Supporting students’ transition to higher education: the effects of a pre-academic programme on sense of belonging, academic self-efficacy, and academic achievement

ABSTRACT

The transition to higher education is a challenging period for many students and requires support. Because students’ backgrounds, such as being a first-generation in higher education student, shape experiences in higher education, it is important to consider these factors when organizing support. Using a quasi-experimental pre-test – post-test design, the current study examined the effects of an online pre-academic programme (PAP) specifically aimed to address background-related challenges, on early academic achievement, sense of belonging, academic self-efficacy, and mobilization of on-campus social capital. Multilevel regression analyses of achievement data (N_PAP = 463; N_Control = 948) and psychosocial data (N_PAP = 115; N_Control = 544) indicated a positive effect of PAP on achievement and sense of belonging, but not on self-efficacy. Mediation analyses showed that effects of PAP did not vary according to background factors. Path analysis further showed a positive association of PAP participation and mobilization of peer social capital, which partly mediated the effect on sense of belonging. No associations were found with mobilization of faculty social capital. The results suggest that PAP participation positively affects students’ transition to HE, in terms of early achievement, sense of belonging in HE, and mobilization of peer capital.

KEYWORDS:

• Transition to higher education
• intervention
• first-generation in higher education students
• social capital
• sense of belonging

Introduction

The transition to higher education (HE) is an important and challenging life event for students. High levels of first-year drop-out and well-being problems indicate that many incoming students experience severe difficulties during their first months in HE (Briggs, Clark, and Hall 2012; Coertjens et al. 2017). Challenges that first-year students encounter include, for instance, making sense of the new learning environment, establishing relationships with peers and faculty, and adjusting to new ways of learning (Brooman and Darwent 2014). To support students with these challenges, many higher education institutions (HEIs) have implemented transition interventions (Coertjens et al. 2017). A particularly prevalent type of such an intervention is the pre-academic or summer-bridge programme: an intervention that occurs during summer before incoming students enrol into their first year in HE and that aims to facilitate students’ first encounter with the HE environment (Sablan 2014; Van Herpen et al. 2020).

Although pre-academic programmes strongly vary in aims and activities, it is common that these programmes primarily include modules on study skills, time management, and campus organization (Greer, Chi, and Hylton-Patterson 2023; Sablan 2014). This focus on academic skills and practical information may not sufficiently support all students however, as this strategy does not explicitly acknowledge the way in which social background influences experiences and challenges of students (Melguizo et al. 2021; Schwartz et al. 2018). Research indicates for instance that first-generation in higher education students (FGHE-students) experience more and additional difficulties during the transition to HE, such as feeling less welcomed and having more doubts about their belonging in HE, than continuing-generation in higher education students (CGHE-students; Leese 2010; Spiegler and Bednarek 2013). Interventions that do not take these background-specific experiences into account may not succeed to offer inclusive support, in the sense that they do not meet the needs of all incoming students (Hausmann et al. 2009; Townsend, Stephens, and Hamedani 2021). To facilitate both FGHE and CGHE students’ transition to HE, it is thus important that pre-academic programmes adopt strategies that address these specific background-related experiences.

Two strategies that transition programmes can adopt for inclusive support are (1) acknowledging and teaching participants how students’ identities influence experiences in HE, and (2) enhancing mobilization of on-campus social capital. Prior studies have shown a potential of these strategies to partly bridge an achievement gap identified for several decades in the US between FGHE- and CGHE-students, by improving FGHE-students’ grades (Schwartz et al. 2018; Stephens, Hamedani, and Destin 2014; Terenzini et al. 1996). In Dutch HE, the context of the current study, this achievement gap is less pronounced (van Rooij et al. 2018), but there are indications that FGHE-students experience more doubts about their academic abilities and belonging in HE (Hulzebos and Munniksma 2022). These findings, and an increasing call to define success in HE more broadly than merely academic outcomes, including for instance psychosocial outcomes such as sense of belonging and academic self-efficacy (Melguizo et al. 2021; van der Zanden et al. 2018), suggest a need to implement and study interventions that aim to impact these transition outcomes as well.

In the current study, the effectiveness of an online pre-academic programme (PAP) is examined. This programme aims to support all transitioning students, but adopts specific strategies to address experiences of FGHE-students, based on theories and prior research on multicultural education (Denson 2009; Gurin et al. 2009) and social capital (Parnes et al. 2020; Schwartz et al. 2018). Using a quasi-experimental pre-test post-test design, we examined the effects of PAP on various transition outcomes (academic achievement, sense of belonging, academic self-efficacy).

The transition to higher education

The relevance of the transition to HE for students’ further academic trajectories and its’ challenging nature are widely emphasized in the HE literature (Briggs, Clark, and Hall 2012; Coertjens et al. 2017). Based on Nicholson’s (1990) transition cycle model, the transition is often conceptualized as a process of change and instability consisting of four stages: preparation, encounter, adjustment, and stabilization. In the preparation stage, students prepare for enrolment by orientating on HE course programmes and choose where to enrol. The encounter stage then occurs during the first weeks in HE. In this stage, students have their first confrontation with the new learning environment and academic culture and try to make sense of it. In the adjustment stage, students attempt to cope with the new environment and gradually adjust their behaviours to succeed. This stage occurs approximately until the end of students’ first year in HE. Then, ideally speaking, stabilization occurs, which is a more stable period in which students make only small adjustments to their behaviour. Within the transition cycle, the encounter stage has been identified as a particular vulnerable time that requires support for students (Coertjens et al. 2017; De Clercq, Parmentier, and Van Meenen 2022).

Transition outcomes

From the literature, a few outcomes can be identified that indicate success of transitioning students during the transition to HE. Firstly, research repeatedly shows the importance of academic self-efficacy (Brooman and Darwent 2014; De Clercq, Parmentier, and Van Meenen 2022), referring to students’ perception of their ability to learn and perform in the educational context (Bandura 1997). Academic self-efficacy is strongly related to academic achievement (Richardson, Abraham, and Bond 2012; Robbins et al. 2006) and could contribute to the resilience of transitioning students to cope with potentially stressful and demanding changes (Kyndt et al. 2019).

