SES. This highlights not only the complexity of SES as a construct, but also that family background, with its specific values and expectations, plays a much greater role in career determination than place of living. Recent research has demonstrated, for example, that different schools in the same postcode area attract different kinds of students, with public schools often serving the most disadvantaged students (Reid & Young, Reference Reid and Young2012). Thus, families with different values, although living in the same suburb, are likely to send their offspring to different schools.
The relationship between SES and career certainty seems to be more complex than it looks at a first glance, with some research findings varying significantly. Gore, Holmes, Smith, Southgate, and Albright (Reference Gore, Holmes, Smith, Southgate and Albright2015), in their recent research involving both primary and secondary school students, found no relationship between SES (measured by parental occupation and education) and the occupational certainty of students. Wilson (Reference Wilson2011), however, in her US-based study with Year 10–12 high school students, found that SES (measured by parental occupation and education), while not having direct effect on occupational expectations, was nevertheless indirectly affecting occupational expectations through perceived parent expectations in relation to their offspring's educational achievements.
Parents of higher SES were particularly associated with higher educational expectations and the latter had a direct effect on occupational expectations. In addition, in her exploratory analysis, Wilson (Reference Wilson2011) found that parents’ expectations, together with academic achievements, explained students’ career compromise. This might also explain our finding that career uncertainty was associated with both lower SES based on parental occupation and lower self-assessed academic achievements of students, a finding that we will examine further in the next section.
Given the potential for students from lower socioeconomic backgrounds to experience more difficulty in making a career choice, it is vital that schools provide appropriate career information and guidance not only to students, but also to their parents or guardians, communicating the relevance of subject content to the world of real career opportunities and arranging access to various career role models. This should be pursued in a culturally appropriate, respectful and engaging way to ensure use and application of the received information.
However, research evidence shows that schools often reproduce existing social differences by teaching what is thought to be relevant for different student groups (Nolan & Anyon, Reference Nolan, Anyon, Dolby and Dimitriadis2004). The problem arises when what used to be relevant is no longer applicable in a modern job market, or when different students receive different information. Future research should therefore investigate the kinds of career-related experiences, school subjects and career role-models that ‘career uncertain’ students have access to, as well as their level of satisfaction with available choices.
Self-Assessment of Academic, Problem-Solving and Goal-Orientation Abilities
The final group of significant findings provided in the results section of this article identified that career uncertain students ranked their current academic, problem-solving and goal-orientation abilities lower than their career certain peers. There may be various reasons for lower self-assessment of academic performance and these personal reflections might not necessarily represent a full picture of the actual abilities of the individuals involved.
According to Correll (Reference Correll2001), ‘widely shared cultural beliefs attached to various tasks’ not only affect self-evaluation in occupationally relevant activities, but also ‘how individuals are channeled into particular activities and subsequent career trajectories by others’ (p. 1725). In her longitudinal study following young people from Year 8 to two years post-school completion, Correll (Reference Correll2001) found that certain groups of students assess their academic achievements lower than others, regardless of their actual performance. This, she found, was due to certain societal expectations and cultural beliefs relevant to the student groups identified in her research.
Whether accurate or not, subjective judgment of academic achievement has been found to influence students’ attitudes to school, post-school aspirations and/or decisions to leave school early (Marks, Reference Marks1998). Interestingly, Marks found significant differences between states, with students going to school in the Australian Capital Territory, Victoria, Western Australia and Tasmania reporting higher levels of self-rated achievement than students in NSW, which suggests that ‘states somewhat differ in the perceptions that they give to students on how well they are doing’ (Marks, Reference Marks1998, p. 10). This may well have been an effect of the NSW academic curriculum — which, before national adoption of the Australian Curriculum, was widely acknowledged to be the most highly defined and prescriptive in Australia (Luke, Graham, Sanderson, Voncina, & Weir, Reference Luke, Graham, Sanderson, Voncina and Weir2006) — together with that state's early adoption of standardised assessment and reporting (Bruniges, Reference Bruniges2001).
