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Although a vast body of studies regarding the variables related to students’ achievement exists, only a handful has investigated how these variables combine and interact together. Such an investigation might make it possible to more accurately illustrate the heterogeneity of students enrolling in university and assess the impact of this diversity on academic achievement. The current study focused on the person-centered approach and investigated the possibility of determining significant subgroups of freshmen in the very first week of the academic year in the Belgian educational context. Using k-mean clustering, 2178 freshmen were classified according to their past performance, socioeconomic status, study choice process, and academic self-efficacy beliefs at the beginning of the first year at university. Analyses of variance were also conducted to analyze the relationship between these student clusters and academic achievement outcomes. Six distinct profiles of students were identified representing different combinations of achievement predictors. Results revealed different odds of success among these profiles. The implications of our approach to the understanding of the heterogeneity of freshmen and the first-year achievement process are discussed.
Transition from high school to university:
a person-centered approach to academic achievement
Mikaël De Clercq
&Benoît Galand
&Mariane Frenay
Received: 3 August 2015 /Revised: 13 November 2015 /Accepted: 10 March 2016 /
Published online: 23 March 2016
#Instituto Superior de Psicologia Aplicada, Lisboa, Portugal and Springer Science+Business Media Dordrecht
Abstract Although a vast body of studies regarding the variables related to studentsachieve-
ment exists, only a handful has investigated how these variables combine and interact together.
Such an investigation might make it possible to more accurately illustrate the heterogeneity of
students enrolling in university and assess the impact of this diversity on academic achieve-
ment. The current study focused on the person-centered approach and investigated the
possibility of determining significant subgroups of freshmen in the very first week of the
academic year in the Belgian educational context. Using k-mean clustering, 2178 freshmen
were classified according to their past performance, socioeconomic status, study choice
process, and academic self-efficacy beliefs at the beginning of the first year at
university. Analyses of variance were also conducted to analyze the relationship
between these student clusters and academic achievement outcomes. Six distinct
profiles of students were identified representing different combinations of achievement
predictors. Results revealed different odds of success among these profiles. The implications of
our approach to the understanding of the heterogeneity of freshmen and the first-year achieve-
ment process are discussed.
Keywords Highereducation.A cademic achie vement .Collegestudent.Entrancepatterns.Self-
Eur J Psychol Educ (2017) 32:3959
DOI 10.1007/s10212-016-0298-5
*Mikaël De Clercq;
Benoît Galand;
Mariane Frenay;
Research Institute of Psychological Sciences, Faculty of Psychology and Education, Universi
Catholique de Louvain, 10 Place Cardinal Mercier, 1348 Louvain-La-Neuve, Belgium
Department of Psychology, Université Catholique de Louvain, 10 Place Cardinal Mercier,
1348 Louvain-La-Neuve, Belgium
The freshman year is a challenging transition accompanied by major changes in students
educational environment such as unfamiliar academic tasks, new social networks, and height-
ened academic competition (Perry et al. 2001). In Belgium, 35 % of freshmen fail the freshman
year and 25 % leave higher education (Droesbeke et al. 2008). The same pattern of results is
reported across Europe, disclosing the global issue of success rates among first-year students at
university (OECD 2013). Considering educational policiesimperatives of expanding the
diversity and number of students enrolling and succeeding at university, academic achieve-
ment can be conceived as an on-going current concern (Gale and Parker 2012). An in-depth
understanding of achievement is thus needed to cope with the increased numbers of freshmen
from diverse backgrounds entering universities.
Over the years, many approaches to student achievement during the first year at university
have been proposed in the literature (Pekrun et al. 2002; Robbins et al. 2006). To date, a large
number of diverse variables, e.g., students socioeconomic status (Arulampalam et al. 2004),
attendance (Dollinger et al. 2008), study skills (Fenollar et al. 2007), self-regulated learning
(Minnaert and Janssen 1999), self-efficacy beliefs (Phan 2009), and social support (Fass and
Tubman 2002), have been depicted as having a direct impact on achievement. These findings
lend credence to a conception of academic achievement as a complex multifactorial process.
However, most of the studies have investigated the impact of these factors independently
without taking their interrelationships into account. Recent surveys have shed light on the
inadequacy of such single-factor analyses for understanding the complex process of academic
achievement (Allen et al. 2010; Busato et al. 2000; De Clercq et al. 2013;EcclesandWigfield
2002;Pintrich2003). These surveys have pointed out a gap in the current literature which
consists in testing the impact of a variable on academic achievement without considering that
this impact resides within studentsglobal functioning. According to these authors, a students
achievement is contingent on the way that individual characteristics combine. It can therefore
be assumed that the impact of a variable on achievement will vary depending on how it
combines with a students other characteristics. This study therefore aimed at screening the
possibility of developing an inclusive approach to freshman achievement that might deal with
the interplay between some important achievement factors.
In such cases, a person-centered approach such as cluster analysis can provide a more
detailed examination than a variable-centered approach (Dumais 2005; Phinney et al. 2005;
Vansteenkiste et al. 2009; Hayenga and Corpus (2010,p.372);a person-centered approach
focuses on particular combinations of variables as they exist within individuals rather than
taking each variable itself as the focal point.The basic assumption of this framework is that
human behavior is the result of dynamic interplay between several variables and can be
accurately understood by investigating these complex patterns of factors (Bergman et al.
2003;Magnusson1999). This framework also postulates that it is possible to identify distinct
typical subgroups with specific patterns in the population (Roeser and Peck 2003).
Several authors have justified such assumptions in the context of higher education (Fenollar
et al. 2007;Heikkiläetal.2011, emphasizing the heterogeneity of students enrolling in the first
year at university and underlining the challenge for educational literature to reflect this
diversity in a more comprehensive way. Numerous higher education theories have also
postulated that students bring to university a set of important characteristics that could
constitute the shaping of forces and weaknesses to adapt to the new academic context (for a
review, see Pascarella and Terenzini 2005). For example, Tintostheory of student departure
40 M. De Clercq et al.
(Tinto 1982,1997) emphasized the impact of pre-entry attributes (e.g., parentseducation,
studentsaspirations) on studentspersistence process. Astins theory of involvement (Astin
1999) highlighted that inputs (family background, academic and social pre-college experi-
ence) could intervene in the development of a students involvement in the college expe-
rience. Pascarellas model of change hypothesized that a students background characteristics
and pre-college traits could have a direct impact on his/her environment perceptions and effort
(Pascarella and Terenzini 2005). Weidmans model of undergraduate socialization postulated
that studentssocialization could differ depending on entrance socioeconomic status, past
performance, aspiration, and motivation (Padgett et al. 2010). Finally, Prices4Pmodelof
learning shed light on the impact of presagevariables (initial motivation, ability)onhow
students perceive learning contexts and initiate approaches to learning (Price 2013).
Based on the work of these authors and their theories, our study focused on cluster
analysis and investigated the possibility of determining significant subgroups of
freshmen in the very first week of the academic year in the Belgian tertiary educa-
tional system. This procedure was based on the assumption that each student enters
university with a specific and complex profile which entails specific adaptation to the
academic world. This perspective has been overlooked in the current literature where
distinctions between entrance characteristics and process variables are often blurred
(Pascarella and Terenzini 2005). The theories mentioned above have insisted on the utility of
such a distinction for understanding the differences between freshmen relative to their responses
to the same academic settings and experiences. Authors have also stressed the necessity of
promoting achievement as soon as possible, because the first months are crucial to adaptation to
the academic world (Neuville et al. 2013; Sauvé et al. 2007).