A second requirement for a successful encounter is the development of a sense of belonging in HE, referring to a feeling of connectedness with the HE community, that one is accepted by members of that community and that one ‘fits in’ (Hausmann et al. 2009; Walton and Cohen 2011). Developing a sense of belonging in HE helps students to achieve and continue in HE (Hausmann et al. 2009; Meeuwisse, Severiens, and Born 2010), and is regarded a basic human motivation and as such related to general well-being (Maunder 2018; Walton and Cohen 2011).

Thirdly, research indicates early academic achievement to be important for a successful transition to HE and in particular highlights the first experiences of assessment as a crucial moment in which students evaluate their own competence as learners and HE student (Coertjens et al. 2017; De Clercq, Parmentier, and Van Meenen 2022). If this experience is positive, the first moment of assessment helps students to validate their own sense of belonging in HE and academic self-efficacy (Christie et al. 2008).

On-campus social capital

Research and theory posit that social capital plays a critical role in students’ ability to navigate the transition to HE, as it relates to students’ academic self-efficacy (Bergey et al. 2019; Brouwer et al. 2016), sense of belonging in HE (Brooman and Darwent 2014; Meeuwisse, Severiens, and Born 2010) and academic achievement (Mishra 2020; Robbins et al. 2006). Social capital refers to an individual’s access to and mobilization of valuable resources, such as information or support, through connections and networks of relationships (Coleman 1990; Lin 1999). In the HE context, on-campus social capital¹ is particularly valuable. Research in this field highlights both the importance of connections with peers (Neves et al. 2019; Wittner, Barthauer, and Kauffeld 2020) and with faculty (Brouwer et al. 2016; Schwartz et al. 2018).

For FGHE-students, it may be more challenging to have access to and mobilize the relevant social capital that is necessary to succeed in HE (Álvarez-Rivadulla et al. 2022; Wittner, Barthauer, and Kauffeld 2020). Research shows that students with higher educated parents may be in advantage in HE, because they can access and mobilize relevant social capital at home, which shows the way to mobilize on-campus social capital which in turn, for instance, helps to provide information regarding study materials, preparing for exams, and support for dealing with academic challenges (Bergey et al. 2019; Mishra 2020; Spiegler and Bednarek 2013). Furthermore, students from higher educational backgrounds are more accustomed to the often implicit values, norms, and rules that are valued in the HE environment (Leese 2010). Although research challenges the assumption that FGHE-students’ cannot rely on their families for support (Martin et al. 2020; Rios-Aguilar and Deil-Amen 2012), these studies also show that on-campus relationships are important for FGHE-students for so-called ‘instrumental capital’, referring to the resources (e.g. advice, information) that individuals can access through their social networks that help them achieve specific goals (Son and Lin 2012). Within the current educational environment, FGHE-students may experience more difficulties with building this necessary on-campus social capital, as research indicated that these students tend to be less likely to report relationships with peers and faculty (Mishra 2020; Schwartz et al. 2018).

Strategies for inclusive transition support

To organize inclusive transition support that meets the needs of all students, it is important that interventions address the specific background-related experiences of students (Hausmann et al. 2009). Prior research indicates two related evidence-informed strategies to address challenges that all students may come across in the transition to HE, but that may be particularly relevant for FGHE-students. Firstly, based on research on multicultural education (Denson 2009; Gurin et al. 2009), the PAP intervention that was examined in the current study was a difference-intervention, implying that it explicitly did not adopt a difference-blind approach, but instead made students aware of ways in which different backgrounds matter for experiences and opportunities in education. Prior research in the US indicates a strong potential of this type of intervention to affect FGHE-students’ course grades, showing a strong decrease in an achievement gap between FGHE- and CGHE-students (Stephens, Hamedani, and Destin 2014; Townsend, Stephens, and Hamedani 2021). This effect was mediated by an increased tendency among FGHE-students to seek support among faculty.

A second strategy adopted by PAP was informed by prior research of interventions based on social capital theory (Coleman 1990; Lin 1999). Social capital interventions aim to empower students to build relevant social support networks, by informing students on the role of social capital in advancing goals, helping students to identify current and potential connections, and helping students to build connections (Schwartz et al. 2018). Prior research indicated a potential of these interventions to improve academic outcomes of FGHE-students, however no CGHE-students participated in these studies (Parnes et al. 2020; Schwartz et al. 2018). Similar to the difference-intervention studied by Stephens and colleagues (2014), the effects of the social capital intervention were mediated by an increased likelihood to seek support from instructors (Schwartz et al. 2018). These prior studies on social capital interventions did not examine the potential of these interventions to enhance mobilization of peer capital, however.

Although connections with faculty are important for a successful transition to HE, social capital research specifically points to the importance of on-campus peer social capital in the transition to HE (Brouwer et al. 2016; Mishra 2020). Positive relationships with peers can make learning environments feel more academically and socially supportive, thereby positively influencing students’ sense of belonging in HE (Meeuwisse, Severiens, and Born 2010; Strayhorn 2018). Furthermore, discussing study strategies with close peers and watching peers use study strategies can influence academic self-efficacy, as individuals can gain confidence in their own ability to perform certain tasks when observing similar others while performing these tasks (Bandura 1997; Bergey et al. 2019). PAP therefore included activities that were aimed at both enhancing the mobilization of faculty and peer social capital in the HE environment.

The current study

The intervention that was examined in the current study, the Pre-Academic Programme (PAP), employs both strategies of difference-intervention (Stephens, Hamedani, and Destin 2014) and social capital interventions (Parnes et al. 2020; Schwartz et al. 2018) aimed to support FGHE-students in the encounter phase of the transition to HE. A prior study of van Herpen and colleagues (2020) showed that this type of intervention can be beneficial to support students during the transition to HE by impacting students’ academic achievement and mobilization of on-campus social capital. The current study extends on this research by examining heterogeneous effects of programme participation by social background and by examining programme effects on various indicators of transition success, including self-efficacy, sense of belonging, and academic achievement. We hypothesized that participation in this programme has a positive effect on three outcomes that are indicative of a successful transition to HE: early academic achievement (H1), sense of belonging in HE (H2), and academic self-efficacy (H3), and that this positive effect is present both for FGHE- and CGHE-students. Furthermore, we hypothesize that PAP impacted these three outcomes by enhancing students’ mobilization of on-campus faculty social capital (H4) and peer social capital (H5).

