Career Goals and Internship Quality Among VET Students

Abstract

Given the increased self-directedness of todays’ career environment, career goals represent to some extent the exercise of individual agency, particularly during ecological transitions (e.g., school to work). The main purpose of this study was to analyze the relationship between internship quality and career exploration behavior, considering students’ career goals content (labor market vs. higher education). Using a longitudinal design (pre- and post-internship), we conducted a study (12th grade; N = 191) that explores the relationship between perceived qualities of the internship and the different dimensions of career exploration. Analysis of variance and analysis of covariance, with repeated measures, were used to analyze the data. The results reinforce the importance of career goals, since they seem to have a differentiating effect on how the quality of the internship interacts with students’ career exploration behavior. Finally, the implications of these findings for career interventions and for future research in this area are discussed.

Keywords

career goals internship quality career exploration career development vocational education and training

Workplace learning experiences have become an important feature of training programs within high school vocational courses (vocational education and training [VET]) and are also considered the main strategy for linking education with occupational realities (Comyn & Brewer, 2018; Griffiths & Guile, 2004). However, because adolescents may address work experiences in diverse ways (Schaap et al., 2012), the impact of these experiences on their career development is not uniform (Gamboa et al., 2014). Therefore, VET researchers should recognize that real-world experiences, such as internships, are different for each individual because young people are not a homogenous population in regard to career behavior and coping strategies (e.g., Germeijs & Verschueren, 2006; Paixão & Gamboa, 2017). In this context and taking into account the suggestions of authors such as Creed and Patton (2003), VET research needs to focus analyses on the complex interactive processes that are established between students’ proactive career behaviors and their significant learning environments. In the career domain, goals represent control over career development and career behavior. Studies indicate that students who have forged clear and purposeful career goals are more likely to be successful in their school-to-work transition (Dietrich et al., 2012; Hirschi & Vondracek, 2009; Melvin, 2017). Additionally, given the increased self-directedness of today’s career environment, career goals represent, to some extent, the exercise of individual agency (Lent & Brown, 2013); this is particularly true during ecological transitions, which almost always require active regulation of behavior due to the discontinuities that individuals experience when moving from a familiar context (e.g., school) to a less familiar context (e.g., internship) (Nurmi et al., 2002; Vondracek & Porfeli, 2008). However, despite the theoretical expectation and the empirical evidence that career goals are pivotal to vocational behavior and development (e.g., Dietrich et al., 2012; Hirschi & Vondracek, 2009; Kracke & Schmitt-Rodermund, 2001; Nurmi et al., 2002), as far as we know there is a lack of empirical research, based on longitudinal analyses, that addresses how internships quality affect career management behaviors, such as career exploration, considering students’ short-term career goals content (e.g., entering the workforce vs. higher education).

Career Goals and Career Exploration

Goals and plans are cognitive-motivational constructs that play an important role in the self-regulation of behavior, affecting overt behavioral processes (Reeve, 2015). According to Lent et al. (1994), goals are ubiquitous elements in career decision-making theories, and their behavioral role is expressed in several concepts, such as core goals, aspirations, and career plans—when they involve specific intentions. More recently, in the career self-management model (Lent & Brown, 2013; Lent et al., 2016), in regard to career exploration and decision-making, it is hypothesized that exploratory goals contribute directly to exploratory actions or behaviors. Under this model, the exercise of adaptive career behaviors (e.g., engaging in career exploration) is assumed to be affected (directly and indirectly) by cognitive person variables (e.g., career goals) and environmental supports and barriers. In this sense, as a self-determination mechanism, goals determine an individual’s intentions to perform particular adaptive career behaviors, namely, in regard to the direction, frequency, and persistence employed in self and occupational information gathering. Additionally, according to social cognitive career theory (SCCT) (Lent & Brown, 2013; Lent et al., 1994), people are more likely to set and implement goals to engage in career exploration when they receive environmental support (e.g., colleagues and supervisors). On the other hand, career exploration literature emphasizes the importance of being aware of the goals that organize the exploratory process, and the literature considers this a central component of the level of intentionality involved in exploratory behavior (Blustein, 1989; Jordaan, 1963; Roger & Creed, 2011; Taveira, 1997). Career exploration is also described as a process that integrates self-determination mechanisms, such as goals and aspirations, into career development (Flum, 2015; Flum & Blustein, 2000), across the life roles and in any life stage (Blustein, 1997). In this light, by considering goals as antecedents of exploration, we can better understand how young people regulate their behaviors as they incorporate information from the world around them and evaluate their learning and occupational life experiences.

