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Journal of Student Affairs Research and Practice
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/uarp20
The Role of Teachable Ownership of Learning
Components in College Adjustment
Amanda S. Case
To cite this article: Amanda S. Case (2020): The Role of Teachable Ownership of Learning
Components in College Adjustment, Journal of Student Affairs Research and Practice, DOI:
10.1080/19496591.2020.1825459
To link to this article: https://doi.org/10.1080/19496591.2020.1825459
Published online: 03 Nov 2020.
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Innovation in Research and Scholarship Feature
The Role of Teachable Ownership of
Learning Components in College Adjustment
Amanda S. Case , Purdue University
Using structural equation modeling, the current study analyzed data from
249 college students to examine how four aspects of ownership of
learning (engagement in learning, self-direction, self-efficacy, and self-
monitoring) predicted academic, social, and institutional adjustment to
college. Results indicated the model was a good fit to the data overall,
but that the three types of adjustment were predicted by different com-
ponents. Implications of the findings for student affairs personnel are
discussed.
Educational policy and practice position academic preparation as the keystone of college
readiness. However, students rarely drop out of college for academic reasons alone. For example,
Johnson (2012) found at least 40% of students who dropped out were academically successful during
their baccalaureate studies. Based on data such as these, numerous researchers have concluded that
nonacademic factors play a crucial role in college attrition (e.g., Strahan & Credé, 2015).
One possible nonacademic contributor to college attrition is difficulties in the transition to
college. Besides having to create new study habits, students must also form new relationships,
rework existing relationships, and take greater responsibility for their personal well-being (e.g.,
Feldt et al., 2011; Katz & Somers, 2017). These changes in students’ academic, social, and
personal responsibilities can create innumerable stumbling points. However, persistence to
degree requires students effectively adjust to college.
Although researchers extensively study student adjustment to college, findings remain
inconclusive (e.g., Credé & Niehorster, 2012). As a result, educational personnel have not
been provided with clear, empirically supported recommendations for supporting student adjust-
ment. The current study aims to both contribute to existing literature on student adjustment to
college and provide student affairs personnel with specific recommendations by focusing on the
role that teachable competencies play in adjustment. To do so, the study draws on Conley’s (2014)
model of college and career readiness and examines how his teachable components of student
ownership of learning relate to academic, social, and institutional adjustment. To date no such
examination has been conducted, so the utility of these teachable competencies in easing the
transition to college has not been explored.
Correspondence concerning this article should be addressed to Amanda S. Case, Educational Studies,
Purdue University, 100 N. University St., West Lafayette, IN 47907-2050. E-mail: amandacase@purdue.edu
Case, A. S. (2020).
The Role of Teachable Ownership of Learning Components in College Adjustment.
Journal of Student Affairs Research and Practice
ISSN: 1949-6591 (print)/1949-6605 (online)
JSARP © NASPA 2020 http://journals.naspa.org/jsarp doi:https://doi.org/10.1080/19496591.2020.1825459 1
College Adjustment
In one of the most predominant theories of student adjustment to college, Baker and Siryk
(1984) purported college adjustment encompasses four domains: academic, social, personal-
emotional, and institutional. Academic adjustment captures whether students have successfully
adapted to the academic demands of college as demonstrated by their attitudes toward academic
work and the effectiveness of their academic behaviors. Social adjustment reflects how students
are transitioning to the interpersonal aspects of college, including whether they are connecting
with people and involving themselves in college activities. Personal-emotional adjustment cap-
tures students’ physical and psychological feelings in response to their transition to college.
Finally, institutional adjustment encapsulates if students feel a sense of belonging at their
institutions.
In addition to their theory, Baker and Siryk (1989) also published the most widely used
measure of student adjustment to college: The Student Adaptation to College Questionnaire
(SACQ). Across a multitude of studies, researchers have shown adjustment as measured by the
SACQ to play a significant role in both college grades and retention (e.g., Bailey & Phillips,
2016; Seidman, 2007). For example, a meta-analysis of studies employing the SACQ found
adding the four domains of adjustment to a regression model of student grades increased the
predictive validity of the model from 18.6% to 29.1% (Credé & Niehorster, 2012).