Method

Participants and procedure

The data of this study were collected among first-year students of a large state-funded research university located in an urban area of the Netherlands with a yearly enrolment of approximately 6,500 new Bachelor students. Data were collected during the academic year 2020–2021. In total, 580 domestic (i.e. Dutch) and 493 international students completed the 2020-version of PAP, which was organized online due to COVID-19 regulations. To examine the effects of PAP, domestic students who successfully participated in the programme and received a certificate for complete participation (i.e. the participant group) were compared to non-participating domestic students (i.e. the control group). International students and 27 domestic PAP participants who did not receive a certificate due to insufficient attendance were excluded from the sample.

Prospective first-year students filled in a voluntary pre-enrolment questionnaire while applying for the course programme, at latest five weeks before the start of PAP. This questionnaire included an academic self-efficacy scale that was used as pre-test (T0) measure in the current study and included questions on students’ expectations of mobilization of on-campus social capital. Directly after PAP, one week before the start of the academic year, students from the participant and control group were invited by email to complete a post-test questionnaire (T1). The second post-test questionnaire (T2) was conducted in the fourth and fifth week of the academic year, before the moment of first assessment for all students. Through student identification numbers, the questionnaire data were linked to the university’s education research database including data on students’ demographics (e.g. generation-status in HE, gender), pre-university academic achievement, and course grades. Ethical approval was granted by the university’s ethical committee for this procedure (reference number: 21-083).

Due to the use of these several sources of data and moments of data collection, there are fluctuations in sample size and characteristics between the various analyses that were used to test the hypotheses. Characteristics of the various samples are presented in Table 1. For the analyses on PAP’s effect on academic achievement (H1), the Achievement Sample was used, consisting of PAP participants (n = 463) and respondents in the control group (n = 948) who either filled out the T1 or T2 questionnaire and provided explicit consent. For the analyses of H2 and H3 (i.e. PAP’s effect on academic self-efficacy and sense of belonging) and H4 (i.e. mediation via social capital) the Psychosocial Sample was used, consisting of T2 respondents (n_pap= 115; n_control= 544). PAP’s effect on academic self-efficacy was further examined by using the Repeated Measures Sample, including participants with self-efficacy scores on T0, T1, and T2 (n_pap= s43; n_control= 166).

Table 1. Background information of respondents in the PAP and control group.

	Achievement sample				Psychosocial sample				Repeated measures sample
	PAP n = 463)		Control n = 948)		PAP (n = 115)		Control (n = 544)		PAP (n = 43)		Control (n = 166)
	N	%	N	%	N	%	N	%	N	%	N	%
Generation-status
Cont.-generation	336	72.5	678	71.5	90	78.3	390	71.7	33	76.7	122	67.5
First-generation	127	27.5	270	28.5	25	21.7	154	28.3	10	23.3	33	32.5
Gender
Female	328	70.8	614	64.8	89	77.4	383	70.4	34	79.1	121	72.9
Male	135	29.2	334	35.2	26	22.6	161	29.9	9	20.9	45	27.1
pu-GPA
Low	76	16.4	208	21.9	12	10.4	104	19.1	3	7.0	27	16.3
Moderate	262	56.6	598	63.1	72	62.6	362	66.5	29	67.4	110	66.3
High	125	27.0	142	15.0	31	27.0	78	14.3	11	25.6	29	17.5
Faculty
Medical studies	122	26.3	93	9.8	19	16.5	30	5.5	9	20.9	12	7.2
Economics	33	7.1	66	7.0	5	4.3	49	9.0	-	-	-	-
Humanities	30	6.5	65	6.9	6	5.2	9	1.7	4	9.3	3	1.8
Law	86	18.6	117	12.3	14	12.2	25	4.6	5	11.6	12	7.2
Social Sciences	71	15.3	318	33.5	42	36.5	269	49.4	14	32.6	85	51.2
Business	81	17.5	263	27.7	26	22.6	145	26.7	10	23.3	50	30.1
Double degree^a	40	8.6	26	2.7	3	2.6	16	2.9	1	2.3	4	2.4
	M	SD	M	SD	M	SD	M	SD	M	SD	M	SD
T0 Expected staff interactions (1–5)	3.72	0.64	3.66	0.66	3.75	0.58	3.64	0.67	3.73	0.48	3.67	0.72
T0 Expected peer interactions (1–5)	4.22	0.56	4.17	0.58	4.23	0.57	4.18	0.58	4.22	0.60	5.15	0.63

^a Students with NA on pu-GPA are excluded from the analyses of early grade performance.

^b Dubble degree students are either Law and Economics students or Law and Business students.

The intervention

Incoming first-year Bachelor students were invited to participate in the pre-academic programme (PAP) that was organized during summer 2020 before students enrolled into the first year. The 5-week online (due to COVID-19) programme was organized by the university’s outreach department. Each week, participants spent 4–6 h on sessions and assignments that had to be done individually (e.g. watching video lectures, reading literature, individual assignments) or with peers (e.g. assignments in pairs, group meetings). The group meetings were moderated by team captains, who were volunteering upper-level students.

Informed by priorly researched difference-interventions based on multicultural education (Denson 2009; Gurin et al. 2009; Schwartz et al. 2018), PAP aimed to make participants aware that their experiences in HE are coloured by their personal background and personal perceptions and that students consequently experience the transition to HE in diverse ways. To reach this aim, participants’ personal backgrounds were connected to theories and literature on social and cultural capital during various sessions. First, participants read texts on visible and invisible aspects of identity, and social and cultural capital to inform them about these concepts. Then, participants did assignments to reflect on their own identity, by making use of the Johari window (i.e. a method to increase self-awareness and mutual understanding with others) and the ways in which identity influences experiences on campus. These assignments were afterwards discussed in groups to make students not only aware of their own background and how this influences their perceptions on campus, but also of the background of their peers.