In line with previous theoretical assertions, there is growing empirical evidence that career goals can determine the quality and quantity of exploratory activity, leading, in most cases, to the construction of new career-related endeavors. Patton et al. (2004) showed that the career goals of high school students predicted their exploratory activity. More precisely, the results suggested that the students who have set goals are more likely to engage in career development behaviors, such as career planning and career exploration. Similarly, Rogers et al. (2008) conducted a study with high school students (10th, 11th, and 12th grades) and concluded that having a clear set of career goals significantly predicts variance in career exploration. Under the conceptual framework of the social cognitive model of career self-management, Lent, Ireland, Penn, Morris and Sappington (2017) found an association between goals and career exploration (self and environment) in a sample of university students.

In a 6-month longitudinal study (T1–T2), Kracke and Schmitt-Rodermund (2001) observed a significant interaction between career goals content (T1) and the observed change in career exploration. In this case, the students planning to pursue studies in higher education showed reduced exploratory activity, while the remaining students (apprenticeship, apprenticeship and higher track school, and not decided) maintained their levels of career exploration. In summary, from our point of view, there is now a great deal of empirical evidence suggesting that goals, among other self-regulatory variables (e.g., career self-efficacy, outcome expectations), are significant predictors of career exploration behavior.

Internship Quality and Career Exploration

According to the existing research, internships and other workplace-based experiences can promote students’ career development (Binder et al., 2015; Gamboa et al., 2013, 2014; Kuijpers et al., 2011; Sekiguchi, 2012) and thereby facilitate their school-to-work transitions (Pan et al., 2018; Silva et al., 2013; Yi, 2018). In this context, career exploration can help promote reflexivity (Savickas, 2016), which is conceived as an action-oriented reflection that helps individuals define new perspectives, namely, when they are facing transitions involving relevant learning experiences in real work contexts (Blustein, 1997; Jordaan, 1963; Lent & Brown, 2013). However, it seems that this construction process depends not only on the attitudes and competencies of the explorer but also on the conditions that specific context offers (e.g., task diversity, experienced autonomy, supervision, social relationships; Blustein, 1997; Ryan & Deci, 2000; Van Vianen et al., 2008). Among the contextual qualities that the literature considers as essential to promote career exploration, the importance of the relational dimensions found in real work contexts should be highlighted (Flum, 2015; Flum & Blustein, 2000). In general, the relational and sociocognitive approaches to career development advocate that the support provided in work contexts not only assists in reducing anxiety and stress but also stimulates the search for new learning strategies, increases confidence in attempting new tasks, and facilitates the sharing of experiences with coworkers (Kenny & Bledsoe, 2005; Vignoli et al., 2005). On the other hand, in addition to being supportive, learning contexts should be challenging, promote autonomy, present an appropriate degree of structure, and allow for the acquisition of new skills (Ryan & Deci, 2000). Given the above, and in line with the relational (Flum, 2015; Flum & Blustein, 2000) and contextualist career approaches (Vondracek & Porfeli, 2008), it appears that the results of the training experience depend upon the interaction between the learning opportunities afforded by the working context and the individual student’s skills and attitudes, namely, in the career exploration domain. Gamboa et al. (2013, 2014) reported a positive link between the internship quality (e.g., autonomy, colleagues’ feedback, social support, learning opportunities, supervisor training, supervisor support) and the career exploration undertaken by high school students participating in a vocational education placement program. Research also suggests that internship quality has a positive and significant impact on students’ entrepreneurial intentions (Yi, 2018) and job search success (Pan et al., 2018). Taken together, empirical research results demonstrated that internship quality is related to adaptive career behaviors (e.g., career exploration and career planning) and adaptive career outcomes (e.g., job search behavior, career success).

Present Study

In the VET system in Portugal, internships are mandatory and, in most cases, occur in the final year of the program. In the secondary level of education (10th, 11th, and 12th grades, usually 15–17 years old), VET courses provide a broad range of training. The main purpose of these courses is to give access to the labor market (Level 3 vocational qualification), but these courses also allow students to pursue their studies at a higher level (diploma in secondary education). Internships are considered a workplace learning activity in which students experience work tasks in work environments, without assuming the full identity of a worker (Watts, 1996), while under the supervision of an experienced professional. In Portuguese public schools, the choice of internship work placements results from partnerships that exist between schools and reliable work institutions, in accordance with national standards provided by the Ministry of Education. In the present longitudinal study carried out with VET interns, we investigated the relationship between internship quality and career exploration, considering the students’ career goals content (entering labor market vs. pursue studies in higher education; T1). With two moments of data collection (T1–T2), the present study aims to overcome the limitations of cross-sectional studies, which do not allow the attribution of the observed within-subject changes in career exploration to the participation in an internship. Additionally, the repeated measures design contributes to clarifying the role of internship quality on career exploration outcomes. Thus, in light of the literature reviewed, we first expected that the impact of the internship on the career exploration of students would depend on the career plans reported at the beginning of the internship (T1; e.g., Kracke & Schmitt-Rodermund, 2001; Mortimer & Zimmer-Gembeck, 2007; Nurmi et al., 2002). Actually, as mentioned earlier, career goals content can contribute to the intraindividual trajectories of career exploration. For VET students, the transition to higher education can significantly increase exploratory activity because the adaptation of the students seems to depend on the information they are able to collect regarding the new learning context (e.g., Jordan, 1963; Kalakoski & Nurmi, 1998). Additionally, VET students face a vast number of challenges in access to higher education, such as low academic performance, high drop-out rates, and low scores on admission exams (Farías & Sevilla, 2015; Hayward & Hoelscher, 2011; Holm et al., 2013). More specifically, (Hypothesis 1) we expected that students wishing to enter higher education, at Time 1, would exhibit a more pronounced increase in career exploration behavior (e.g., self, environment, and amount of information) and stress (e.g., decision-making and exploration) than those students who wanted to immediately join in the labor market.