In addition to predicting college outcomes, many scholars and practitioners argued adjust-
ment to college is meaningful in and of itself (e.g., Katz & Somers, 2017). For this reason,
researchers have begun unpacking the factors that predict adjustment with the intention of
informing policy and programming. However, findings across studies vary, making the applica-
tion of those results problematic. In their meta-analysis of 237 studies, Credé and Niehorster
(2012) found college adjustment has highly inconsistent relationships with experiences at college
and social support and only weak relationships with demographic information and prior achieve-
ment. Given this, it is not surprising that policies and interventions aimed at developing such
factors to help students adjust to college have received inconsistent support. For example, while
some research supported the effectiveness of interventions such as summer bridge programs (e.g.,
Strayhorn, 2011), first-year seminars (e.g., Keup & Barefoot, 2005), learning communities (e.g.,
Laufgraben & Shapiro, 2004), and first-year orientations (e.g., Mayhew et al., 2011) in easing
the transition to college, other research found these same interventions have limited or no impact
on adjustment (e.g., Barnett et al., 2012). On the other hand, Credé and Niehorster (2012)
found strong relationships between college adjustment and certain personality (e.g., conscien-
tiousness), and self-evaluation (e.g., self-esteem) factors. Because of the nature of these latter
factors, which are difficult to address through interventions, even the known strong relationships
have provided little guidance for policy and programming. It is therefore worthwhile to examine
whether factors that can be targeted through programming play a meaningful role in college
adjustment.
Conley’s Model of College Readiness
Factors that are amenable to intervention form the basis of Conley’s (2014) model of college
readiness, which has been employed in a range of research and policy about college readiness,
especially for students of Color and students from economically marginalized backgrounds (e.g.,
DeAngelo & Franke, 2016; Granger & Noguera, 2015). According to Conley, college readiness
requires not only academic preparation but also the ability to navigate the culture of higher education
institutions both within and outside of the classroom (i.e., academic, social, and institutional
Teachable Components in College Adjustment
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adjustment). Conley’s model includes both academic and nonacademic aspects of college readiness,
including cognitive strategies (e.g., conducting research, interpreting data), content knowledge (e.g.,
being conversant in core subject areas, terminology, and facts), transition knowledge and skills (e.g.,
understanding unstated codes of conduct that enable students to successfully navigate postsecondary
contexts; a.k.a., “college knowledge”), and learning techniques and skills (e.g., ownership of learning,
learning behaviors). This final aspect of Conley’s model is most central to this article.
According to Conley, learning techniques and skills include two sub-elements: (a) learning
techniques, such as time management, test taking skills, and strategic reading skills; and (b) owner-
ship of learning (OoL), which refers to students’ attitudes about themselves as learners and toward
learning overall. Previous research supported the importance of learning techniques in college
adjustment (e.g., Van Rooij et al., 2017). However, researchers have yet to explore the relationship
between adjustment to college and the five components that comprise Conley’s conceptualization of
OoL: engagement in learning, self-direction, self-efficacy, self-monitoring, and persistence.
Engagement in Learning
Student engagement definitions fall into four categories: behavioral, psychological, socio-
cultural, and holistic (Kahn, 2013). In their OoL model, Conley and French (2014) focused on
psychological engagement, including student “investment in learning” and “challenge seeking” (p.
1022). Like all of the OoL variables, student engagement is amenable to intervention, making it
a favorite variable for educational researchers concerned with student success, and for good
reason. Researchers found significant positive relationships between student engagement and
college outcome variables such as grades and persistence from first-year to sophomore year (e.g.,
Richardson et al., 2012).
Self-Direction
Conley and French (2014) defined self-direction as a student’s belief that she can flexibly
direct and adjust her strategies to meet goals even in uncertain circumstances. Conley and
French’s definition of self-direction overlaps with the construct of cognitive flexibility, which is
a teachable component of executive functioning that allows people to adjust their thoughts or
behaviors in response to situational demands. Cognitive flexibility has been associated with a host
of academic skills such as problem solving, language development, and math (Kercood et al.,
2017).
Self-Efficacy
Self-efficacy refers to a person’s belief that he can successfully execute the behaviors necessary to
complete a task or goal (Bandura, 1982). Hundreds of studies have supported the relationship
between self-efficacy and academic outcomes, including overall student adjustment to college (e.g.,
Brady-Amoon & Fuertes, 2011). However, few studies examined how self-efficacy related to the
subtypes of adjustment. Like other OoL components, self-efficacy can be learned. Bandura (1982)
theorized four sources of self-efficacy including verbal persuasion, vicarious experience, performance
accomplishments, and physiological and affective states, all of which have been shown to increase
self-efficacy (e.g., Phan & Ngu, 2016).
Self-Monitoring
Conley and French (2014) theorized OoL also requires students to be able to self-monitor,
which they defined as being able to reflect on and adjust learning behaviors. Researchers
Teachable Components in College Adjustment
JSARP © NASPA 2020 http://journals.naspa.org/jsarp doi:https://doi.org/10.1080/19496591.2020.1825459 3
documented the positive relationships between self-monitoring and multiple college outcomes,
including GPA and retention (e.g., Galla et al., 2019); research also supported the effectiveness
of interventions for increasing self-monitoring (e.g., Zimmerman, 2002).