Social capital theory (Coleman 1990; Schwartz et al. 2018) was translated into programmatic activities that emphasized that studying in HE is a social and collaborative process and that quality interactions with both peers and academic staff are important for academic achievement and students’ further personal and professional development. Therefore, participants had to work on assignments in pairs or in groups throughout the whole programme and each week ended with a group meeting in which the material of the week was discussed in greater depth under guidance of a team captain. These collaborative sessions allowed participants to better get to know their peers. Furthermore, during the last week participants did an assignment in which they mapped their own social capital and were asked to think about ways in which social networks provide individuals with social capital to reach certain goals. The assignments in which students had to work together with peers or had to think actively about the worth of their own social network were primarily aimed to affect the extent to which students interact with peers and faculty on campus.

Measures

Academic self-efficacy was measured with an 8-item scale before (T0) and after (T1, T2) PAP. The scale was developed by Pintrich et al. (1993) and adapted to measure students’ beliefs about their capacity to achieve in HE. An example item was: ‘I am confident I can understand the basic concepts taught in the course programme’. Response categories ranged from 1 (not true at all) to 5 (completely true). Cronbach’s α’s ranged between .86 and .89 across time-points.

Sense of belonging was measured with a 5-item scale (α = .78) in the T2 questionnaire. The scale was derived from Meeuwisse, Severiens, and Born (2010) and adapted to better fit the COVID-19 online-education context by removing an item from the final scale that asked whether students enjoyed coming to campus. An example item of the scale was: ‘I feel at home at university’. Response categories ranged from 1 (not true at all) to 5 (completely true).

Early grade performance was used as an indicator of academic achievement and was measured by early semester grade point average (EGPA), obtained from the university’s educational research database. EGPA was measured as the average of students’ grades of the first courses, running during the first two months with exams in the beginning of November at the latest, weighted by course credits (EC). Within course programmes, students followed the same courses, which were mostly introductory courses or basic academic skills courses. The total number of EC that students attained during the first two months varied between course programmes with a minimum of 12 EC and a maximum of 15 EC.

Generation-status in HE was measured via the educational background of students’ parents, derived from the university’s educational research database. Students whose parents did not study in HE (ISCED 2011-level 6 or lower) were defined as FGHE-students (Spiegler and Bednarek 2013). Generation-status in HE was a dichotomous variable with value 0 for CGHE-students and value 1 for FGHE-students.

Mobilization of faculty capital was measured by two scales, which distinguish between formal (i.e. study-related) and informal interactions with faculty (Meeuwisse, Severiens, and Born 2010). An example item of the formal faculty interactions scale (6 items; α = .73) is ‘Teachers approach me to enquire about my study progress’. An example item of the informal faculty interactions scale (7 items; α = .75) is ‘I talk about my personal situation with teachers’.

Mobilization of peer capital was measured by two scales used by Meeuwisse, Severiens, and Born (2010) as well. An example item of the formal peer interactions scale (6 items; α = .80) is ‘I collaborate well with fellow students.’ An example item of the informal peer interactions scale (5 items; α = .81) is ‘I have close interpersonal relationships with fellow students.’ Item response options of the faculty and peer social capital scales ranged from 1 (not true at all) to 5 (completely true).

Analysis

To examine PAP’s effect on the three transition outcomes early grade performance (H1), sense of belonging (H2), and academic self-efficacy (H3), multilevel regression analysis was used, because students (level 1) were nested within faculties (level 2), which would be a violation of the independence assumption (Hox 2002). Pre-university achievement and gender were firstly included in each of the three multilevel regression models, together with generation in HE. In step 2, PAP participation was included to examine the programme’s effect on the outcome variables, while controlling for pre-university achievement and gender. Subsequently, an interaction term of PAP*FGHE-status was included to examine whether associations between PAP and the three outcome variables varied across generation in HE. Because of the low number of level-2 units (i.e. seven faculties), robustness analyses were performed with ordinary least square (OLS) regression models with the various faculties included as dummy variables (see Table 4).

Besides using multilevel regression analyses, the effect of PAP on academic self-efficacy (H3) was further examined by an analysis of the pre-test (T0) post-tests (T1, T2) data using mixed analysis of variance including PAP participation as between-subject factor and time as within-subject factor. This analysis allowed to test for the intermediate effect of PAP on self-efficacy (T1) as well as its’ lasting effect over a few weeks (T2).

To address H4 on the mediation effect of PAP on the three transition outcomes via mobilization of social capital, we applied path analysis in R using the package Lavaan (Rosseel 2012). The overall goodness of fit of the model was tested with various indices (Kline 2011). Indications of a good-of-fit model are a non-significant chi-square test, CFI and TLI > 0.95, and RMSEA <0.06. For indirect effects of PAP via social capital mobilization, bias-corrected bootstrapped 95% confidence intervals are reported.

Results

Preliminary analyses

Preliminary analyses of the background characteristics of the PAP group and control group (see Table 1) were conducted to assess whether selection effects occurred. Chi-squared tests indicated that the two groups differed significantly from each other, in the sense that women, X²(1) = 4.91, p = .03, and students with higher prior educational achievement, X²(2) = 30.6, p = <.001, were overrepresented in the PAP-group. To partly adjust for this imbalance, gender and prior educational achievement were included as covariates in the multilevel models. The groups did not differ from each other in terms of generation in HE. Furthermore, t-tests indicated no significant differences between the PAP and control group on pre-enrolment expectations of interactions with peers, t(909.83) = −1.64, p = .10, and interactions with staff, t(921.33) = −1.51, p = .13.