Second, the impact of the internship experience on career exploration would depend on the quality of the learning context in which the students participate (e.g., Gamboa et al., 2013, 2014; Kuijpers et al., 2011; Pan, 2018; Van Vianen et al., 2008; Yi, 2018). Therefore, (Hypothesis 2) we expected that the diversity of tasks, autonomy, learning opportunities, and technical and emotional support provided by the supervisor would relate positively to career exploration behavior, beliefs, and satisfaction and negatively to career exploration stress and decision-making stress.

Finally, based on findings, we recognize that the results of an internship experience depend on the relationships established between the students’ career goals content and the quality afforded by the working context (e.g., Kenny & Bledsoe, 2005; Kuijpers et al., 2011). Thus, (Hypothesis 3) it was expected that, for students planning to enter into the labor market immediately after graduation, the quality of the internship would have a greater impact on career exploration than for students wishing to enter higher education because this first formal work experience has both more intrinsic and instrumental value for these students (e.g., Billett, 2005; Simons et al., 2004; Vansteenkiste et al., 2004; Van Vianen et al., 2008).

Method

Participants

Participants were 191 twelfth-grade students (87 boys, 45.5%; 104 girls, 54.5%), from four public vocational education high schools in southern Portugal. Ages ranged from 17 to 25 years (M = 18.27, SD = 1.55) at the time of first measurement (T1). About 15% of the participants were older than expected for this level of education because VET courses in Portugal admit young adults who need to acquire professional qualification before entering the labor market. Regarding socioeconomic status (SES), nearly half of the sample was coded as low SES (51.3%), 43.5% were coded as medium SES, and only 5.2% of the students were coded as high SES. The students averaged about 221 hr (14 weeks) of placement in their internships (range = 185–310 hr).

Measures

A Demographic Questionnaire was used to collect information regarding students’ gender, age, SES, internship duration, and postsecondary immediate career plans. SES was determined based on parent’s professional qualifications, according to the Portuguese National Classification of Occupations (1 = unskilled, 9 = highly skilled workers) and the educational level (1 = basic education, 7 = university degree). The scores for father and mother were taken as indicators of the family SES (three levels: low, medium, and high). On the other hand, career plans (T1 and T2) were assessed by asking students to choose one of the following options: In the end of the course, I intend to (a) continue studying in higher education or (b) get a job.

Students’ career exploration was assessed using the Portuguese version (Taveira, 1997) of the Career Exploration Survey (CES; Stumpf et al., 1983). The CES is a multidimensional self-administered scale with 54 items, designed to assess five beliefs about exploration—employment outlook (3 items, e.g., “what are the possibilities of getting a job in the professions that interest you most,” α = .84), certainty of exploration outcomes (3 items, e.g., “how certain are you that you will begin work upon graduation in the specific job you prefer,” α = .89), external instrumentality (11 items, e.g., “what is the probability that each of the following activities will result in obtaining your career goals—planning my job search in detail,” α = .85), internal instrumentality (4 items, e.g., “what is the probability that each of the following activities will result in obtaining your career goals—learning more about myself,” α = .73), and importance of preferred position (3 items, e.g., “How important is it to you at this time to work at the job you prefer,” α = .68); four dimensions of the career exploration process—self-exploration (5 items, e.g., “in the last 3 months, I’ve been retrospective in thinking about my career,” α = .70), environment exploration (4 items, e.g., “in the last 3 months, I initiated conversations with knowledgeable individuals in my career area,” α = .76), intended-systematic exploration (ISE; 2 items, e.g., “in last 3 months, I intended to conduct activities to try out my skills,” α = .62), and amount of information (3 items, e.g., “how much information do I have on what one does in the career area(s) you have investigated,” α = .68); and three reactions to vocational exploration—satisfaction with information (3 items, e.g., “How satisfied are you with the information you have on the specific job in which you are interested,” α = .81), exploration stress (4 items, e.g., “how much undesirable stress have the following caused you relative to other significant issues with which you have to content—exploring specific jobs,” α = .78), and decisional stress (4 items, e.g., “how much undesirable stress have the following caused you relative to other significant issues with which you have to content—deciding what I want to do,” α = .91). Responses were given on 5-point (Items 1–43) and 7-point (Items 44–53) Likert-type scales, with higher scores indicating higher degrees of exploratory behavior/beliefs/reactions. The validity, reliability, and multidimensionality of the CES have been widely demonstrated (e.g., Rowold & Staufenbiel, 2010; Stumpf et al., 1983). Regarding the Portuguese version, confirmatory factor analysis, conducted by Taveira (1997), with a sample of 9th- and 12th-grade students, supported a 12 first-order factor structure of the CES. The measurement model for the CES-Portugal was slightly different from the original model. Regarding career exploration beliefs, the model with the best adjustment indices has five factors (χ²/df = 5,469, goodness-of-fit index [GFI] = .935, adjusted GFI [AGFI] = .919, standardized root mean square residual [SRMR] = .045) instead of the seven factors of the original version. In the exploration process, the best fit model has four factors (χ²/df = 4.69, GFI = .967, AGFI = .919, SRMR = .045) instead of the six factors of the original version. Cronbach’s αvalues presented above relate to the present study.