Following this line of thought, a more systematic conception of these profiles was endorsed
by focusing on four factors in particular (past performance, socioeconomic status, self-efficacy
beliefs, and study choice) documented as important achievement predictors and identified as
particularly relevant for an open access
system. The four selected factors also meet several
entrance categories endorsed by the above-mentioned theories. Recent studies have argued that
both background and psychosocial factors need to be collected in order to offer a more
comprehensive approach to studentsentrance characteristics (Dollinger et al. 2008;Allen
et al. 2010; Richardson et al. 2012).
Past performance
International findings claim that past academic performancesrepresented by high school
grade point average or a standardized achievement test scoreare the most powerful predic-
tors of achievement at university level (Díaz et al. 2001; Hackett et al. 1992; Perry et al. 2001;
Vandamme et al. 2005). Much energy has been devoted to examining past performance, and
research and theories attempting to accurately grasp the impact of this performance on
academic achievement have emerged in educational literature (Richardson et al. 2012).
Dollinger et al. (2008) concluded that past performance and abilities could explain about
37 % of the academic achievement of college students. Allen et al. (2008)foundthathigh
school grades typically explained about 28 % of freshmens grade point average. This factor is
The Belgian tertiary educational system is qualified as an open accesssystem because there is no standard-
ized testing of students or admission criteria and the successful completion of comprehensive secondary school
allows any student to enter any university program without passing an entrance test (except in engineering).
A person-centered approach to academic achievement 41
of utmost importance in open access systems which have no standardized testing of students
(De Clercq et al. 2013).
Several studies have concluded that the impact of past academic performance on freshmens
academic achievement reflects the academic preparation of students for the academic world
(Allen et al. 2008; Robbins et al. 2006). Past achievement allows easier adaptation to the new
complex environment (Díaz et al. 2001) and promotes the establishment of a stable belief in
ones ability to succeed (Elias and MacDonald 2007). Other authors have highlighted that past
performance is an indicator of studentscognitive abilities (Willingham et al. 2002). Past
performance is therefore considered by each aforementioned theory as an important entrance
characteristic. It is, for instance, included in pre-entry attributes of Tintos theory of departure
and in pre-college traits of Pascarellas model of change.
Socioeconomic status
Socioeconomic status (SES)reflected by the social, cultural, and economic resources avail-
able to students (e.g., parentseducation and income)has also been widely studied. Many
studies found that students from lower socioeconomic backgrounds had lower achievement
(Battle and Pastrana 2007; Caldas and Bankston 1997; Robbins et al. 2004). A meta-analysis
by Sirin (2005a, b) reviewed the literature on academic achievement from 1990 to 2000 and
highlighted a moderate-to-strong relationship between SES and academic achievement.
Three main explanations for this have emerged from the literature. According to
Willingham et al. (2002, p.18), the social advantages implied by higher SESwould
presumably act over a lifetime on the development of general cognitive skill in and out of
school.Other authors have claimed that the impact of SES on academic achievement could
reflect a school effect (Sackett et al. 2009;ZwickandGreen2007). Freshmen from under-
privileged backgrounds have to cope with limited school resources and have no access to an
adequate education. They are therefore ill-prepared to undertake academic studies. Finally,
sociological theories have argued that the impact of SES on academic achievement may
indicate that educational institutions reproduce the social order through sorting mechanisms.
These authors suggest that underprivileged students have few opportunities to build social and
cultural capital and are therefore ill prepared to adapt to the academic world (Linnehan et al.
2011;Mills2008). Other authors have highlighted that students from underprivileged back-
grounds often have lower confidence in their abilities to succeed (Phinney et al. 2005)and
underachieve in high school (Zwick and Green 2007). Like past performance, SES is an
integral part of background variables depicted in the above theories. It can be related to family
background in Astins model of involvement and to parentseducation in Tintostheoryof
Self-efficacy beliefs
Another line of research on psychosocial factors argues that academic self-efficacy beliefs are
of the utmost importance for student achievement in higher education. This concept was
originally developed by Bandura (1997) in the social cognitive theory. Several authors claim
that confidence in ones abilities and chances of success is strongly correlated to performance
and fosters many other variables such as mastery goal orientation, intrinsic motivation, self-
regulated learning, effort ratings, emotional competences, social integration, intention to
persist, and deep-processing study strategies (Adeyemo 2007; Bong and Skaalvik 2003;
42 M. De Clercq et al.
Fenollar et al. 2007; Torres and Solberg 2001). Elias and Macdonald (2007) highlighted that
academic self-efficacy accounted for about 22 % of variance in college achievement and went
beyond the variance accounted for by past performance. These results were corroborated by
Chemers et al. (2001) who undertook a study on first-year university students and found both a
direct and indirect powerful predictive effect of academic self-efficacy beliefs on academic
achievement, above and beyond the impact of past academic performance.
According to several authors, self-efficacy is paramount because it favors a positive
motivational, cognitive, affective and behavioral process that good adaptation to academic
requirements entails (Brown et al. 2008; Elias and MacDonald 2007; Kennett et al. 2009;
Pintrich 2003; Robbins et al. 2004;Zimmerman2000). However, as mentioned by Zimmerman
(2000), self-efficacy beliefs are not an omnibus trait but rather a multidimensional construct
differing on the basis of the domain of functioning. Therefore, global self-efficacy beliefs may
be less predictive in explaining differences in academic achievement than domain-specific self-
efficacy beliefs. From a theoretical point of view, academic self-efficacy beliefs are not
traditionally included as entrance characteristics even though they could be related to pre-
college experience in Astins theory of involvement. However, considering the major impact of
this variable on the achievement process, the variable was embedded in the analyses.
Study choice
Finally, in certain countries such as Belgium or the Netherlands where access to university is
much more open, the study choice process seems to play a key role in achievement. In such a
context, this process is often initiated much later than in more restrictive systems. It is therefore
essential to analyze the impact of the study choice process on achievement among first-year
students at university. Informed choice can be defined as the complexity of a studentsstudy
choice process (Biémar et al. 2003). Several research studies and theories such as the future
time perspective theory (Husman and Lens 1999) and educational choice implementation
(Germeijs and Verschueren 2007) have highlighted the importance of the information process.
Findings led to the conclusion that students who make an informed and thoughtful study
choice attain higher academic achievement, are more satisfied with their courses, apply more
adaptive study strategies, and are more confident in their ability to succeed (Biémar et al. 2003;
Husman and Lens 1999;Lensetal.2002). However, this field of research remains underde-
veloped in the higher education context and needs further investigation to support these initial
findings. The notion of study choice is quite close to the aspiration highlighted in the models
developed by Tinto, Pascarella, and Weidman. These models justified the consideration of
aspiration as a significant entrance characteristic.
Research questions addressed
The studies reviewed above have highlighted important predictors of academic success.
However, although they have revealed some insights into the relationship between these
predictors, none of them has investigated their complex combinations within students and
the way these relate to academic achievement. As mentioned above, the present study was
aimed at developing a person-oriented conception of entrance factors that affect students
achievement during their first year of university in the Belgian educational system.
Consequently, the outline of our study was determined by considering the limitations of extant
literature and addressing the following two research questions:
A person-centered approach to academic achievement 43
RQ1. Can meaningful subgroups of freshmen with specific combinations of entrance
variables be identified?
RQ2. Do these subgroups differ regarding achievement?
We might hypothesize that several complex profiles could emerge from the broad panel of
students entering the university, each profile related to specific odds of success. Several
hypotheses could be drawn on the manner in which factors might combine in pattern-
centered analyses.
First, according to the empirical studies highlighting that SES, past performance, self-
efficacy beliefs, and informed choice are connected (Husman and Lens 1999; Phinney et al.