Mean scores, standard deviations, and Spearman correlations of the study’s core variables are presented in Table 2. The PAP group and control group have similar means on the transition outcomes EGPA (M_PAP= 7.00; M_control= 6.96) and self-efficacy on T2 (M_PAP= 3.61; M_control = 3.59), with low corresponding effect sizes of d = .04 for EGPA and d = .03 for self-efficacy. On the third transition outcome, sense of belonging, the PAP group (M = 3.95) scored higher than the control group (M = 3.67), with a medium effect size of d = .39. Lastly, turning to the social capital variables, Table 2 shows that the PAP group scores higher on formal peer interaction (M_PAP= 3.81; M_control= 3.62) and informal peer interaction (M_PAP= 3.34; M_control= 3.08) with corresponding medium effect sizes of respectively d = .27 and d = .28. No score difference have been observed between the PAP group and control group on the variables indicating mobilization of faculty social capital (i.e. formal faculty interactions and informal faculty interactions).

Table 2. Mean scores, standard deviations, and Spearman correlations of variables.

Variables	M (SD) PAP	M (SD) Control	d-value	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.	12.	13.
Achievement Sample
PAP^a				(-)	−.06	.11**	.06	−.06	−.03	.13***	.01	.15***	.10**	.11**	.03	.02
2. FGHE-student^a				−.01	(−)	−.02	−.07	−.02	.03	−.10*	.01	−.02	−.01	−.04	.12**	.01
3. First term GPA^b	7.00 (0.97)	6.96 (0.92)	.04	.02	−.07*	(-)	−.04	.06	−.07	.22***	.10*	−.01	.06	.03	−.05	.00
4. Self-efficacy(T0)^c	4.13 (0.51)	4.06 (0.50)	.14	.07*	−.07*	.02	(-)	.05	.07	.01	.36***	.18***	.11**	.12**	.11**	.04
5. Male^a				−.06*	−.01	−.05*	.05	(-)	−.02	−.06	.15***	−.05	−.13**	−.07	−.11**	.15***
6. Moderate pu-GPA^a				−.06*	−.01	−.02	.05	−.05	(-)	−.62***	.03	.06	.05	.05	.08*	.05
7. High pu-GPA^a				.14*	−.05*	.28***	−.01	−.05	−.60***	(-)	−.08*	.01	.01	.07	−.11*	−.09*
Psychosocial Sample
8. Self-efficacy(T2)^c	3.61 (0.65)	3.59 (0.62)	.03								(-)	.29***	.17***	.09*	.22***	.11**
9. Sense of belonging^c	3.95 (0.73)	3.67 (0.69)	.39									(-)	.44***	.46***	.32***	.23***
10. For. peer int.^c	3.81 (0.75)	3.62 (0.71)	.27										(-)	.70***	.36***	.23***
11. Inf. peer int.^c	3.34 (0.97)	3.08 (0.93)	.28											(-)	.29***	.25***
12. For. staff int.^c	3.38 (0.66)	3.33 (0.66)	.08												(-)	.62***
13. Inf. staff int.^c	2.53 (0.76)	2.49 (0.77)	.06													(-)

Note: Spearman Correlations for Psychosocial Sample variables are presented above the diagonal, correlations for Achievement Sample variables below the diagonal.

^a Dummy variables (0 = no; 1 = yes); reference category of moderate and high pu-GPA is low pu-GPA.

^b Variable with 1–10 scale.

^c Variables with 1–5 scale.

*p < .05; **p < .01; ***p < .001

Transition outcomes

Table 3 presents the multilevel regression models of PAP’s effect on EGPA (achievement sample), and sense of belonging and academic self-efficacy (psychosocial sample). Models 0 in Table 3 firstly allowed to assess the variance of the outcome variables that can be explained at faculty level by calculating intra class correlations (ICCs). For EGPA the ICC was .187, implying that approximately 18.7% of its’ variance can be explained on faculty level. For academic self-efficacy the ICC was .090 and for sense of belonging .041. If anything, these results indicated that there are important aspects on faculty level that particularly influence EGPA and to a lesser extent academic self-efficacy and sense of belonging. These results support our choice for multilevel models to avoid Type-I errors (Musca et al. 2011).

Table 3. Multilevel regression models of transition outcomes.

	Achievement sample	Psychosocial sample
	Early grade performance	Sense of belonging	Academic self-efficacy
Model 0
Intercept	6.892 (.157)***	3.790 (.068)***	3.528 (.080)***
Individual level var.	.727 (.853)	.487 (.698)	.364 (.603)
Faculty level var.	.167 (.408)	.021 (.146)	.036 (.189)
AIC	3587.410	1412.282	1224.224
BIC	3603.166	1425.749	1237.692
Model 1
Intercept	6.370 (.250)***	2.798 (.234)***	1.807 (.194)***
First gen. status	−.025 (.048)	.005 (.063)	.028 (.051)
Self-efficacy (T0)	.003 (.042)	.231 (.051)***	.411 (.042)***
Male	−.112 (.048)*	−.046 (.066)	.201 (.054)***
Achievement
Low	Ref.	Ref.	Ref.
Moderate	.491 (.055)***	.092 (.074)	−.036 (.060)
High	1.108 (.072)***	.017 (.099)	−.031 (.081)
AIC	3268.164	1374.227	1111.733
BIC	3309.979	1409.906	1147.412
Model 2
Intercept	6.351 (.252)***	2.780 (.222)***	1.805 (.194)***
PAP	.106 (.047)*	.223 (.073)**	.037 (.060)
First gen. status	−.022 (.048)	.011 (.063)	.029 (.051)
Self-efficacy (T0)	−.003 (.042)	.224 (.051)***	.409 (.042)***
Male	−.104 (.048)*	−.043 (.065)	.202 (.054)***
Achievement
Low	Ref.	Ref.	Ref.
Moderate	.492 (.055)***	.083 (.074)	−.083 (.060)
High	1.100 (.072)***	−.009 (.099)	−.036 (.082)
AIC	3269.402	1370.608	1117.150
BIC	3316.444	1410.747	1157.289
Model 3
Intercept	6.349 (.252)***	2.777 (.222)***	1.802 (.194)***
PAP	.111 (.054)*	.208 (.083)*	.015 (.068)
First gen. status	−.016 (.058)	.001 (.068)	.015 (.055)
Self-efficacy (T0)	−.003 (.042)	.225 (.051)***	.411 (.042)***
Male	−.104 (.048)*	−.043 (.065)	.202 (.054)***
Achievement
Low	Ref.	Ref.	Ref.
Moderate	.492 (.055)***	.084 (.074)	−.036 (.060)
High	1.100 (.072)***	−.006 (.099)	−.031 (.082)
PAP*First gen.	−.019 (.102)	.065 (.173)	.098 (.141)
AIC	3274.093	1374.139	1120.749
BIC	3326.362	1418.738	1165.348

Note: Standard error is presented between brackets.