The Internship Quality Inventory (IQI; Gamboa, 2011) was developed to measure students’ perceptions of their practicum training quality, considering the internship learning objectives (Portuguese Guidelines) for Level 3 VET courses. The results of the exploratory factor analysis (Kaiser–Meyer–Olkin index = .93) utilizing principal components with Varimax rotation indicated six reliable factors (factor loadings > .45; ranged from .49 to .83), which explained 63% of the variance, that were conceptualized as follows: autonomy (4 items, e.g., “in my internship, I was able to use my initiative,” α = .81), peer feedback (4 items, e.g., “in my internship, my colleagues gave me clues to improve my work,” α = .83), social support (4 items, e.g., “in my internship, the people I worked with were friendly,” α = .82), learning opportunities (8 items, e.g., “my internship involved the performance of a wide variety of tasks,” α = .85), supervisor training (10 items, e.g., “advised by my supervisor, always knew what I had to do,” α = .92), and supervisor support (7 items, e.g., “my supervisor showed much interest in my opinions and ideas,” α = .87). Participants responded to the items of the IQI on a 5-point Likert-type scale that ranged from strongly disagree (1) to strongly agree (5), with higher scores indicating a higher degree of perceived quality. IQI also showed evidence for convergent validity by being related with measures of career adaptability (Silva & Gamboa, 2014), career exploration and decision-making self-efficacy (Gamboa et al., 2014), and internship satisfaction (Gamboa, 2011). Cronbach’s α values presented above relate to the present study.

Procedures

This study consisted of a short-term longitudinal design with two points of data collection: before the internship (T1) and immediately after finishing the internship (14 weeks later; T2). After the initial phase, on which the study was presented to the participating schools, we began establishing the groups and organizing the timing of the data collection. Subsequently, appropriate informed consent procedures (i.e., parents’, students’, and school boards’ permission) were followed in collecting data. The application of the instruments was carried out by trained coresearchers, in a classroom context, with the assistance of the class teacher. The perception of the internship quality (IQI) was assessed only at T2. On average, each assessment session lasted around 45 min (before and after the internship). There was no attrition from T1 to T2 because all students had a period of classes after the internship during which they prepared for final exams. To ensure data confidentiality, pairing of the questionnaires was performed using an alphanumeric code.

Results

Table 1 presents the means and standards deviations for the studied variables (all sample), at both moments (T1–T2). Concerning the career exploration variables, globally, mean values rise from the first (T1) to the second moment (T2), meaning that there was an increase in exploratory activity during the internship period, which was accompanied by higher levels of satisfaction with information (t = −4.05, p < .01) and a higher level of stress regarding career exploration (t = −3.06, p < .01) and career decision-making (t = −2.35, p < .05). On the other hand, correlational analysis shows that girls had more favorable exploratory activity than boys. In reference to age, older students, at the second moment (T2), presented more favorable exploratory beliefs and higher values in all subscales of the exploration process. Regarding SES, the students from higher levels are the ones seeking information in a more systematic and intentional manner (T2 = r = .13, p < .05).

The results presented on the diagonal (T1 × T2) show that there is some stability in exploratory activity, considering that these range from r = .31 (ISE) to r = .52 (decisional stress). Taking into account the results presented below (T1) and above (T2) the diagonal, we found that, in general, the different dimensions of the CES show significant correlations in the expected direction. Also in Table 1, the results suggest that, overall, the quality of the internship is positively correlated with the different variables of career exploration (T2), the most notable being the observed values between autonomy and ISE (r = .34, p < .01) and between supervisor support and ISE (r = .32, p < .01).

Table 1.

Bivariate Correlations for Variables at T1 (Reported Below the Diagonal) and T2 (Reported Above the Diagonal).