2005; Zwick and Green 2007), we could adopt a global perspective and postulate that these
variables will evolve together. As a result, one factors score will have an impact on the scores
of the other factors. Three subgroups might therefore be expected, respectively characterized
by low, average, and high scores on the four factors. A subgroup with low scores on all the
factors is expected to be the least successful. Conversely, a subgroup with high scores is
assumed to be the most successful.
Second, we could move toward a specific point of view. Four subgroups of students
respectively characterized by a specific weakness in one of the four factors under investigation
are expected. These four subgroups could allow us to consider the impact of each entrance
weaknesses when it is offset by other forces. According to the core impact of these factors in
extant literature (Allen et al. 2008; Elias and Macdonald 2007), it is expected that a subgroup
with particularly low past-performance or academic self-efficacy beliefs will demonstrate
significantly lower performance than the two other subgroups. We postulated that these two
weaknesses would scarcely be compensated by other entrance forces.
Sample and procedure
Two thousand one hundred seventy-eight first-year university students from 29 different study
programs (psychology, law, economics, medicine, physical education, chemistry, computer
science, engineering) in a Belgian university participated in a survey on a voluntary basis
(48.5 % of the population of first-year students with a mean age of 18.3 years). Of the study
participants, 52.9 % were women. The questionnaire was administered during a lecture session
at the beginning of the year, and the student grade point average was collected, with students
consent, at the end of the academic year. One thousand nine hundred thirty-three students
consented to the collection of their grade point average. Independent ttest analyses were
performed to compare initial scores on entrance variables of students who gave their consent
with those who did not. No significant difference was found, which strengthens the assumption
that our sample is representative.
Socioeconomic status In order to develop an accurate measure of SES, multiple indicators
were collected and combined in an overall SES measure. Three indicators of SES were
gathered, namely parental education (mothers and fathers highest educational level), home
44 M. De Clercq et al.
possessions (i.e., number of cars, computers, art objects, musical instruments, literature books,
poetry books, etc.), and the students sociocultural activities (i.e., traveling abroad, going to the
theater, visiting museums, skiing during the winter holidays, etc.; alpha= 0.67). Recent studies
have supported such a composite measure (Sackett et al. 2009;Sirin2005a, b). Exploratory
factor analysis revealed one single factor (eigenvalue higher than 1) explaining more than
40 % of the variance, with factor loadings ranging from 0.57 to 0.72. The factor score was
extracted to create an overall SES indicator.
High school grade Participants were asked to report their overall average percentage in their
final year of high school on a four-point scale (1, 6070 %; 2, 7080 %; 3, 8090 %; 4, more
than 90 %).
Academic self-efficacy beliefs Academic self-efficacy beliefs were assessed through seven
items adapted from Galand (2004)(e.g.,As long as I do my work, ImsureIcan
succeed this year; alpha = 0.72). Items were answered on a five-point scale from
1=totally disagreeto 5 = totally agree.Exploratory factor analysis revealed one
single factor (eigenvalue higher than 1) explaining more than 50 % of the variance. Factor
loadings ranged from 0.55 to 0.77.
Informed choice This variable was operationalized as the number of actions of information
undertaken by students in their study choice process. Participants were invited to
consider six actions of information (discussions with teachers, appointments with
guidance counselors, meeting professionals of the field, spontaneously gathering
information about ones possible education and future career, reflecting upon ones
professional choice and job prospects, thinking about the different possible higher
education programs after secondary education) and were then asked which ones they
undertook in their choice-making process. The reliability coefficient for this scale was
good (alpha = 0.74).
Outcome variables Continuous variables: Academic achievement was represented by the
average percentage scores in all courses at the end of the academic year. This measure was
collected from department records and used as an overall indicator of achievement. To be
successful in the tertiary education system of the Belgian-French community, an overall final
percentage of 60 is required. Categorical variable: A second measure was created through
categorical variables in order to provide another appraisal of academic achievement.
Achievement scores were divided into four dimensions representing the four typical
student outcomes at the end of the year. The first dimension, entitled attrition,
depicted students who obtained between 0 and 30 % as their final percentage: these
students were considered to have dropped out of the academic year. The second
dimension, entitled failure,brought together students with a final percentage ranging
from 30 to 60 %: these students actually committed to their academic year but did not
succeed. The third dimension, entitled success,represented students with a final
percentage ranging from 60 to 70 %: these students completed the academic year
satisfactorily but did not gain honors. The final group was made up of students whose
final percentage was over 70 % and was called the honor roll.Of students, 16.4 %
were classified in the attrition group, 29.5 % in the failure group, 37.1 % in the success
group, and 17 % in the honor roll group.
A person-centered approach to academic achievement 45
In line with the assertions made by several authors (Daniels et al. 2008; Peck and Roeser 2003;
Phinney et al. 2005; Valle et al. 2008), k-means cluster analyses were run with regard to
entrance achievement factors in order to identify the most highly similar groups of students
within groups and the most highly dissimilar students between groups. According to Daniels
and colleagues (Daniels et al. 2008), such methods can enable an unbiased investigation of
natural occurring patterns among college students and a better understanding of the combined
effects of the variables of interest. Furthermore, Neuville and Bourgeois (2007)haveargued
that the k-means method is suitable for handling a large data set (>150 subjects) and can
compensate for a low initial partition of data. However, it is important to understand that the
selection and interpretation of clusters are essentially guided by researchers rather than
by analytical results. Nonetheless, to carry out our selection, we considered the
distribution of students in each cluster and the adjustment of the final cluster solution
to the entrance factor variability using multivariate analysis of variance (MANOVA).
Once the final cluster solution was set up, analyses of variance (ANOVA) and
descriptive analyses were used to compare clusters in terms of grade point average.
Finally, categorical measures of achievement were analyzed through cross-tabulation and
chi-square tests.
Preliminary analysis
Table 1displays the zero-order correlations and summary statistics of each studys variables.
The scores corresponding to skewness and kurtosis were found to be within the normal values.
As expected, high school grade, SES, academic self-efficacy beliefs, and informed choice were
positively correlated (p<.001) with academic achievement. Although correlations were small-
er than expected, these results lend credence to the predictive value of these entrance variables
for final academic achievement.
Tab le 1 Means, standard deviations, skewness, kurtosis, and correlations of the study variables
Min Max MSD Skew. Kurt. 1 2 3 4 5
1. High school
1 4 1.77 0.75 0.64 0.24
2. Socioeconomic
0.00 1.00 0.52 0.24 0.14***
3. Academic
1 5 3.52 0.55 0.21 0.09 0.13*** 0.12***
4. Informed choice 0 6 4.54 1.34 0.83 0.26 0.07** 0.16*** 0.02
5. Academic
0 100 59.1 13.9 0.80 0.32 0.37*** 0.12*** 0.11*** 0.10***
*p< .05;**p<.01;***p<.001
46 M. De Clercq et al.
Cluster analysis
As entrance factor scales have different metrics, each one was standardized through Z-
transformations before being entered into the cluster analysis. Five k-means cluster analyses
were run separately specifying two-, three-, four-, five-, and six-cluster solutions. Based on
several indices such as the number of iterations, sample sizes in each cluster, and interpret-
ability, the six-cluster solution was eventually selected as the most meaningful distinction. The
reliability of this solution was also investigated through the cross-validation procedure de-
scribed below. The final cluster centroids are described in Table 2and illustrated in Fig. 1.
Centroids can be defined as studentsmeans in high school grade, SES, academic self-
efficacy beliefs, and informed choice in each cluster. A cautionary note is nevertheless required
in the interpretation of these scores. As each scale was standardized, a positive centroid does
not reflect a high score per se but rather, a higher score than the overall sample mean.