Persistence
In education research, the term persistence describes whether or not students have completed
a program of study (e.g., “persistence to degree”). Conley (2014) used the term differently, defining
persistence as an internal characteristic of students who continue regardless of challenges, which
overlaps with Conley’s definitions of self-direction and self-monitoring. Due to Conley’s nontradi-
tional definition, scholarship on the relationship between persistence as Conley intended it and
college outcomes is difficult to find. Further, no existing psychometrically sound measures capture
Conley’s definition. Given these limitations, persistence is not included in the current study.
Current Study
Researchers found meaningful relationships between many of the OoL components and
several college outcome variables. However, the relationships between these components—as
defined by Conley—and the various dimensions of college adjustment have not been examined.
Given that higher education has failed to support degree attainment in all students, higher
education personnel must be informed of all possible points of intervention to support degree
attainment. The current study aims to contribute by exploring whether the components in
Conley’s conceptualization of OoL, which have been shown to be amenable to intervention,
predict academic, social, and institutional adjustment to college. Based on previous research, it
was hypothesized that the OoL components would significantly predict all three types of
adjustment, but that self-direction and self-monitoring would play a more important role in
academic adjustment than in social or institutional adjustment.
Methods
This project was informed by my identity as a counseling psychologist and my work as
a faculty member. As a counseling psychologist, my worldview is shaped by several values.
I am committed to seeing the strengths in individuals and how those strengths contribute to
their development across personal and professional endeavors. In addition, I view individuals
as inextricably linked to their contexts; contexts that, for some and at times, can be unwel-
coming and toxic, inhibiting people’s abilities to draw on their strengths. As a result of my
multiple privileged identities as a white, cis-gender, heterosexual, economically-stable woman,
most contexts—particularly academic contexts—are not toxic to me. However, academia has
been and remains toxic to many of my students and mentees, and they have articulated
consistently and painfully how their academic struggles are rarely about academics. This
work is, therefore, an extension of my values and my previous work around nonacademic
factors in educational success. It is also done on behalf of my students, holding higher
education systems accountable for welcoming and serving them more effectively.
Participant Recruitment and Selection
Participants were recruited by the International Baccalaureate Research Office (IBRO),
which is responsible for commissioning and conducting research on international baccalaureate
(IB) programs. IB offers rigorous curriculum at over 5,000 approved primary, middle, and
secondary schools in 157 countries, including more than 2,000 schools in the United States.
Teachable Components in College Adjustment
4 doi:https://doi.org/10.1080/19496591.2020.1825459 http://journals.naspa.org/jsarp © NASPA 2020 JSARP
U.S. schools offering IBs are diverse, including 36% private schools and 64% public schools,
more than 30% of which are Title I eligible; more than 40% of students enrolled in IBDPs are
students of Color and 16% are eligible for free or reduced meals (Gordon et al., 2015). At the
secondary school level, referred to as the International Baccalaureate Diploma Program (IBDP),
all students participate in a curriculum that emphasizes academic rigor to prepare students as
lifelong learners who are inquisitive, knowledgeable, communicative, principled, caring,
balanced, and reflective (IBO, 2019). Because the IBDP curriculum is standardized across all
IB high schools worldwide, sampling participants from IBDP students reduces some of the
variability in high school preparation that often muddies research on college adjustment.
However, the IBDP curriculum is often considered more rigorous than standard (i.e., non-
honors, non-AP) curriculum (IBO, 2019).
To recruit participants, the IBRO sent out a solicitation e-mail to a randomly selected
group of 6,000 IBDP students who graduated in the last six years. The e-mail included a link
to the online survey. In exchange for participation, respondents could enter a drawing to win
one of ten 50 USD gift cards. The informed consent form was on the first page of the
survey, detailing the purpose, procedures, potential risks and benefits, confidentiality, and
voluntary nature of the study. Respondents who agreed to participate were then directed to
the survey questions, which took approximately 30–45 minutes to complete. At the end of
the survey participants received contact information for the author if they had follow-up
questions.