	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22
1. Gender	1	.19**	−.18**	−.03	−.01	.07	.12*	.07	.14*	.08	.06	.10	.10	.01	−.01	.17**	.04	.10	.19**	.07	.02	−.17*
2. Age	.19**	1	−.04	.16**	.07	.17**	.20**	.02	.16**	.19**	.23**	.17**	.09	.07	−.05	.13**	.10	.06	.07	17**	.09	.09
3. SES	−.18**	−.04	1	.07	.10	.06	.04	.03	.06	.01	.13*	.06	.01	−.03	−.02	.03	.04	−.02	−.04	.02	.03	.04
4. EO	.01	.05	.01	.40**	.49**	.44**	.39**	.14	.47**	.16*	.38**	.44**	.49**	−.06	−.11	.17*	.07	.13	.17*	.19*	.17*	.04
5. CO	.08	−.02	−.04	.33**	.39**	.28**	.11	.23**	.21**	.15*	.26**	.22**	.25**	.07	−.03	.07	.08	.04	.04	.11	.06	−.01
6. EI	.14*	.04	.08	.17*	.10	.41**	.71**	.33**	.44**	.49**	.34**	.32**	.34**	.02	.04	.24**	.14	.25**	.18*	.31**	.18*	.04
7. II	.16*	.05	.09	.20**	.07	.58**	.40**	.28**	.36**	.44**	.21**	.17*	.21**	−.04	.04	.20**	.11	.18*	.16*	.12	.07	.04
8. IPP	.06	.02	.01	−.02	.10	.31**	.20**	.41**	.23**	.23**	.10	.16*	.10	.14	.13	.05	.13	.15*	.02	.17**	.05	.07
9. EE	.15*	.07	.02	.34**	.26**	.29**	.22**	.13	.40**	.34**	.34**	.51**	.50**	−.03	−.14*	.24**	.19*	.19*	.17*	.27**	.21**	−.05
10. SE	.02	.09	.05	.14	−.08	.40**	.43**	.16*	.30**	.40**	.22**	.07	.14	.18*	.10	.06	.25**	.06	.01	.17*	.08	−.07
11. ISE	.03	.05	.04	.30**	.21**	.24**	.19**	.16*	.43**	.28**	.31**	.38**	.33**	.04	.05	.34**	.23**	.25**	.22**	.32**	.19**	.07
12. AI	.06	.07	.10	.43**	.21**	.15*	.18**	−.02	.38**	.13	.31**	.46**	.59**	−.11	−.24**	.12	.07	.13	.19**	.19*	.16*	.05
13. SI	.07	.02	.03	.39**	.16*	.15*	.15*	.09	.30**	.10	.25**	.47**	.33**	−.13	−.18*	.08	.12	.04	.10	.11	.10	.02
14. ES	.08	.08	−.04	.01	−.06	.10	.08	.23**	−.12	.20**	−.04	−.12	−.13	.37**	.62**	−.02	−.07	−.06	−.13	.04	−.04	−.05
15. DS	.03	.02	−.09	.01	−.16*	.08	.06	.10	−.20**	.09	−.05	−.16*	−.15*	.49**	.52**	.06	−.15*	.07	−.10	.02	−.06	−.07
T1: M (SD)		18.41(1.57)		9.22(2.06)	7.66(2.75)	33.07(5.04)	13.98(2.60)	11.25(2.24)	11.22(3.28)	14.57(3.96)	5.03(1.73)	9.42(2.01)	9.29(1.94)	14.92(4.36)	20.21(6.96)
T2: M (SD)				9.72(2.09)	8.66(2.75)	33.16(4.88)	13.94(2.57)	10.88(2.12)	12.59(2.97)	15.81(3.61)	5.82(1.68)	9.48(2.07)	9.93(1.87)	15.99(4.22)	21.35(6.90)	3.73(0.68)	3.52(0.78)	3.86(0.65)	4.13(0.73)	3.52(0.72)	3.75(0.80)
t test				3.08**	4.54**	0.23	0.18	2.19*	5.59**	3.11**	5.50**	3.47**	4.05**	3.06**	2.35*

Note. Intercorrelation among T1 and T2 variables (at the diagonal), mean and standard deviations of the assessed constructs, paired sample t test (N = 191). (3) SES = socioeconomic status; (4) EO = employment outlook; (5) CO = certainty of exploration outcomes; (6) EI = external instrumentality; (7) II = internal instrumentality; (8) IPP = importance of preferred position; (9) EE = environment exploration; (10) SE = self-exploration; (11) ISE = intended-systematic exploration; (12) AI = amount of information; (13) SI = satisfaction with information; (14) ES = exploration stress; (15) DS = decisional stress; (16) Autonomy; (17) Peer feedback; (18) Learning opportunities; (19) Social support; (20) Supervisor support; (21) Supervisor training; (22) Duration of the internship.