Although Marks (Reference Marks2010) has found that student ability — as measured by literacy and numeracy in Year 9 — has greater impact on university entrance and participation than student socioeconomic background or course type, student ‘ability’ is additive. As we described in our introduction, academic ability is affected by what students have and have not learned at school over time, by the ideas and concepts to which they have been introduced, and how well those ideas and concepts have been taught. While our findings suggest that self-perceptions of academic achievement (or ability) and family background (which includes cultural norms and family educational practices) bear greater influence than the individual characteristics (such as age and gender) that are often assumed to have the greatest weight, each of these is arguably affected by prior educational experience. Further research is therefore needed to determine whether there are differences between ‘career certain’ and ‘career uncertain’ groups in terms of their educational experiences, as well as in the quality and relevance of those experiences. It is also important to identify and utilise information in regard to what interests less academically inclined students in terms of school curriculum and the world of work.
Recommendations
As our findings indicate a strong influence of family background on students’ career determination, we recommend that schools:
• work to build closer relationships with students’ families and surrounding communities through career guidance meetings, career information expos, social activities and interactions with local business;
• educate students and their families about the best home practices for developing high academic achievements, problem-solving and goal-orientation abilities;
• provide career guidance to both students and their families in creative and engaging ways for various cultural and socioeconomic groups, perhaps with provision of real life examples and role models, especially for those with less social capital and networking opportunities;
• implement programs that allow for mixing of different groups of students and opportunities for students to exchange their ideas and experiences of learning strategies and career pathways.
Working with families can potentially provide students with a better navigational capacity in the world of post-school career trajectories and help them to achieve better understanding of the connections between what is studied at school and real-life career opportunities. However, knowing that career uncertain students tend to rate their academic, problem-solving and goal-orientation abilities lower than career certain students, it is important that we:
• understand what kind of career education experiences are made available to young people, the comparative quality of those experiences, and their relevance to different student groups;
• identify and support the educational and career interests of less confident or academically capable students, as they now have to remain for longer at school due to current educational policies.
Limitations
There were a number of limitations to this study. First, the study was conducted only with students from one state of Australia (NSW), and these findings may not hold for students in other states or countries. Second, students attending Catholic systemic schools, while representing 18.2% of the NSW school education sector, were not present in our data set. In an attempt to address this issue, two Catholic non-systemic schools were included in the sample; however, it has to be acknowledged that the opinions of the students in those schools may vary from those in Catholic systemic schools. Third, the participants of the survey self-selected. It is possible that students who were most struggling with career determination might consider that participation in a ‘career choice’ survey is not for them and therefore chose not to participate. Fourth, online questionnaires, while being a very time-saving and flexible tool for researchers, might not be the best option for all participants. This may be partly because some students might have limited access to the internet and digital devices such as computers and smart phones; and partly because for some students, due to different levels of academic achievement, oral responses may have been preferred. Finally, the data of the survey was self-reported by students, which unavoidably brought a certain level of subjectivity. In order to maximise the authenticity of responses, we ensured anonymity and confidentiality of all information being provided.
Conclusion
Young people currently face a number of challenges in their transition from school to work, study or training. While both financial and labour markets are experiencing radical transformation, there is little evidence that educational systems are successfully adapting to these challenges. Despite attempts to raise young persons’ aspirations and to broaden participation in higher education, a sizeable minority of high school students have difficulty imagining their future career trajectories. Government initiatives encouraging potential early school leavers to remain at school for an increased number of years do not necessarily improve occupational outcomes for those young people. This leaves the effectiveness of such initiatives questionable and urges for a better understanding behind reasons and factors associated with career indecisiveness.
Recent research findings show that not all students equally benefit from the increased number of years of schooling. Our study demonstrates no increase in career certainty associated with an increase in the number of years spent at school. We did find, however, some demographic characteristics common for career uncertain young people in our sample. Those students who were significantly more uncertain in their post-school careers were more likely to have English as the only language spoken at home, and their parents were more likely to work in occupations associated with lower SES. Career uncertain students were also significantly more likely to rate themselves lower in academic, problem-solving and goal-orientation abilities.
Acknowledgments
The authors would like to thank the 706 participants of the survey as well as the liaising staff members of the 12 participating NSW schools. We are also grateful to Macquarie University for financial support.