Of the 566 individuals who completed the survey, 227 were not currently enrolled in an
undergraduate institution. Preliminary analyses revealed 98% of them had already graduated
from college and that they scored significantly higher scores on all study constructs than their
currently enrolled peers. Based on these analyses, they were eliminated from the data set so as not
to overestimate study findings based on rosy retrospection, which is the tendency to perceive
events more favorably in retrospect than when they were occurring (Norman, 2009). The
responses of an additional 90 individuals were excluded either because they failed to complete
more than half of the survey measures or because they were attending an institution outside of
the United States. Students attending non-US institutions were eliminated due to known
differences in the structures of U.S. versus non-U.S. universities that could affect the adjustment
process (Hughes, 2017).
Participants
249 individuals between the ages of 17 and 26 (�x = 20.7) participated in the study, 62.7% of
whom self-identified as women. Almost half of the participants (47.4%) indicated they were in
their senior year of college, with the remainder indicating they were in their junior (24.9%),
sophomore (17.3%), and first (6.0%) years. Slightly more than half of the sample self-identified
racially as White (55.4%), with an additional 23.3% identifying as Asian, 9.2% as Biracial or
Multiracial, 5.2% as Latinx, and 2.8% as Black. More than two-thirds of the participants (72.7%)
indicated at least one of their parents had earned a bachelor’s degree. A majority of participants
(78.0%) also rated their family as at least “moderately” financially stable. All of the participants
were enrolled in a U.S. college or university at the time of data collection. These institutions were
diverse, including more than 250 private and public colleges and universities across the United
States.
Teachable Components in College Adjustment
JSARP © NASPA 2020 http://journals.naspa.org/jsarp doi:https://doi.org/10.1080/19496591.2020.1825459 5
Measures
Demographics. Participants responded to questions regarding individual and family
demographics, including questions on age, gender identity, race, and parental education.
Participants were also asked to rate their family’s perceived financial stability on a 10-point
scale from 1 (Very unstable) to 10 (Very stable). Participant education was assessed through
questions about current enrollment and educational attainment.
College Adjustment. Participant adjustment to college was measured using the Student
Adaptation to College Questionnaire (SACQ; Baker & Siryk, 1989), which is currently the most
widely used measure of student adjustment to college (Credé & Niehorster, 2012). The SACQ
includes 67 items answered on a 9-point scale ranging from 1 (Doesn’t apply to me at all) to 9 (Applies
very closely to me). Previous research supported the reliability and validity of the SACQ, with
significant correlations being found in the appropriate direction with other markers of student
adjustment (Beyers & Goossens, 2002) and internal consistency estimates ranging from 0.82 to
0.90 across the four subscales (Mattanah, 2014). In the current study, items that comprise the
academic, social, and institutional adjustment subscales were analyzed. Internal consistency estimates
were strong (academic, α = 0.90; social, α = 0.87; institutional, α = 0.88).
Engagement in Learning. Engagement in learning was measured using six items from the
graduate qualities subscale of the Course Experience Questionnaire (CEQ) that “tap attitudes
and perspectives related to … lifelong learning” (Griffin et al., 2003, p. 263). Items are answered
on a five-point scale from 1 (Strongly disagree) to 5 (Strongly agree) and include statements such as
“My university stimulates my enthusiasm for further learning.” Internal consistency analyses for
these items demonstrated strong reliability (α = 0.83).
Self-Direction. Conley and French’s (2014) definition of self-direction, was measured using
four items from the Cognitive Flexibility Scale (CFS; Martin & Rubin, 1995). The original
version of the CFS includes 12 items that are responded to on a scale from 1 (Strongly Disagree)
to 4 (Strongly Agree). Previous research supported the validity and reliability (α = 0.92) of the
scale (Bilgin, 2009). For the current study, four items related to self-direction were selected,
including “My behavior is the result of conscious decisions I make.” Estimates suggest the four
items have acceptable internal consistency (α = 0.68).
Self-Efficacy. Self-efficacy was measured using the 10-item General Self-Efficacy Scale
(GSE; Schwarzer & Jerusalem, 1995). Items (e.g., “I can solve most problems if I invest the
necessary effort”) are rated on a 4-point scale from 1 (Not at all true) to 4 (Exactly true). More
than 1,000 studies have employed the GSE, which has been translated into 33 languages.
Psychometric analyses suggest the scale has acceptable validity, as well as strong internal
consistency (α = 0.75 to 0.91; Scholz et al., 2002). Internal consistency estimates for the
current study fell within the same range (α = 0.89).
Self-Monitoring. Self-monitoring was measured using a modified version of the Adolescent
Self-Regulation Inventory (ASRI; Moilanen, 2007). The original version of the ASRI is
comprised of 36 items rated on a scale from 1 (Not at all true for me) to 5 (Really true for me).
Previous research supported the internal consistency and validity of the scale (Moilanen, 2007).