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

To analyze the effect of career goals content (T1) on the vocational development of students, we ran an analysis of variance (ANOVA) with repeated measures. In the between-subject factor, two categories of career goals were considered (T1): (1) pursue studies groups (PSGs: N = 100) and (2) labor market group (LMG: N = 91), while the vocational variables were entered as the within-subject factor. The associations between the sociodemographic variables and the career plans variable were analyzed, showing statistically significant associations with SES (χ² = 35.910, df = 2, p < .001). With regard to age, the difference in the mean values is significant (t = −2,769, p = .006) and in favor of students seeking to enter the labor market. Thus, SES and age entered as covariates.

In Table 2, results of the ANOVAs show that the interaction between group (PSGs × LMG) and moments of data collection (T1-T2) was significant in 4 of the 12 career exploration variables, specifically in the Self-Exploration (F = 4.01, p = .047, η² = .022), Amount of Information (F = 7.20, p = .008, η² = .039), Exploration Stress (F = 6.60, p = .011, η² = .036), and Decisional Stress (F = 4.09, p = .045, η² = .023). In this case, the effect is related to the fact of students who wish to enter higher education (PSG) have significantly higher amount of exploratory activity, with statistically significant gains, while students who want to join the workforce (LMG) present a moderate swing. Therefore, the students wishing to continue their studies after completing the 12th grade present a higher increase in exploratory activity: Self-Exploration (T1–T2; M_difference= −1.130, t = −2.679, p = .009) and Amount of Information (T1–T2; M_difference = −.701, t = −3.587, p = .001). Concerning the stress with exploration factor, it should be noted that differences between T1 and T2 reach statistical significance in both groups (PSG: M_difference = −1.130, t = −2.087, p = .039; LMG: M_difference = −1.03, t = −2.415, p = .018). Finally, with regard to the stress in decision-making, both groups experienced an increase in stress, but the differences between moments (T1–T2) were only statistically significant in the group intending to enter higher education (PSG; M_difference = −2.16, t = −3.411, p = .009). Regarding the internship quality variables, there were no significant differences between the two groups.

Table 2.

Means and Standard Deviations of Career Exploration (CES) and Perceived Quality of Internship (IQI) Variables, Analyses of Covariance With Repeated Measures, and the Corresponding Effect Size ( $η_{p}^{2}$ ).

	PSG (100)		LMG (91)
	T1	T2	T1	T2
	M (SD)	M (SD)	M (SD)	M (SD)	F	p	η²
Employment outlook	8.92 (2.09)	9.45 (2.15)	9.59 (1.94)	10.02 (2.01)	0.557	.452	.003
Certainty of exploration outcomes	7.63 (2.80)	8.53 (2.63)	7.74 (2.69)	8.80 (2.90)	0.128	.721	.001
External instrumentality	34.27 (5.11)	33.50 (4.97)	31.70 (4.66)	32.76 (4.80)	1.981	.161	.011
Internal instrumentality	14.41 (2.44)	14.15 (2.69)	13.49 (2.70)	13.70 (2.44)	0.316	.575	.002
Importance of preferred position	11.45 (2.25)	11.10 (2.30)	11.02 (2.24)	10.60 (1.87)	0.983	.323	.005
Environment exploration	11.23 (3.28)	12.50 (2.98)	11.24 (3.29)	12.70 (2.98)	0.564	.454	.003
Self-exploration	14.72 (3.89)	15.85 (3.51)	14.47 (4.03)	15.19 (3.69)	4.01	.047	.022
Intended-systematic exploration	4.93 (1.82)	5.79 (1.80)	5.13 (1.62)	5.87 (1.55)	0.081	.776	.000
Amount of information	9.19 (1.88)	9.89 (2.10)	9.62 (2.06)	9.98 (2.04)	7.20	.008	.039
Satisfaction with information	9.17 (1.94)	9.75 (1.98)	9.43 (1.94)	10.12 (1.74)	0.014	.906	.000
Exploration stress	15.00 (4.65)	16.14 (4.47)	14.87 (4.03)	15.90 (3.87)	6.60	.011	.036
Decisional stress	20.16 (6.67)	22.32 (6.74)	20.24 (7.35)	20.37 (6.97)	4.09	.045	.023
Autonomy		3.71 (0.75)		3.75 (0.58)	0.234	.629
Peer feedback		3.46 (0.78)		3.59 (0.77)	1.361	.245
Social support		4.13 (0.71)		4.14 (0.76)	.025	.876
Learning opportunities		3.87 (.66)		3.85 (.66)	.027	.869
Supervisor training		3.74 (.80)		3.76 (.81)	.037	.848
Supervisor support		3.50 (0.74)		3.55 (0.70)	.275	.601

Note. N = 191. PSG = pursue studies group; LMG = labor market group; CES = Career Exploration Survey; IQI = Internship Quality Inventory.