*p < .05; **p < .01; ***p < .001.

Model 1 of EGPA indicated no association with FGHE-status (b = −.03 p = .604): grades obtained by FGHE-students did not differ significantly from grades obtained by CGHE-students when controlling for gender, prior academic performance, and academic self-efficacy (T0). In Model 2, PAP participation was included. This model showed that, after adjusting for prior academic performance, gender, and T0 self-efficacy, students in the PAP-group received higher grades during the first few weeks in HE than students in the control group (b = .11; p = .025), which supports H1 expecting a positive effect of PAP participation on academic achievement. The small and non-significant interaction term of PAP participation and FGHE-status (b = −.02; p = .849) included in Model 3 further suggested that the programme’s effect on EGPA did not differ depending on generation in HE: FGHE-students did thus not benefit more or less from PAP participation regarding their grade performance as compared to CGHE-students.

Model 1 of sense of belonging did not indicate a significant main effect of FGHE-status either (b = .01; p = .931), implying that FGHE-students’ sense of belonging in HE did not differ from their CGHE-peers. A positive and significant effect of PAP participation on sense of belonging HE was shown in Model 2 (b = .22; p = .002), indicating that students who participated in the programme felt a stronger connection with the campus community after one month in HE than non-participants, supporting our expectations as formulated in H2. In Model 3, the interaction term of PAP participation and FGHE-status is small and non-significant (b = .07; p = .705), suggesting that PAP’s effect on sense of belonging was similar for FGHE-students and CGHE-students.

Lastly, Table 3 presents the multilevel regression models of academic self-efficacy. Similar to the models of EGPA and sense of belonging, these models did not show a significant effect of FGHE-status on self-efficacy (b = .03; p = .581). The models further indicated that PAP participation did not affect self-efficacy (b = .04; p = .537). The interaction term of PAP participation and FGHE-status is also non-significant (b = .10; p = .487).

To further examine PAP’s effects on academic self-efficacy (H3), a mixed ANOVA was performed using data from the pre-test post-test repeated measures sample (see Table 1), including PAP participation as between-subject factor and Time as within-subject factor. Mauchly’s test indicated that the assumption of sphericity had been violated (X²= .87, p < .05). Therefore, Greenhouse-Geisser epsilon was used to adjust for degrees of freedom for the within-subject factor (ϵ = .887). The results showed no significant effect of PAP participation (F(1,207) = .08, p = .930, η²= <.001) or the interaction of PAP and time (F(2, 414) = .69, p = .503, η²= .001). In line with the multilevel regression analysis of self-efficacy, the mixed ANOVA thus indicated that students in the PAP group and the control group did not vary from each other in terms of academic self-efficacy. Contrary to our expectations (H3), we thus need to conclude that an effect of PAP on academic self-efficacy is absent.

OLS-regression analyses of PAP’s effect on the three transition outcomes including six faculty dummy variables were performed as robustness analyses (see Table 4). Similar to the multilevel regression models, these models indicated positive and significant effects of PAP on EGPA and sense of belonging, but not on academic self-efficacy, and no significant effects of FGHE-status or the interaction term FGHE-status*PAP.

Table 4. OLS-regression models of transition outcomes for robustness checks.

	Achievement sample	Psychosocial sample
	Early grade performance	Sense of belonging	Academic self-efficacy
Intercept	6.209 (.190)***	2.967 (.246)***	1.591 (.120)***
PAP	.113 (.054)*	.182 (.084)*	.001 (.068)
First gen. status	−.016 (.058)	.001 (.068)	.014 (.056)
Self-efficacy (T0)	−.004 (.042)	.223 (.051)***	.410 (.042)***
Male	−.103 (.048)*	−.022 (.066)	.209 (.054)***
Achievement
Low	Ref.	Ref.	Ref.
Moderate	.494 (.055)***	.083 (.074)	−.030 (.060)
High	1.103 (.072)***	−.010 (.102)	−.007 (.083)
PAP*First gen.	−.019 (.102)	.066 (.173)	.102 (.141)
Faculty
Medical studies	Ref.	Ref.	Ref.
Economics	−.406 (.101)***	−.315 (.143)*	−.108 (.117)
Humanities	.589 (.098)***	.112 (.207)	.450 (.169)**
Law	−.362 (.080)***	−.028 (.158)	.193 (.129)
Social Sciences	.435 (.071)***	−.271 (.113)*	.347 (.092)***
Business	.812 (.073)***	−.306 (.120)*	.374 (.098)***
Double degree^a	−.097 (.111)	.066 (.173)	.085 (.152)

Note: Standard error is presented between brackets.

^a Dubble degree students are either Law and Economics students or Law and Business students.

*p < .05; **p < .01; ***p < .001.

Path analysis of mediation via on-campus social capital

To examine whether the positive effects of PAP on EGPA and sense of belonging were mediated by a positive effect of PAP on mobilization of peer social capital (i.e. formal and informal peer interactions) and faculty social capital (i.e. formal and informal faculty interactions), we performed a path analysis, using a stepwise procedure. The baseline model included all effects of PAP on the two outcome variables EGPA and sense of belonging, the effects of the four peer and faculty variables on these two outcome variables, and the effects of PAP on the peer and faculty interaction variables. This model achieved the following fit indices: χ² (6) =  831.712; p < .001 CFI = .204, TLI = −1.788, RMSEA = .462; 90% CI[.435;.488], implying poor model fit. After including residual correlations between the four interaction variables and excluding non-significant paths between the interaction variables and the two outcome variables, the fit indices indicated good fit of the final model: χ² (1) = 0.09, p = .764, CFI = 1.00, TLI = 1.02, RMSEA = .00; 90% CI[.00; .07]. This model is depicted in Figure 1. Table 5 shows the standardized estimates for the final model.