For the current study the author selected 17 items (e.g., “I change the way I do things when I see
a problem with how things are going”) from the original 36, eliminating conceptually redundant
items for the sake of managing survey length. Psychometric analyses of the selected items
indicated they have strong internal consistency (α = 0.84).
Teachable Components in College Adjustment
6 doi:https://doi.org/10.1080/19496591.2020.1825459 http://journals.naspa.org/jsarp © NASPA 2020 JSARP
Results
Model Testing
Models were evaluated using structural equation modeling (SEM) in AMOS 25 with
maximum likelihood estimation. Indices of fit were selected that would reduce the likelihood
of Type I and Type II error, including chi-square test (χ
2
), the root mean square error of
approximation (RMSEA), and the comparative fix index (CFI). Suggested cutoff criteria for
each of these indices vary, from more stringent (e.g., RMSEA ≤ 0.06, CFI ≥ 0.95) to more
lenient (e.g., RMSEA ≤ 0.08, CFI ≥ 0.90) (Hu & Bentler, 1999). Regardless of which criteria
are used, SEM experts caution against viewing criteria as definitive cutoffs, as sample size and
model complexity affect model fit (Weston & Gore, 2006).
Item Parceling
Item parcels were created for each of the OoL measures that had 10 or more items. Scholars
have documented the advantages of using item parcels in SEM in comparison to item-level data,
including increased reliability, fit, and precision of parameter estimates and decreased bias in
estimates (e.g., Little et al., 2002). In the current study two OoL variables (engagement, self-
direction) were measured using six or fewer items, so all of those items were used as indicators for
their corresponding latent variables. Three parcels were created for the remaining two OoL
variables (self-efficacy, self-monitoring) and the three outcome variables (academic, social,
institutional adjustment).
The current study used the factorial algorithm method (Matsunaga, 2008) to create item
parcels. In this method, exploratory factor analyses are conducted on each measure and then
items are purposively assigned to parcels in descending order based on the magnitude of factor
loadings. The internal consistencies for each of the created parcels were as follows: self-
efficacy (0.74, 0.70, 0.67); self-monitoring (0.60, 0.60, 0.74), academic adjustment (0.72,
0.80, 0.75); social adjustment (0.65, 0.73, 0.71); and institutional adjustment (0.68, 0.68,
0.72). Financial stability was included as a manifest variable because it was measured by
a single item.
Missing Data
Due to the length of the full survey from which data for the current study were drawn, 58%
of participants had missing data on the constructs of interest. Of those participants with missing
data, 32% had data missing on a single variable and 25% had data missing on two or more
variables. No trends were observed in missing data across variables. Full information maximum
likelihood (FIML), which has been found to be less biased and more efficient than other
approaches, was used to handle missing data (Enders & Bandalos, 2001).
Preliminary Analyses
Preliminary analyses were conducted to examine relationships between demographic, OoL,
and adjustment variables. MANOVA results revealed no significant differences in any OoL
components or the SACQ based on parent education level, participant sex, race, or year in
school, or any interactions between those variables. In contrast, correlation analyses did reveal
small but significant relationships between family financial stability and self-efficacy (r = 0.15, p =
0.016) and academic adjustment (r = 0.21, p = 0.002). Based on these results, family financial
stability was included in the measurement and structural models. Means, standard deviations,
and correlations for all study variables are presented in Table 1.
Teachable Components in College Adjustment
JSARP © NASPA 2020 http://journals.naspa.org/jsarp doi:https://doi.org/10.1080/19496591.2020.1825459 7
Table 1.
Descriptive Statistics And Factor Correlations Among Study Variables
Variables 1 2 3 4 5 6 7 8
1. Family financial stability –
2. Engagement in learning 0.04 –
3. Self-direction 0.10 0.33** –
4. Self-efficacy 0.15* 0.38** 0.54** –
5. Self-monitoring 0.04 0.34** 0.38** 0.56** –
6. Academic adjustment 0.21** 0.56** 0.36** 0.39** 0.53** –
7. Social adjustment 0.12 0.56** 0.36** 0.37** 0.25** 0.47** –
8. Institutional adjustment 0.08 0.63** 0.32** 0.31** 0.30** 0.64** 0.83** –
M6.10 24.64 12.67 31.88 69.58 159.23 111.67 100.79
SD 2.08 3.67 2.05 4.07 7.55 27.28 20.53 17.22
Range 1–9 15–30 4–16 18–40 47–87 96–211 41–147 45–126
*p < 0.05; **p < 0.01
Teachable Components in College Adjustment
8 doi:https://doi.org/10.1080/19496591.2020.1825459 http://journals.naspa.org/jsarp © NASPA 2020 JSARP
Measurement Model
A measurement model, in which all factors were allowed to covary, was tested to ensure the
latent constructs were adequately measured by the items and parcels. Results indicated the
measurement model had good fit to the data (χ
2
[272] = 645.40, p < 0.001; RMSEA = 0.07,
90% CI [0.06, 0.08]; CFI = 0.90), and all indicators had factor loadings above 0.40.