Finally, to assess the significant interactions between perceived internship qualities (IQI) and the changes in vocational variables from T1 to T2, considering students’ career goals (T1), analyses of covariance with repeated measures were employed for each of the following conditions: (1) continue studies or (2) enter the labor market. In repeated measurements factor, we entered the vocational variables, with two levels (before and after the internship), while the qualities of the internship were entered as covariates.

In Table 3, we point out that, regarding PSG, only two significant interactions were observed with the quality of the internship. Namely, environment exploration is associated with the supervisor training (η² = .056) and the ISE with the autonomy experienced in the internship context (η² = .067).

Table 3.

Summary Data—Test of the Effect of Interaction Between Factor Within-Subject (Career Exploration Variables) and the Covariates (Internship Qualities) in Analyses of Covariance With Repeated Measures and the Corresponding Effect Size ( $η_{p}^{2}$ ).

	Group: Pursue Studies (N = 100)				Group: Entering the Labor Market (N = 91)
Factor Within-Subject	Internship Quality				Internship Quality
		F	p	η²		F	p	η²
Employment outlook
Certainty of exploration outcomes					Supervisor training	F(1, 89) = 4.13	.045	.044
External instrumentality					Learning opportunities	F(1, 89) = 6.05	.016	.064
Internal instrumentality
Importance of preferred position
Environment exploration	Supervisor training	F(1, 98) = 5.46	.022	.056
Self-exploration					Learning opportunities	F(1, 89) = 4.97	.028	.053
Intended-systematic exploration	Autonomy	F(1, 98) = 7.05	.009	.067	Autonomy	F(1, 89) = 3.97	.050	.043
					Peer feedback	F(1, 89) = 6.41	.013	.068
					Social support	F(1, 89) = 9.21	.003	.094
					Learning opportunities	F(1, 89) = 10.19	.002	.103
					Supervisor training	F(1, 89) = 13.31	.000	.130
					Supervisor support	F(1, 89) = 10.32	.002	.104
Amount of information
Satisfaction with information					Autonomy	F(1, 89) = 3.91	.050	.042
Exploration stress
Decisional stress

On the other hand, Table 3 presents also results for the group that planned on immediately entering the world of work after the internship (LMG). Contrary to results from the previous group, we found that there are several qualities of the internship that significantly interact with the career exploration variables change (T1–T2). Thus, learning opportunities are associated with the changes that occurred in external instrumentality (η² = .064), self-exploration (η² = .053), and ISE (η² = .103). With regard to ISE, we found significant interactions with autonomy (η² = .043), peer feedback (η² = .068), social support (η² = .094), learning opportunities (η² = .103), supervisor support (η² = .104), and supervisor training (η² = .130). Moreover, supervisor training appears to be associated with the certainty of exploration outcomes factor (η² = .044) and the autonomy with satisfaction with information factor (η² = .042).

Discussion

The first set of analyses (all samples), which examined the effect of time on career exploration, revealed that from T1 to T2, there were statistically significant gains, especially in the environment exploration and ISE variables. These results are consistent with the literature, which supports the idea that the transition to new contexts of learning produces an increase in exploratory activity (e.g., Blustein, 1997, Flum & Blustein, 2000; Kalakoski & Nurmi, 1998). Concerning the observed relationships between the sociodemographic variables and the different dimensions of career exploration (CES), it should be noted that, overall, the results are consistent with previous studies (e.g., Kracke & Schmitt-Rodermund, 2001; Rogers & Creed, 2011; Taveira, 1997). The correlations between the mean values obtained at each time (T1 × T2) suggest some stability in exploratory activity. In fact, the literature highlights the results from T1 as the best predictors of each variable at T2 (e.g., Rogers & Creed, 2011), especially at the end of adolescence, when the vocational processes most directly involved in career decision-making and commitment are more firmly stabilized. Regarding the quality of the internship (IQI), the results are also consistent with the vocational literature (e.g., Flum, 2015; Gamboa et al., 2013; Mortimer & Zimmer-Gembeck, 2007; Pan, 2018; Sekiguchi, 2012; Van Vianen et al., 2008), considering that all of its dimensions are significantly correlated, in the expected direction, with most of the dimensions of career exploration.

Our main goal was to study the impact of internships on vocational development, while considering students’ career goals (entering the workforce vs. higher education). In other words, we wanted to know to what extent, as defended in the literature (e.g., Jordaan, 1963; Kracke & Schmitt-Rodermund, 2001; Lent et al., 1994; Nurmi et al., 2002; Rogers et al., 2008), career goal content differentiates students in career exploration during the internship. The results indicated that students who expressed early in the internship that they intended to continue studies in higher education showed more pronounced increases in exploratory activity (self-exploration and amount of information) and experienced higher levels of career exploration and decision-making stress than their colleagues who planned to immediately enter the job market. From our point of view, these differences may be due to a stronger need for information among students wishing to enter higher education, since, contrary to what happens with their colleagues who will immediately enter the working world, they are concerned with preparing for the transition into a system (e.g., university or polytechnic higher education) that is not in continuity with their existing system (e.g., school, internship context). This transition, according to developmental-contextualist career theory (e.g., Vondracek & Porfeli, 2008), increases feelings of discontinuity because of the anticipated ecological transition.