Figure 1. Model of the relationships between PAP, peer and faculty interactions, and the transition outcomes. Note: Significant paths (boldfaced p ≤ 0.001) and standardized coefficients are displayed.

Table 5. Summary of unstandardized coefficients for the final path model (Figure 1).

			95% confidence interval
	Unstandardized coefficients	Standard error	Lower bound	Upper bound
Early grade performance^a
PAP	0.248**	0.091	0.069	0.428
FPI	0.089	0.052	−0.013	0.191
FSI	−0.101	0.053	−0.213	0.007
Sense of belonging^a
PAP	0.173**	0.063	0.050	0.296
IPI	0.197***	0.035	0.128	0.266
FPI	0.176***	0.048	0.083	0.269
FSI	0.175***	0.039	0.099	0.251
IPI^a
PAP	0.265**	0.096	0.076	0.454
FPI^a
PAP	0.202**	0.074	0.057	0.347
ISI^a
PAP	0.048	0.079	−0.107	0.203
FSI^a
PAP	0.060	0.068	−0.073	0.194

Note: N = 588. FPI: Formal peer interaction; IPI: Informal peer interaction FSI: Formal staff interaction; ISI: Informal staff interaction.

^a Endogenous variables.

*p < .05; **p < .01; ***p < .001.

The model in Figure 1 indicated a positive and significant relation of PAP with the two variables indicating mobilization of peer social capital, i.e. formal peer interactions (b = .202; p = .006) and informal peer interactions (b = .265; p = .006), but not with faculty interactions. Figure 1 further shows a strong positive relation between (in)formal peer interaction and formal faculty interaction with sense of belonging. EGPA was not significantly related to any of the interaction variables.

To test H4, we further examined indirect effects of PAP on sense of belonging and EGPA via the (in)formal interaction variables, for which we calculated 95% CI with a bootstrap procedure of 1000 iterations. These calculations indicated indirect significant relationships of PAP with sense of belonging via formal peer interactions (b^*= 0.036, [0.007; 0.076], p = .039) and informal peer interactions (b^*= 0.52, [0.012; 0.098], p = .018). Students who participated in PAP thus had more self-reported formal and informal interactions with their peers, which in turn had a positive effect on their sense of belonging in HE. No significant indirect relationship was present between participation in PAP and EGPA, via students’ peer interactions (b^*= 0.02 [−0.001; 0.047], p = .140).

Discussion and conclusion

In the present study, we examined whether PAP, a pre-academic transition programme organized during the encounter stage of the transition to HE (Nicholson 1990; Van Herpen et al. 2020), supported first-year students by impacting three outcomes indicative of a successful transition: early grade performance, sense of belonging and academic self-efficacy. To examine whether the programmes’ strategies to organize inclusive support were successful, we further examined whether the programmes’ effects differed between FGHE- and CGHE-students. Lastly, we analysed whether positive effects of the programme on the transition outcomes were mediated by an effect on various measures of on-campus social capital (i.e. formal and informal interaction with peers and faculty).

The results indicated that PAP seems to have small but positive impact on students’ early academic achievement (H1) and sense of belonging in HE (H2). The analyses of PAP’s outcome on academic self-efficacy (H3) indicated no difference between participants and non-participants. Effects on self-efficacy were absent both directly after PAP and after the first month in HE. Moderation analyses further showed that programme effects did not vary depending on students’ generation-status in HE: FGHE- and CGHE-students benefitted from the programme to an equal extent. Path analysis lastly indicated that PAP participants had mobilized more peer social capital (H5), but that an effect on mobilization of faculty social capital was absent (H4). Mobilization of peer social capital was in turn positively related to sense of belonging in HE, suggesting that a positive effect of PAP on this outcome could be explained by an improvement of participants’ on-campus social capital.

To offer inclusive support, PAP adopted strategies that explicitly address students’ diverse experiences during the transition to HE, influenced by students’ social background. Firstly, informed by multicultural education (Denson 2009) and difference-education interventions (Stephens, Hamedani, and Destin 2014; Townsend, Stephens, and Hamedani 2021), participants reflected on visible and invisible aspects of their identity, the influence thereof on experiences in education, and the possible resources that students could draw from their identities. Secondly, PAP aimed to enhance students’ on-campus social capital (Schwartz et al. 2018), informed by research indicating difficulties among FGHE-students to build on-campus connections (Álvarez-Rivadulla et al. 2022; Mishra 2020). PAP’s positive effect on sense of belonging through mobilization of peer social capital partly offers support for this intended mechanism.

The finding that participation in PAP was unrelated to mobilization of faculty capital contradicts prior research on social capital transition programmes (Parnes et al. 2020; Schwartz et al. 2018). PAP aimed to impact these connections with faculty by emphasizing their importance, by discussing ways to build these connections, and by reducing internal barriers to approach faculty. These modules were, however, more theoretical in nature and therefore not adapted to the online COVID-19 context, which substantially altered student–faculty connections. These theoretical modules were also not turned directly into practice within the online programme environment, as there were only limited possibilities to actually meet faculty during the programme. This is contrary to modules on mobilization of peer capital, which included many possibilities to practice connecting and working together with peers in an online environment. The fact that students had limited opportunities to meet faculty during the programme also points to a more general suggestion for programme improvement. In the intervention as studied by Parnes and colleagues (2020), participants engaged in role-playing activities and interviewed individuals with whom participants wanted to connect more (e.g. staff members). These types of practical activities may be essential to provide participants with experiences to enhance orientation towards support seeking from faculty. mobilization.