Centroids must thus be understood as a normative assessment and not as a criterion-
referenced assessment. For instance, a cluster with a positive centroid on informed choice
was composed of students who made more thoughtful study choices than average students
from the sample. Finally, MANOVA was computed with cluster-membership as the between-
subjects factor and the four cluster variables as dependent variables. This analysis revealed
whether clusters differed across entrance variables. The overall MANOVA was significant, and
Roys largest root = 2.06, F(5, 2122) = 873.29, p< .001. As shown in Table 2, the univariate
test for each cluster variable was significant and showed that cluster membership explained
more than 50 % of the variance of the four variables used to create the clusters. These results
lend credence to the cluster solution as a meaningful depiction of entrance studentsdistribu-
tion and support the fact that each selected entrance variable contributes to the cluster solution.
Validation of the cluster solution A cross-validation procedure was set up to assess the
replication of the six-cluster solution (Breckenridge 2000; Phinney et al. 2005). To this end, the
data set was randomly divided into two samples (sample 1, n= 1069 and sample 2, n= 1109).
Subsequently, k-means clustersspecifying a six-cluster solutionwere performed separately on
samples 1 and 2 using the cluster centroid derived from the global sample. Based on the work of
Cohen (1960), the agreement between the cluster solutions for the whole sample and for the two
subsamples was average to high (kappa = 0.62 for Sample 1; kappa = 0.58 for sample 2). This
procedure thus proved that the six-cluster solution had good reliability in the data set.
Characteristics of the clusters As mentioned earlier, statistical and theoretical criteria led to
our choice of a six-cluster solution which revealed meaningful profiles highlighting specific
patterns of factors. The description and interpretation of these clusters are presented below.
1. Disadvantaged (n=280; 12.9 %). The first cluster depicted students with the most negative
profiles. These were characterized by a negative centroid on the four entrance achievement
factors. In addition to a score on academic self-efficacy beliefs that was more than one standard
deviation below the mean score, low scores on SES, high school grades, and informed choice
were also reported. A typical student from this cluster would thus originate from a lower
socioeconomic background, report weaker high school grades, have substantially lower
confidence in his/her ability to succeed, and operate a less informed study choice process.
2. Poor performer (n= 429; 19.7 %). The second cluster was labeled poor performer due to
the extremely negative centroid (more than a standard deviation from the mean) on high
A person-centered approach to academic achievement 47
Tab le 2 Cluster centroids and multivariate analysis of variance
Entrance profiles Cluster 1 :
Cluster 2: poor
Cluster 3:
Cluster 4:
Cluster 5:
Cluster 6:
n280(12.9 %) 429 (19.7 %) 276 (12.7 %) 291 (13.4 %) 417 (19.1 %) 435 (20.0 %)
High school grade 0.66 b 1.04 a 0.23 c 0.06 d 0.71 e 0.87 f 509.49*** 0.55
Socioeconomic status 0.93 b 0.48 de 0.14 c 1.33 a 0.38 d 0.61 e 451.29*** 0.52
Academic self-efficacy beliefs 1.26 a 0.08 c 0.20 c 0.54 d 0.69 b 0.91 e 462.33*** 0.52
Informed choice 0.34 b 0.50 d 1.71 a 0.02 c 0.45 d 0.38 d 458.17*** 0.52
Letters indicate post hoc comparison grouping based on Tukeys HSD; cluster centroids with different letters differ significantly
*p< .05;**p< .01;***p<.001
48 M. De Clercq et al.
school grade. Indeed, these students reported lower high school grades than the average
student. However, they had also made more thoughtful study choices and came from more
privileged backgrounds than the average freshman.
3. Thoughtless (n= 276; 12.7 %). The third cluster was entitled the thoughtless profile.Students
in this cluster showed a highly negative centroid on informed choice, a negative centroid on
high school grade, but a positive centroid on SES and academic self-efficacy beliefs. This kind
of student came from a more privileged background and entered university with a higher
confidence in his/her ability to succeed compared to the average student, notwithstanding lower
high school grades and a poor study choice process. Students in this profile had not initiated
any real complex study choice process and were thus potentially vulnerable to attrition.
4. Underprivileged (n= 291; 13.4 %).The fourth cluster was particularly characterized by an
extremely negative centroid on socioeconomic background and was labeled the under-
privileged profile. Such students came from a more underprivileged background but had
higher academic self-efficacy beliefs.
5. Apprehensive (n=417; 19.1 %). The fifth cluster exhibited a favorable profile except for
academic self-efficacy beliefs; it was labeled the apprehensive profile. Despite positive
centroids on high school grades, SES, and informed choice, this profile had a negative
centroid on academic self-efficacy beliefs.
6. Advantaged (n= 435; 20.0 %). The final cluster depicted students with the most positive
profiles and was thus labeled the advantaged profile. In contrast to the disadvantaged profile,
these students had initiated a complex study choice, possessed great confidence in their abilities
to succeed, reported high grades in high school, and came from privileged backgrounds.
Fig. 1 Final centroids of entrance achievement factors for each profile
A person-centered approach to academic achievement 49
Clusters and academic outcomes Using a one-way ANOVA, we found significant differ-
ences between the clusters across the average percentage at the end of the year F(5,
1928) = 37.64, p<.001,ŋ
= 0.13. The disadvantaged profile exhibited the lowest average final
percentage of the six clusters. The poor performer, thoughtless, and underprivileged profiles
also displayed an average final percentage below the minimum required to pass the academic
year. Special attention should thus be paid to students in these clusters. By contrast, the
apprehensive and advantageous profiles showed average performances above the minimum
60 % required to pass the year in the Belgian educational context.
More precisely, post hoc comparisons across groups based on Tukeys honestly significant
difference (HSD) test showed that the disadvantaged profile (M= 55.4; SD = 11.6) had a
significantly lower percentage than all other profiles except the poor-performer profile (see
Table 3for post hoc detailed results). Second, the poor performer (M= 57.6; SD = 10.7) had a
significantly lower percentage than the apprehensive and advantaged profiles, but not the other
three profiles. Third, the thoughtless (M= 59.1; SD = 11.3) and underprivileged profiles
(M= 59.9: SD = 10.5) showed a significantly lower percentage than the apprehensive and
advantaged profiles and a higher percentage than the disadvantaged profile. However, they
did not differ from one another and from the poor performers. Fourth, the apprehensive
(M= 63.9; SD=10.9) and advantaged profiles (M= 66.1; SD = 10.9) both had a significantly
higher percentage than the four other profiles but did not differ from one another. The
underprivileged profile was therefore classified in the low achiever level.
Descriptive statistics allowed us to clarify these results. Frequencies were investigated on four
levels of academic outcomes: attrition, failure, success, and the honor roll. Initial results revealed
that failure and attrition rates were 16.4 and 29.5 %, respectively. These percentages are less
significant than the national rates of 25 % for attrition and 35 % for failure (Droesbeke et al. 2008).
Using a chi-square test for independence and cross-tabulation (Greenwood and Nikulin
1996), the association between the six clusters and the four typical student outcomes at the end
of the year was investigated. The results described in Table 4highlight significant variations in
the proportion of academic outcomes from one profile to another (χ
(15) = 238.66; p< .001).
Cramers V coefficient revealed a moderate association (V= 0.35; p< .001) between the
clusters to which students belonged and final outcomes.