In this sense, when we consider career exploration as a critical process that ensures career adaptability (Blustein, 1997; Flum & Blustein, 2000; Jordaan, 1963; Kalakoski & Nurmi, 1998), we realize that the students planning to enter higher education increase their levels of exploration and experience greater anxiety precisely because of their new role (undergraduate students). This new role may be insufficiently integrated into their experiential repertoire because VET courses, despite allowing students to enter higher education, mainly prepare them to join the labor market in the short term. These findings suggest that such courses should deepen the attention given to preparing student responses that are adequately effective in meeting not only the academic challenges but also the personal and social challenges they will face (e.g., Farías & Sevilla, 2015; Hayward & Hoelscher, 2011).

Finally, it is important to discuss the impact of the internship quality on career exploration, taking into account students’ career goals (entering the workforce vs. higher education). First, the results are consistent with the literature, which supports the differentiating effect of career goals, or intentions, on vocational development (e.g., Blustein, 1989; Jordaan, 1963; Kracke & Schmitt-Rodermund, 2001; Lent et al., 1994). In fact, students planning to enter the labor market in the short term seem to be more “sensitive” to the quality of the internship. Second, it must be noted that within this group of students, the qualities of the internship with the greatest impact include learning opportunities, variety of tasks, and supervisor support and training. According to Van Vianen and colleagues (2008), the quality of the internship is, in fact, more important for those students who are planning to begin to work in their near future, and this finding is due, most likely, to the recognition of this experience as contributing to their technical–professional preparation. From the students’ perspective, the quality of their more immediate professional performance will depend (mostly) on the quality of (learning) knowledge they acquire throughout the internship, which may be more or less representative of the professional practices they will encounter when they start their occupation (e.g., Billett, 2005). Additionally, taking into account the results obtained by Vansteenkiste and colleagues (2004) and Simons and colleagues (2004), plans that are framed in terms of future intrinsic goal attainment (as opposed to future extrinsic goal attainment) or which have an endogenous instrumentality concerning future desired goals (as opposed to an exogenous and/or external instrumentality concerning those future goals) have an overall positive impact on cognitive and behavioral processes. In fact, for students planning to enter the labor market, internships are more clearly aligned with their anticipated occupational situation in the near future, thus presenting both a stronger intrinsic and instrumental value for this group of students.

Limitations and Implications for Practice

The fact of being a longitudinal study with only two measures stands as a strong limitation, considering that the adopted design only allows linear analysis between the two moments of data collection. Therefore, in the future, it will be important to develop longitudinal studies with several waves of data collection that cover the entire training cycle (10th, 11th, and 12th grades). This will enable the analysis of possible changes associated with participation in work experience in the context of the students’ developmental trajectories. In future research, other indicators beyond self-report measures should be used, such as the supervisor profile (e.g., pedagogic training, professional experience, communication skills) and workplace features (e.g., technical, human resources policy), in order to better clarify the extent to which these aspects interact with students’ perceptions about the quality of their internship experience.

In terms of implications for practice, our main contribution lies in altering the idea that internships, as a modality of experiential learning, have a uniform positive impact on learners’ cognition and behavior. On the contrary, considering the diverse characteristics of the students attending vocational courses, the organization of learning experiences in the real work context should take into account their initial career goal content, as well as the reformulation these goals may undergo during the training period. Based on the results found in this study, it is also essential to underscore the importance of examining students’ motivational and vocational profiles during the internship, thus guaranteeing that the learning experiences planned for this period promote the necessary changes in the students’ vocational sphere, as well as in their short-term career plans. Lastly, the results of our study challenge, to a certain extent, the widely spread idea that work experiences are always valid per se, once they uncovered the impact, in the vocational development of students wishing to immediately enter the job market, of the quality of the supervision and of the learning opportunities provided during the internship. For instance, career counselors should work closely with educational decision makers, school managers, and teachers, helping them, according to the specific stakeholder, devise, implement, and/or evaluate adequate apprenticeship or internship schemes, via the identification of both personal (e.g., educational aspirations, actions, and strategies) and contextual (both social and structural) key success factors. In summary, and taking into account the dynamic nature of the vocational processes in experiential learning contexts, we advocate that, beyond the technical training and other formal learning procedures carried out in internships, learning in real work environments should be seen as an opportunity for career development and personal growth.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Vítor Gamboa

Maria Paula Paixão

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