Structural Model
The structural model included direct relationships between family financial stability and aca-
demic, social, and institutional adjustment. Family financial stability was also modeled to predict all
three kinds of adjustment indirectly, through the four OoL components (see Figure 1). No relation-
ships between either the four OoL variables or the three adjustment variables were modeled. Using
guidelines for fit indices described earlier, results indicated the model had good fit to the data (χ
2
[272] = 645.40, p < 0.001; RMSEA = 0.07, 90% CI [0.06, 0.08]; CFI = 0.90). In the model, family
financial stability directly predicted academic adjustment (β = 0.21, p < 0.001), but no other variables.
Academic adjustment was significantly predicted by engagement with learning (β = 0.50, p < 0.001)
and self-monitoring (β = 0.53, p < 0.001) but not by self-direction (β = −0.28, n.s.) or self-efficacy
(β = 0.10, n.s.). Only engagement with learning significantly predicted social (β = 0.62, p < 0.001) and
institutional (β = 0.72, p < 0.001) adjustment. No other paths were significant.
Indirect Effects
Individual indirect effects were tested using RMediation statistical package, which uses the
distribution-of-the-product method to build confidence intervals for mediated effects (Tofighi &
Figure 1. Standardized parameter estimates for the structural model. *p < 0. 05, **p < 0. 01.
Financial
stability
Engagement
Self-
monitoring
Social
adjustment
Academic
adjustment
Self-
direction
Self-
efficacy
.03
.16
.21**
.06
.53**
.62**
-.28
.10
-.19
-.13
.10
.50**
.08
.08
Institutional
adjustment
.08
.72**
.07
.06
Teachable Components in College Adjustment
JSARP © NASPA 2020 http://journals.naspa.org/jsarp doi:https://doi.org/10.1080/19496591.2020.1825459 9
MacKinnon, 2011). According to this method, indirect effects are significant if their 95%
confidence interval does not include zero. None of the tested indirect effects were found to be
significant.
Alternative Model
The relationship between Conley’s OoL components and the various domains of student
adjustment college have never been tested. As such, the structural model in the current study
tested the predictive power of all of these components without suggesting any a priori relation-
ships between those components. However, Conley and French (2014) theorized OoL may
begin with engagement with learning, which then allows students to develop and enact self-
direction, self-efficacy, and self-monitoring skills. The alternative model tested these relation-
ships between variables. This model had poorer fit indices than the structural model (χ
2
[278] =
696.58, p < 0.001; RMSEA = 0.07, 90% CI [0.07, 0.08]; CFI = 0.89), which was a significant
difference (χ
2
[6] = 51.18, p < 0.001).
Discussion
To date, empirical studies have not tested Conley’s theory that students with more devel-
oped OoL competencies are better prepared to adjust to college. Therefore, the current study
examined the role of these teachable components in adjustment to offer suggestions for student
programming. Results indicated the model provided good fit to the data, with the OoL
components significantly predicting the adjustment of students in the study sample. However,
only some of the OoL components had a significant relationship with adjustment. Whereas
engagement in learning and self-monitoring contributed significantly to student academic
adjustment to college, only engagement in learning significantly predicted student social and
institutional adjustment. Therefore, although Conley’s overarching theory that OoL supports
college readiness was supported, the results challenged the importance of all four components
that Conley included in his theory of OoL.
The fact that not all four OoL components significantly predicted academic, social, and
institutional adjustment makes sense in some ways. Adjusting to the academic demands of
college has been shown to require different competencies social or institutional adjustment
(e.g., Strayhorn, 2018). Conley’s model did not differentiate between these domains of adjust-
ment, instead conceptualizing adjustment unidimensionally. Based on study results, Conley’s
OoL model could be strengthened by accounting for the multiple ways students need to adjust to
college to be successful, though more research would be necessary to confirm study findings.