More precisely, the attrition rates were above average for disadvantaged, poor performer,
thoughtless, and underprivileged profiles. Only the apprehensive and advantageous profiles
Tab le 3 Post hoc comparison among the six entrance profiles on final percentage
Entrance profiles Mean on final
Mean differences across clusters
1 23456
1. Disadvantaged 55.45 a
2. Poor performer 57.57 ab 2.12
3. Thoughtless 59.09 b 3.64* 1.52
4. Underprivileged 59.95 b 4.50** 2.38 0.86
5. Apprehensive 63.94 c 8.49*** 6.37*** 4.85*** 3.99**
6. Advantaged 66.09 c 10.64*** 8.52*** 7.00*** 6.14*** 2.15
Letters indicate post hoc comparison grouping based on Tukeys HSD; means with different letters differ
*p< .05;**p<.01;***p<.001
50 M. De Clercq et al.
delivered percentages below average. Attrition was especially high for the disadvantaged
profile in which 25.1 % of students dropped out before the end of the first year. Failure rates
were also above average in the disadvantaged, poor performer, thoughtless, and underprivi-
leged profiles. Once again, the failure rate for the disadvantaged profile was particularly high:
more than 40 % of students failed. Conversely, the apprehensive and advantaged profiles
presented failure rates below average. Considering attrition and failure together, more than
65 % of students classified in the disadvantaged profile were unable to cope with the academic
requirements of their first year at university. More surprisingly, only the disadvantaged profile
was found to have a success rate below average. All the other profiles showed a success rate of
approximately 40 %. The main distinction relating to the success rate lay in the honor roll
category where the success rate for the apprehensive and advantaged profiles was more than
twice that of the other profiles. Moreover, the success rate for these two profiles was higher
than 60 % when considering success and honor roll outcomes together.
Our goal in this study was to further clarify the possibility of developing an inclusive approach
to freshman achievement that might address the interplay between some important entrance
achievement factors in the Belgian tertiary education context. More precisely, the study
attempted to test the feasibility of identifying meaningful subgroups and analyzed the extent
to which being a member of a specific subgroup impacted on student academic success.
University entrance based on a person-centered approach
The first research question was: Can meaningful subgroups of freshmen with specific
combinations of entrance variables be identified?The results provided empirical evidence
Tab le 4 Crosstabs cluster membership × achievement outcomes
Four-level academic outcomes
Attrition Failure Success Honor roll Total
1. Disadvantaged 61 105 61 16 243
25.1 % 43.2 % 25.1 % 6.6 % 100.0 %
2. Poor performer 76 138 153 23 390
19.5 % 35.4 % 39.2 % 5.9 % 100.0 %
3. Thoughtless 49 77 97 26 249
19.7 % 30.9 % 39.0 % 10.4 % 100.0 %
4. Underprivileged 57 84 99 27 267
21.3 % 31.5 % 37.1 % 10.1 % 100.0 %
5. Apprehensive 40 91 148 101 380
10.5 % 23.9 % 38.9 % 26.6 % 100.0 %
6. Advantaged 34 75 159 136 404
8.4 % 18.6 % 39.4 % 33.7 % 100.0 %
Total 317 570 717 329 1933
16.4 % 29.5 % 37.1 % 17 % 100 %
A person-centered approach to academic achievement 51
that various profiles can be identified at the beginning of the year, each constituting a unique
combination of predictors. This finding is in line with several higher education theories which
postulate that students enter university with a set of specific characteristics (Pascarella and
Terenzini 2005). More precisely, in addition to entirely favorable and adverse profiles, cluster
analysis highlighted complex profiles including a combination of high and low scores on the
four entrance variables. These results support two of our assumptions concerning the type of
profile that might emerge from analyses.
First, the assumption that clusters with specific weaknesses could emerge from the analysis
was corroborated by the identification of four subgroups of students with particularly low
scores in one of the four variables investigated. This finding provides rationales to consider the
contribution of each specific line of research in the academic achievement issue. In this line of
thought, research and theories about specific achievement predictors might endorse comple-
mentary perspectives of achievement, each perspective being a good fit for a specific profile of
freshmen. The vast body of knowledge that has attempted to explain the impact of past
performance on achievement (Allen et al. 2008; Elias and MacDonald 2007; Robbins et al.
2006) could be particularly relevant in understanding the adaptation of the poor-performer
profile. Authors who have undertaken in-depth investigations of the actual impact of SES on
achievement (Linnehan et al. 2011;Mills2008; Sackett et al. 2009; Zwick and Green 2007)
have been able to clearly depict specific difficulties with which the underprivileged profile has
to struggle. Banduras social cognitive theory (Bandura 1997) could pinpoint specific proximal
variables that could foster achievement in the apprehensive profile. Finally, future time
perspective and educational choice implementation theories (Germeijs and Verschueren
2007; Husman and Lens 1999) may be of major interest for an understanding of the
achievement process of the thoughtless profile. A substantial lens through which to understand
the thoughtless profile might also be found in Berzonskys identity process style which
postulates that students in this profile are more diffuse-avoidant-oriented individuals
(Berzonsky and Kuk 2000). However, further investigation is required to corroborate this
Second, the depiction of clusters with low and high scores on the four variables partially
confirmed our expectation of clusters composed of variables evolving together. This finding
substantiates the importance of the inclusive approach of achievement (Allen et al. 2010;De
Clercq et al. 2013). We can postulate that students endorsing cumulative strengths or weak-
nesses show specific adaptation to the academic context that differs from students with a
specific weakness. Consequently, an in-depth investigation of the combined impact of achieve-
ment predictors should be an on-going concern in attempts to understand achievement from
disadvantaged and advantaged profiles.
From clusters to final outcomes
The second research question was as follows: Do different subgroups differ regarding
achievement?To respond to this question, we highlighted significant differences between
profiles with regard to overall percentages and the proportion of successful students. The
analyses also demonstrated that students in the disadvantaged profile are particularly vulner-
able to attrition and failure and that those in the advantaged profile are presumably better
prepared to face the first year at university. Focusing on the categorical measure of achieve-
ment, results revealed that more than 65 % of students classified in the disadvantaged profile
failed whereas more than 70 % in the advantaged profile passed the year.
52 M. De Clercq et al.
The results also highlighted that the apprehensive profile emerged as a high achievers
profile. Thusnotwithstanding the literature that argues that academic self-efficacy beliefs are
a key factor necessary for academic achievementstudents with lower confidence in their
abilities to succeed than the average freshman might be good achievers, as this weak point can
be offset by other achievement factors. This finding illustrates the utility of evaluating the
combination of factors to develop a precise conceptualization of academic achievement. The
actual effect of a variable on achievement may depend on how it combines with other student
characteristics. It also partially qualifies the plethora of studies on the strong impact of self-
efficacy beliefs on achievement (Chemers et al. 2001).
It is also worth noting that preliminary results revealed a significant relationship between
the four variables and academic achievement. While high school grades had a moderate
relationship with achievement, SES, informed choice, and self-efficacy beliefs had a weak
positive relationship with final performance. The relatively low impact of these variables was
quite surprising, especially for academic self-efficacy beliefs which are often reported as strong
predictors of achievement. Two main explanations are possible. First as mentioned above, self-
efficacy beliefs differ according to the domain of functioning. Therefore, global confidence in
ones ability to succeed must essentially be considered to be at the apex of academic self-
hierarchy and to have an indirect impact on academic achievement by initiating the develop-
ment of domain-specific academic self-efficacy beliefs (Zimmerman 2000). Elias and
MacDonald (2007) provide the second explanation. They argue that self-efficacy beliefs are
not good predictors of performance in new academic contexts such as the first year at
university. According to them, when facing a new learning context, past performance is the
main predictor of achievement and demonstrates studentsglobal learning abilities. This line of
thought concurs with our findings.