A possible explanation for the absence of an effect of PAP on self-efficacy lies in the context-specificity. Self-efficacy is defined as students’ confidence to perform well in the specific context of the course programme. Informed by self-efficacy theory (Bandura 1997) and prior research on peer-led self-efficacy interventions (Bergey et al. 2019), PAP aimed to impact self-efficacy by activities in which participants discussed study strategies with their peers, based on video lectures watched prior to these discussions. These videos were not specifically tailored towards the specific courses in which participants enrolled after PAP however. These courses differed from each other in terms of content and educational design, ranging from lecture-based education to problem-based learning. Therefore, participants may have experienced doubts while transferring the learned study strategies to the new study context and did not feel more confident about their own ability to succeed academically within their courses. A possible direction for future self-efficacy intervention research might therefore be to examine whether interventions that make strong and explicit connections between the content of the programme and the specific characteristics of course programmes are more effective to influence academic self-efficacy. This connection could for instance be enhanced by organizing return sessions, including peer discussions about participants’ first experiences with studying at university and applying the study strategies that were addressed in the programme. Besides enhancing students’ confidence to successfully study in HE, return sessions may also help students to effectively apply and modify study strategies, which may strengthen the impact of the programme on early grades.

The results of the current study strengthen the evidence of earlier research on the effectiveness of difference-education interventions (Stephens, Hamedani, and Destin 2014; Townsend, Stephens, and Hamedani 2021) and social capital interventions (Parnes et al. 2020; Schwartz et al. 2018) by showing that these programmes not only have the potential to improve academic achievement, but that these programmes can also affect broader important psychosocial outcomes, such as sense of belonging in HE (Hausmann et al. 2009; Strayhorn 2018). Furthermore, while previous research often studied programme effects solely among ‘target samples’ such as FGHE-students (Greer, Chi, and Hylton-Patterson 2023) or showed that programme effects were only present for either underrepresented groups in HE, such as FGHE-students (Townsend, Stephens, and Hamedani 2021) or for majority students (Hausmann et al. 2009), PAP seemed to have facilitated the transition to HE of FGHE- and CGHE-students to an equal extent.

A limitation of the current study are selection effects that might have occurred. Analyses of the participants showed for instance an overrepresentation of students who achieved highly in pre-university education and of women. In our multilevel regression analyses, we partly adjusted for this imbalance by adding covariates in our models to control for gender and pre-university achievement. To estimate causal effects of programme participation, randomization of participant and control group is to be preferred, however. Furthermore, to offer inclusive support to first-year students, it is important that transition programmes actively aim to reach a group of participants that is more representative of the general student population.

A further important note regarding selection effects is that this is not only a limitation in the analysis of PAP’s effectiveness, but also has possibly influenced our results regarding the influence of generation-status in HE on the various transition outcomes. Although our analyses do not show a negative main effect of FGHE-status on the transition outcomes, as one might expect from previous research (Spiegler and Bednarek 2013; Wittner, Barthauer, and Kauffeld 2020), it is beyond the scope of our study to draw conclusions about the influence of FGHE-status on the transition to HE: for instance, FGHE-students who experience a lower sense of belonging in HE (Strayhorn 2018), may be underrepresented in PAP, and may also be underrepresented in our sample of students for the control group. At the same time, however, our results indicating an absent negative effect of FGHE-status on various outcomes resonate with earlier research done in the Dutch HE context (Brouwer et al. 2016; van Rooij et al. 2018) which shows a different pattern compared to much of the current research that has been done on FGHE-students, mostly in the US. These previous results and our current results indicate a need to further examine the specific challenges of FGHE-students in non-US contexts, by adopting sensitive sampling strategies.

In summary, our study showed that an online intervention organized during the encounter stage of the transition to HE supports students by positively impacting early grade performance and sense of belonging in HE, both among FGHE- and CGHE-students. These findings are relevant for developing effective transition programmes and for adopting effective strategies within the daily educational environment to improve students’ success during the transition to HE.

Disclosure statement

This article has been published under the Journal’s transparent peer review policy. Anonymized peer review reports of the submitted manuscript can be accessed under supplemental material online at (https://doi.org/10.1080/21568235.2024.2331122).

Additional information

Notes on contributors

Pieter M. van Lamoen

Pieter M. van Lamoen is a PhD student at the Department of Psychology, Education & Child Studies at Erasmus University Rotterdam, the Netherlands. His research interests include the transition from secondary to higher education, diversity and inclusion, social capital, and academic success.

Marieke Meeuwisse

Marieke Meeuwisse is an Associate Professor at the Department of Psychology, Education and Child Studies of the Erasmus University Rotterdam, the Netherlands. Central themes in her research are diversity, inclusion, and student success, with a special interest in the higher education learning environment, student and staff interactions, and sense of belonging.

Annemarie M.F. Hiemstra

Annemarie M.F. Hiemstra is an Associate Professor at the Department of Psychology, Education and Child Studies of the Erasmus University Rotterdam, the Netherlands. Her research interests include personnel selection, psychological assessment, hiring discrimination, and workplace diversity.

Lidia R. Arends

Lidia R. Arends is Professor of Methodology and Statistics at the Department of Psychology, Education &Child Studies, Erasmus University Rotterdam, the Netherlands. Besides, she is biostatistician at the Department of Biostatistics, Erasmus University Medical Center, Rotterdam, the Netherlands. Her areas of interest include research methods, (logistic) regression analysis, multilevel analysis, systematic reviews, and meta-analysis.

Sabine E. Severiens

Sabine E. Severiens is a Full Professor at the Department of Psychology, Education and Child Studies of the Erasmus University Rotterdam, the Netherlands. Her research interests include diversity and educational equity, from the perspective of the teacher (culturally responsive teaching) as well as the student (motivation, integration, and the learning environment).

Notes

1 Throughout this paper, we use the term on-campus social capital to refer to resources that students can access through connections with other actors in the HE environment (e.g. peers, staff). This can be both the physical campus, but also the online campus, which was the most relevant HE environment during the COVID-19 pandemic.