Most noteworthy in the results is the importance of Conley’s conceptualization of student
engagement in learning to all three types of adjustment. Unlike much of the college literature
that defines engagement behaviorally (i.e., interacting with faculty, participating in collaborative
learning and extracurricular activities; Quaye et al., 2020), Conley and French (2014) operatio-
nalized engagement psychologically, focusing instead on students’ attitudes toward challenges
and lifelong learning. It was not until relatively recently that researchers started examining the
role of psychological engagement on college student outcomes. That research found psycholo-
gical engagement to be a strong predictor of academic success (e.g., Timms et al., 2018). Study
results extend those findings, suggesting psychological engagement may be an especially powerful
teachable predictor of academic, social, and institutional adjustment.
Also noteworthy was the relationships that were not found. No significant differences were
found on any of the OoL components or the three adjustment domains based on student sex,
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race, year in school, or parent education. Some of these findings are in opposition to existing
research. For example, researchers consistently found that students of Color and first-generation
college students experience additional challenges when adjusting to college due to campus culture
and climate (Strayhorn, 2018). Additional research is necessary to better understand the role that
sampling students from an academically rigorous high school program played in their nonaca-
demic competencies and adjustment experiences. In addition, the fact that self-efficacy did not
predict any aspect of adjustment contradicts existing research. It is possible these findings are due
to the way self-efficacy was measured. Like Bandura, Conley and French (2014) conceptualized
self-efficacy as a self-belief that applies across tasks and settings. As such, a general self-efficacy
measure was employed in the study. However, other researchers proposed self-efficacy is domain
specific (i.e., academic self-efficacy, social self-efficacy, etc.; Schunk & Pajares, 2002). The
general self-efficacy measure may not have been sensitive enough to capture the influence of self-
efficacy on the specific domains of adjustment.
Findings regarding the direct relationships between family financial stability and the three
types of adjustment both align with and contradict existing literature. Researchers consistently
found students without financial concerns are better able to focus on academics in college (Joo
et al., 2008). Study findings that perceived family financial stability significantly predicts
academic adjustment are in line with these results. However, the lack of demonstrated relation-
ships between financial stability and social and institutional adjustment contradict existing
literature. Researchers consistently documented the struggles first generation college students
have socially adjusting to college and feeling a sense of belonging (e.g., Strayhorn, 2018). It may
be that although generation status has become a proxy variable for social class in higher education
research, perceptions of financial stability measured in the current study are capturing something
different than generation status. That possibility seems likely given that no differences were
found on any OoL or adjustment variables between first generation students and their non-first-
generation peers.
Limitations
Methodological limits should be taken into consideration when interpreting and applying
study results. First, because Conley defined some of the OoL components in nontraditional
ways, and because of concerns regarding attrition due to survey length, several constructs were
measured using modified instruments or by selecting items from existing measures that captured
Conley’s definition. The measures used in the study were shown to have acceptable to strong
internal consistency, however assessing constructs in this manner introduces possible psycho-
metric issues, particularly in terms of validity.
Second, a significant portion of the study participants had missing data. Although the issue
of missing data is common to virtually all research, any missing data can introduce study
limitations, including reduced statistical power, biased estimates, and resulting invalid conclu-
sions (Kang, 2013). The author took steps to impute missing data using a less biased method
(i.e., FIML), however study conclusions should be interpreted in light of this limitation.
Finally, sampling participants via the International Baccalaureate Research Office necessarily
restricts the generalizability of study findings to college students as a whole. By virtue of being
enrolled in an IBDP, participants were exposed to a different and theoretically more rigorous
high school curriculum than peers not enrolled in an IBDP. Although IBDPs can be found in
racially and economically diverse schools, the participants in the current study did not fully align
with national demographics of IBDP students in particular or college students overall. Regarding
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race, the study sample underrepresented Black, Latinx, and White students and overrepresented
Asian and multiracial students. In addition, although the sample was relatively aligned with the
economic composition of IBDP students, the participants came from more privileged back-
grounds than college students overall. It is well known (e.g., Conefrey, 2018) that race and social
class are key predictors of adjustment to college, so the findings from the current study should be
interpreted in light of the composition of the study sample.
Implications
Despite the limitations described, study results hold important lessons about student adjust-
ment to college. Researchers have historically found the most consistent and strongest predictors
of adjustment to college to include personality traits and other internal and affective factors
(Credé & Niehorster, 2012). Although these previous findings have been useful in understanding
which students may effectively transition to college, they have been less instructive for program
or intervention design as characteristics such as these have proven difficult (albeit not impossible)
to change. For this reason, study results are especially exciting due to the fact that all of the OoL
components are teachable, pointing out a clear but as of yet unexplored avenue for interventions.
In particular, findings suggest that even for students from academically rigorous high schools
who are often thought to be protected from adjustment difficulties, engagement in learning and
self-monitoring play an important role the transition to college, which would make it worthwhile
for college programming and interventions to target these components.