With regard to our results, achievement differs depending on different subgroups. More
precisely, the combination of risk factors could have a severe negative impact on a students
achievement, whereas the impact of an isolated weakness could be offset by a students
strengths. Entrance characteristics could thus have a cumulative impact on academic achieve-
ment. However, a more detailed analysis of the results revealed that the scope of this finding
requires further assessment. First, entrance profiles explained only about 13 % of final grade
variance. Second, every cluster included both successful and unsuccessful students. It thus
appears necessary to relativize the predictive value of a students entrance characteristics and
consider the impact of the achievement process during the academic year. Following this idea,
students with cumulative entrance weaknesses (disadvantaged profiles) might therefore suc-
ceed if their adaptation during the year is particularly successful.
Practical implications
Taken together, these results can pinpointfrom the very beginning of the yearthose
students who are at risk of failure and thus need specific attention. From a pragmatic view,
the early identification of profiles could be useful to prevent drop-out and failure. Such a
person-centered approach could provide tools to capture and consider studentsspecific
differences in achievement and retention interventions. This perspective could lead to a shift
from the undifferentiated promotion of achievement to the establishment of tailor-made
interventions to fit the specific needs of students according to their profiles. For example,
students from the apprehensive profile might participate in psychosocial self-affirmation
interventions that target their beliefs and feelings about the university in order to promote
A person-centered approach to academic achievement 53
their confidence in their ability to succeed (Cohen et al. 2009). Moreover, characterizing
specific patterns from the very beginning of the year also provides some indications that make
it possible to intervene in the early promotion of academic achievement, before students fall
too far behind to reduce the gaps (Neuville et al. 2013).
To some extent, we might also question the relevance of the university entrance selection
process. Our findings highlighted that, in each entrance profile, a significant proportion of
students actually passed the first year. This assertion was also true for students with cumulative
entrance weaknesses whoin more restrictive systemswould probably not gain entry to
university. Consequently, this paper suggests that the open access system might be considered
a viable perspective to democratize access to higher education, in line with the aspirational
expansion of higher education set out in educational policies in many European countries
(Gale and Parker 2012).
Limitations and future perspectives
Among the limitations of this study, three should be pointed out. First, our approach to
achievement factors has not taken cognitive factors into account. However, cognitive variables
such as learning strategies and self-regulation have been shown to have a significant impact on
achievement (Fenollar et al. 2007; Minnaert and Janssen 1999;Notaetal.2004;Pintrichand
De Groot 1990; Shell and Husman 2008; Vermunt 2005; Ward and Walker 2008). An
estimation of freshmens cognitive strategies could thus enrich our approach to students
entrance profiles. Second, studies have depicted informed choice as a meaningful entrance
factor in an open-access educational system such as in the Netherlands or Belgium. However,
such a variable may be of minor concern in more restrictive systems such as the English
educational system. Replicating this study in a country where access to higher education is
highly contingent on school performance and/or SAT scores may thus be interesting. Finally,
the past performance measure used in the study can be criticized. As no standardized test
scores exist in the Belgian educational context, past performance was measured using a four-
point item assessing high school final average percentages. This measure is quite global and
self-reported, and it does not consider educational track, specialization in secondary education,
and the characteristics of the high schools. Given the circumstances, the reliability of high
school grades as a reliable indicator of studentsactual past performance is questionable
(Zwick and Green 2007).
The major contribution of these findings lies in the fact that they highlight the relevance of
taking a person-centered approach into account and make it possible to depict different
meaningful entrance profiles. However, many questions remain unanswered, and a comple-
mentary future issue would be to investigate the relation between these entrance profiles and
more proximal achievement variables such as attendance, social integration, and teaching
practice. We might presumably infer that freshmen within distinct profiles adapt differently to
the academic world. In other words, studentsentrance characteristics could entail a specific
achievement process with specific variables. For instance, a poor-performer profile could
involve other adaptation difficulties compared to a thoughtless profile. The impact of proximal
predictors of achievement such as academic engagement, motivation, and social integration
may therefore vary from one profile to another. Several theories support such an assumption.
For example, Astins theory of involvement (Astin 1999) postulates that studentspre-entry
attributes will affect their engagement during the academic year. Future studies might therefore
determine whether the pattern of relationships between academic achievement and proximal
54 M. De Clercq et al.
variables is the same across entrance profiles. Further investigation of this question might be
attempted through multigroup structural equation modeling. Based on the literature, an overall
model of the achievement process at university could be tested while investigating the ways in
which it varies depending on studentsentrance characteristics.
Another future perspective could be to more deeply assess atypical students in the different
profiles. On the one hand, it would be quite interesting to explore how successful students
from the disadvantaged profile cope with first-year university difficulties to achieve academic
success. On the other hand, scrutinizing students from the advantaged profile who fail or drop
out of the first year of university could also provide a more in-depth understanding of the
achievement issue. Perry et al. (2001) have emphasized the necessity of fully grasping this
paradox of failure,namely why bright high school students fail when they enter university.
This assessment might be undertaken by combining a person-centered approach with qualita-
tive procedures.
In conclusion, our approach has provided some clues to differentiate students who cope
with the transition from high school to university from those who do not. However, in order to
deepen our understanding of the former issue and address the latter, our findings need to be
replicated. Moreover, they must be combined with more specific analytical approaches such as
a qualitative approach or structural equation modeling. This would enable a dynamic and
comprehensive conceptualization of academic achievement to be carried out.
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(0)10 47 86 27; URL:, Web site:
Mikaël De Clercq is a Ph.D student at the Université catholique de Louvain.
His research interests focus on the predictors of academic achievement among first-year university students and
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Most relevant publications in the field of Psychology of Education:
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Benoît Galand. Department of Psychology, Université Catholique de Louvain, 10 Place Cardinal Mercier,
? B-1348 Louvain-La-Neuve, Belgium. Phone:; Web site:
Benoît Galand obtained a Ph.D. degree in psychology from the University of Louvain in 2001.
He is a professor in the department of Psychology of UCL. His research interests revolve around the effects of
instructional practices on student motivation, learning, and psycho-social adaptation. He lectures in educational
psychology, pedagogy, and research methodology to graduate and undergraduate students.
Mariane Frenay. Department of Psychology, université Catholique de Louvain, 10 Place Cardinal Mercier,
? B-1348 Louvain-La-Neuve, Belgium. Phone:, Web site : http://www.
Mariane Frenay is a professor at the Université catholique de Louvain, Belgium
(Dean of the faculty of Psychology and Education) and received her PhD in Instructional Psychology in 1994.
She is an active researcher in the field of higher education teaching and learning and faculty development. She
has been a member of the UNESCO Chair of university teaching and learning at her university since 2001 and
has been teaching for several years in Ma sters and Doctoral programs in Psychology and Education.
A person-centered approach to academic achievement 59
... Relevant profiles were identified, highlighting that the student body cannot be considered a consistent whole and that the particularities of these students do impact their adjustment to university. Among these studies, recent work consistently highlighted six entrance students' profiles based on combinations of social, academic, motivational, and study choice background indicators (De Clercq, Galand, and Frenay 2017;De Clercq and Perret 2020): Disadvantaged, Underprivileged, Apprehensive, Poor Performer, Thoughtless, and Advantaged profiles. These profiles provided interesting insights to soften students' transition but necessitate further replications. ...
... Several fall into the pitfalls of unreadiness, reluctance, and fearfulness identified by Nicholson (1990). Previous research addressed this heterogeneity in the preparation by combining the factors mentioned above (De Clercq, Galand, and Frenay 2017;De Clercq, Van Meenen, and Frenay 2020). These studies identified several patterns of students with specific weaknesses. ...
... These studies identified several patterns of students with specific weaknesses. More precisely, six profiles were depicted: Disadvantaged, Poor performer, Thoughtless, Underprivileged, Apprehensive, and Advantaged (De Clercq, Galand, and Frenay 2017). Disadvantaged students combined weaknesses on past performance, SES, informed choice and self-efficacy beliefs, a combination of factors that increased their chance to experience difficulties during the academic year, dropout, and failing the year. ...
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The transition into higher education (HE) is a particularly challenging process for students due to a large variety of difficulties and requirements. Moreover, increasing student numbers and diversity in European HE have complexified the issue of the successful transition to university. Consequently, it is important to further develop our understanding of the heterogeneity of students and the specific challenges that impact their successful and less stressful transitions into higher education. This paper contributes to this scientific endeavour. More precisely, a study was carried out among 1,048 first-year students from a French-speaking Belgian university. Using latent profile analysis, our results yielded five profiles representing different combinations of achievement pre-dictors (high school grade, socioeconomic status, informed-choice, and self-efficacy beliefs). When comparing the profiles, our results further highlighted key differences in the way students experienced the specific challenges associated with the transition and succeeded at the end of the first year. The discussion of the results allowed us to provide practical implications and future perspectives on the thorny issue of diversity into the transition to HE.
... Whereas Flemish secondary education is highly segregated, and rigidly tracked, Flemish higher education is open access, only requiring a secondary education degree, from any track, upon enrollment in any program or institution. This situation makes social inequalities in earlier stages of the educational career determining for higher education enrollment (De Clercq, Galand, and Frenay 2017). Given that socioeconomic composition effects are stronger in separation models (Dupriez et al. 2012), we expect that the socioeconomic and migrant school composition, at least, partly predicts higher education enrollment and program choice in Flanders. ...
... Interaction terms between SEC and school-wide social capital (Model 3A), and between MC and school-wide social capital (Model 3B) were added in separate models first, and simultaneously in model 3C, which allowed us to investigate whether school-wide social capital increased or offset disadvantages of attending low SEC or high MC schools (H4a, H4b). Lastly, features of separation models (i.e., tracking and grade retention) were controlled for in model 4. In rigidly tracked systems, track placement is expected to be associated with higher education enrollment (De Clercq et al. 2017). Moreover, research showed that social inequality in the Flemish educational system is structured along track lines, meaning that schools offering the same tracks are also characterized by a similar school composition, and similar resources. ...
... To quantify and understand the socioeconomic conditions of student households, literature has considered home ownership, loans to acquire, materials, extension, structure, access to public services, number of inhabitants, furniture, electrical appliances, vehicles and materials for school use (Cowan et al., 2012;De Clercq et al., 2017;Charles et al., 2018;Johnson, 2020). Having these possessions at home provides valuable information about family income over short and long periods. ...
... In line with Montes and Lerner (2012) and Karagiannaki (2017), a household's physical and material conditions also make a difference in students' academic performance. The need for materials such as a computer or internet access is part of the wealth mentioned (Sirin, 2005;Lovenheim, 2011;Cowan et al., 2012;De Clercq et al., 2017). Not having these materials makes it more difficult to achieve academic success and puts students who come from poor families at a great disadvantage. ...
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This article studies the relationship between the socioeconomic conditions of higher education students in Colombia and their academic performance during the pandemic. The household’s socioeconomic conditions are approximated by the education level of the parents, their occupation and the possession of assets. A multiple regression model tests the effect of these variables on academic performance before and during the pandemic. Results suggest that before the pandemic, the mother’s graduate education and household technology assets showed a positive impact on test score. Mixed effects of parents’ occupations by gender were also found. During the pandemic, the effect of the mother’s education remained the same, and the effect of technological assets, in-person education and high-quality accredited establishment increased.
... In other words, staff at university level needs to be aware of the students' needs, design curricula accordingly, and eventually, based on these curricula, practice what they preach (Dozier et al., 2006). Students, in turn, whose needs are disregarded and whose potentials are neglected, are prone to failing courses and modules, and might drop out of the study programme (Bäulke et al., 2018;Burns et al., 2019;De Clercq et al., 2017;Girelli et al., 2018;Rump et al., 2017;Trautwein & Bosse, 2017). ...
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This paper reports results from a quantitative curriculum study on literature modules in Austrian undergraduate teacher education programmes. In order to optimise course delivery in literature classes at the University College of Education Upper Austria (PHOÖ), reading habits and attitudes of 153 first-year EFL students for secondary school education were assessed in an online-questionnaire. The questionnaire examined students’ exposure to literary texts, their self-assessment as avid readers, their performative literacy, and their preferred reading stance. Results show rather limited avid reading, a self-centred performative literacy as well as a profoundly pragmatic reading stance. Such habits and attitudes could not only jeopardise success within the current teacher education study programmes but also aggravate the well-known Peter Effect, rendering prospective EFL teachers incapable of inspiring enthusiasm for literary reading in their future students. After discussing these results, the paper concludes with potential ramifications for curricular revisions as well as avenues for further research.
... Ahora bien, aun cuando estos siete desafíos sean comunes a cada nueva generación de estudiantes, es relevante sostener que entre los estudiantes existirían condiciones desiguales para afrontarlos. Trayectorias educativas y vitales distintas y en ocasiones muy distantes, promueven disposiciones y recursos disímiles al momento de intentar participar y aprender en la educación supe-rior (De Clercq et al., 2016). Además, las prácticas educativas en este nivel no siempre resultan inclusivas ni sensibles a perfiles juveniles heterogéneos, aumentando el riesgo de incomodidad, malestar, desaliento e incluso salida temprana desde una carrera. ...
... En Belgique francophone, le contexte de cette étude, l'accompagnement du choix professionnel se pose tout particulièrement. En effet, l'entrée à l'université n'est pas conditionnée par un processus de sélection fort, ce qui a pour conséquence que le processus de choix d'études, initié parfois tardivement, se pose souvent avec moins de réflexion, ce qui peut le rendre moins solide et affirmé par l'étudiant (Boudrenghien et al., 2011;De Clercq et al., 2017). C'est cette complexité du processus de choix d'études et professionnel que développent particulièrement Germeijs et ses collègues dans leur conception multidimensionnelle du processus du choix de carrière (Germeijs et al., 2012;Germeijs et Verschueren, 2006). ...
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Les processus de transition et d’adaptation au monde universitaire, ainsi que l’orientation vers le monde professionnel constituent des défis majeurs pour les étudiants universitaires. Face à cette problématique, de nombreux dispositifs de soutien ont été proposés par les universités. Cependant, leur impact n’a pas souvent été évalué. L’objet de cette recherche vise précisément à présenter les résultats de l’évaluation d’un dispositif de soutien au projet d’études et au projet d’insertion socioprofessionnelle : le dispositif « Projet de formation ». Cette évaluation a été réalisée auprès de deux cohortes d’étudiants, à trois ans d’intervalle, selon trois niveaux d’analyses, inspirés du modèle d’évaluation de Kirkpatrick et Kirkpatrick. Les résultats de cette étude montrent une amélioration significative des bénéfices perçus du dispositif ainsi qu’une augmentation rapportée du transfert des compétences acquises. Après avoir situé les limites de notre étude, nous évoquons quelques perspectives au regard de l’approche évaluative employée.