Recommendations for College Interventions
Of the nearly 2 million students who start college each year, around 40% will never earn
a college degree (Kirp, 2019). The fault of this lies not in the students but rather in the
institutions (e.g., Lawrence, 2005). Part of the reason why postsecondary institutions are failing
to support students to degree may be because student support has often been siloed, with
administrators, student affairs personnel, and faculty rarely working in concert with one another
to support student adjustment to college (Savoca & Bishop, 2020). This cannot continue.
Instead, based on the results of the current study, all college personnel could support student
adjustment to college by encouraging the development of psychological engagement and self-
monitoring both before and after matriculation.
Most incoming students likely know that academics are important to college success; fewer
may understand the role that nonacademic factors, such as psychological engagement and self-
monitoring, play in their successful transition to and persistence through college. This knowledge
gap can be addressed even before students step foot on campus. Student affairs personnel could
consider introducing the concepts of psychological engagement and self-monitoring in their
introductory materials to students by defining the terms, describing why they are important for
college success, and emphasizing that such competencies can be learned and developed with
practice. In addition, administrators and student affairs personnel could consider books that
touch on these concepts for their common reading or in first-year seminars. For example,
Csikszentmihályi’s (1990) Flow: The Psychology of Optimal Experience or Dembo and Seli
(2016) Motivation and Learning Strategies for College Success could be assigned over the summer
and then discussed during summer bridge programs or first year orientations. Given the
increased use of remote learning, colleges could also consider having virtual book clubs over
the summer, where incoming students can discuss how psychological engagement and self-
monitoring may affect their college experience with peers and peer mentors.
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Once students arrive on campus, student affairs personnel and faculty can also work
collaboratively to help students develop and implement effective psychological engagement and
self-monitoring practices. Doing so will likely need to start with training for both student affairs
personnel and faculty, which can be led by university teaching/learning experts. For student
affairs personnel, the training could focus on what psychological engagement and self-
monitoring are, the role such skills play in college success, how to talk with students about
their self-perceived proficiency in these areas, and strategies students can use to develop their
psychological engagement and self-monitoring skills outside of classes. For faculty, the training
could emphasize the known relationships between student-teacher relationships and student
psychological engagement (Morris, 2019), as well as strategies they can implement in their
classes to help students develop self-monitoring skills. One model that can be used to teach
faculty about their role in fostering psychological engagement in students is Saucier’s (2019)
Trickle-Down Model of Self and Student Engagement, which suggests that student psychological
engagement begins with instructor engagement.
Faculty and student affairs personnel could also be trained on simple strategies they can
integrate into classes or workshops hosted during summer bridge programs, orientations, or
through ongoing support services, to help students develop their psychological engagement and
self-monitoring skills. Regarding self-monitoring, researchers have found new college students
struggle because they are uncertain what college-level work looks like. As a result, they are
unprepared to engage in self-monitoring behaviors to assess whether they are producing college
quality work. By asking students to assess and discuss assignment exemplars, researchers found
students can understand assignment expectations so they can better monitor their performance
(Hawe et al., 2017). Psychological engagement can also be targeted by providing students with
opportunities both within and outside the classroom that encourage reflection around their
motivation for learning. Within classes these reflective opportunities could focus on student
motivations for taking a particular class or pursuing a particular major. Outside of class, students
could be encouraged to reflect on what they are getting out of participating in a campus activity or
their rationale for attending a specific institution. Through these opportunities, students may build
up their identities as lifelong learners, which could assist with their adjustment (Kahn, 2013).
These suggestions are not intended to be exhaustive but rather to suggest simple steps
college personnel could take to assist students in their adjustment to college. In order for these
suggestions to be implemented, however, administrator buy-in is required. Therefore, upper
student affairs personnel should host trainings for or disseminate materials to administrators that
specifically describe the importance of nonacademic factors such as psychological engagement
and self-monitoring in student success. By providing such information, student affairs personnel
will also be better positioned to advocate for the allocation of funds for the programming
suggested above.
Conclusion
Student adjustment to college is undeniably complex, which has made it difficult for student
affairs personnel to identify the most effective mechanisms for supporting students as they
transition. The findings of the current study are especially exciting in that context, largely
because they provide clear points of intervention. Of course, it is not the case that implementing
the above suggestions will ensure all students effectively transition to college. However, by
prioritizing student ownership of learning and ensuring students have ample opportunities to
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become engaged in their educational contexts and learn how to self-monitor, we may be able to
smooth the path for some students as they enter and adjust to college.
ORCID
Amanda S. Case http://orcid.org/0000-0001-7027-7871
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