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The choice of a study program is based on complex individual decision-making processes. Thereby, economics is one of the most popular fields of study worldwide. Considering previous studies, the role of the teacher is often neglected. However, it can be assumed that teachers' professional knowledge plays a significant role in a student's choice of a study program. Thus, the present study investigated the influence of the professional knowledge that students perceive in their economics teacher on their aspirations and choice of an economics study program. The longitudinal data of 1387 Swiss high school students were analyzed. Economic competencies were measured multidimensionally and included knowledge, motivation, interest, value-oriented dispositions, and attitude. There were small to moderate correlations between the professional knowledge that students perceived in their economics teacher and their economic competencies. With regard to the intention and choice of economics, the results show small to moderate effects of the pedagogic content knowledge and the general pedagogic knowledge that students perceive in their teacher. These findings contribute to the discussion on the role of the economics teacher. It is therefore recommended that the teaching professionalism of economics teachers, which has been criticized in different countries, be promoted more strongly and more systematically.
Dropout in higher education has been a widely studied phenomenon, with a consensus on its potential to prevent the transfer of the personal and social benefits of higher education. In this context, it is recognised that rural student dropout in higher education has not been widely analysed, neither by states nor by the academic community. Hence, the Colombian case is not an exception to this reality, where public policies have made efforts to prevent and mitigate it by seeking to facilitate access to education through funding, as well as to strengthen and develop the skills of rural students that should have been acquired at previous academic levels. Despite this, dropout levels remain high, and with the effects of COVID-19 are expected to increase. Furthermore, this generates indications that there are explanatory variables and causes that have not been addressed by the State and Higher Education Institutions (HEIs) for the correct treatment of this educational phenomenon. Thus, the objective of this thesis was to establish which strategies in the framework of Colombian public policies should be implemented by the State and HEIs for the analysis, diagnosis, prevention, and mitigation of dropout in students located in or coming from rural areas enrolled in undergraduate programmes, through the development of models. A mixed methodology, incorporating both qualitative and quantitative methods, was proposed for the fulfilment of this objective. The study began with a documentary review to contextualise public policies on access to higher education, as well as those on retention and timely graduation. Subsequently, a model based on system dynamics was developed with the aim of understanding the economic effects of dropout on the actors at the educational level. With this framework, we proceeded to identify other explanatory variables influencing dropout in the rural student population through a systematic review of the literature and cluster modelling. Finally, a systems thinking model was developed based on the narratives of rural students who intended to drop out or had dropped out. The thesis presented here developed a first comprehensive analysis of dropout in rural higher education in Colombia, framing new perspectives for the development and complementation of existing public policies, based on the identification of the explanatory variables and causes that lead students to end their training process early. In general terms, advances were made in the field of knowledge regarding the study of public policies, simulation modelling applied to the field of education, the characterisation of students who drop out and those who intend to drop out, as well as those who intend to remain, and, finally, the establishment of the causes of dropout in the student population under study.
This study aims to better understand differences in the decision-making process behind study choices for higher education by investigating the presence of exploration profiles and then explore the explanatory base. To achieve this, we first identified different exploration profiles of students transitioning to higher education (n = 5660), and then investigated whether they were predicted by different student variables (i.e., learning strategies, gender, and educational track) and linked with different outcomes of the decision-making process (i.e., the amount of information acquired regarding higher education, decisional status, and commitment). A latent profile analysis identified three exploration profiles based on the decisional tasks of orientation, self-, broad, and in-depth exploration: passive (35%), moderately active (52%), and highly active explorers (13%). Students’ learning strategies (regulation and processing strategies) were associated with these profiles. Students with more effective regulation and processing strategies were more likely to be highly active than passive or moderately active explorers. Female students and students from the technical track were more likely to be found in the highly active profile compared to the moderately active and the passive or moderately active profile, respectively. Finally, highly active explorers had the most favorable outcomes, measured by decisional status, commitment, and amount of information. Based on a substantial dataset, our findings contribute to a more comprehensive understanding of the explanatory base of important differences in the study choice making process of students opting for higher education. This may ultimately lead to more fitting support for students in less beneficial profiles.
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The peer-review system, commonly considered critical for research integrity and rigour, has been criticised for being slow, exclusionary and exploitive. Concerns include the high profits of academic publishers as well as the growing number of insecurely employed academic staff who report high levels of stress and burnout. The consequence has been a decline in willing reviewers, publication delays, and potential damage to the career trajectories of early career researchers and PhD candidates at institutions that rely on metrics of academic impact as measures of academic performance. Rather than overhaul the system and undermine current benefits, this critical review adopts an ecological lens to posit an approach that is humanistic, transparent, and above all things, kind. This approach frames an applied perspective on how to improve peer-review moving forward.
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This chapter reviews the recent research on motivation, beliefs, values, and goals, focusing on developmental and educational psychology. The authors divide the chapter into four major sections: theories focused on expectancies for success (self-efficacy theory and control theory), theories focused on task value (theories focused on intrinsic motivation, self-determination, flow, interest, and goals), theories that integrate expectancies and values (attribution theory, the expectancy-value models of Eccles et al., Feather, and Heckhausen, and self-worth theory), and theories integrating motivation and cognition (social cognitive theories of self-regulation and motivation, the work by Winne & Marx, Borkowski et al., Pintrich et al., and theories of motivation and volition). The authors end the chapter with a discussion of how to integrate theories of self-regulation and expectancy-value models of motivation and suggest new directions for future research.
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Résumé Cette étude porte sur les caractéristiques sociodémographiques et le vécu scolaire des élèves relatives à l’absentéisme. Il s’agit aussi de comparer l’adéquation de trois modèles théoriques de l’absentéisme. Deux cent douze élèves du secondaire ont participé à une étude visant à examiner leurs perceptions du contexte scolaire, leurs orientations motivationnelles, leurs compétences perçues, leur sentiment d’aliénation vis-à-vis de l’école et leur absentéisme. Au-delà des différences entre filles et garçons, les caractéristiques sociodémographiques des élèves ne permettent pas suffisamment d’expliquer l’absentéisme. Elles font plutôt ressortir le rôle du sentiment d’aliénation, de même que l’effet du contexte scolaire sur la motivation.
Education at a Glance 2013: Highlights summarises the OECD’s flagship compendium of education statistics, Education at a Glance. It provides easily accessible data on key topics in education today, including: • Education levels and student numbers: How far have adults studied, and how does early childhood education affect student performance later on? • Higher education and work: How many young people graduate from tertiary education, and how easily do they enter the world of work? • Economic and social benefits of education: How does education affect people’s job prospects, and what is its impact on incomes? • Paying for education: What share of public spending goes on education, and what is the role of private spending? • The school environment: How many hours do teachers work, and how does class size vary? Each indicator is presented on a two-page spread. The left-hand page explains the significance of the indicator, discusses the main findings, examines key trends and provides readers with a roadmap for finding out more in the OECD education databases and in other OECD education publications. The right-hand page contains clearly presented charts and tables, accompanied by dynamic hyperlinks (StatLinks) that direct readers to the corresponding data in Excel™ format.
A correlational study examined relationships between motivational orientation, self-regulated learning, and classroom academic performance for 173 seventh graders from eight science and seven English classes. A self-report measure of student self-efficacy, intrinsic value, test anxiety, self-regulation, and use of learning strategies was administered, and performance data were obtained from work on classroom assignments. Self-efficacy and intrinsic value were positively related to cognitive engagement and performance. Regression analyses revealed that, depending on the outcome measure, self-regulation, self-efficacy, and test anxiety emerged as the best predictors of performance. Intrinsic value did not have a direct influence on performance but was strongly related to self-regulation and cognitive strategy use, regardless of prior achievement level. The implications of individual differences in motivational orientation for cognitive engagement and self-regulation in the classroom are discussed.