Engagement & Disengagement in University Students Dissertation

PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE
FACULTY OF EDUCATION
DOCTORATE IN EDUCATION
Measurement Quality and Factors Associated with Engagement and Disengagement in
University Students
DISSERTATION PROJECT FOR DOCTORAL CANDIDACY
THESIS DIRECTOR: Carlos González, PhD
COMISSION: Edgar Valencia, PhD
Sergio Celis, PhD
Chi Baik, PhD
STUDENT: Andrea Espejo
November 2024
SUMMARY
Higher education plays a fundamental role in personal and professional development, where first-generation
students (those whose parents have not completed university studies) face unique challenges, particularly
in Latin America. These students often exhibit a lower sense of institutional belonging, higher stress levels,
and less use of support services, although recent literature highlights their positive traits such as greater
academic motivation and resilience.
In the context of higher education, current research reveals significant gaps in understanding engagement
and disengagement, particularly concerning first-generation students. Both constructs are crucial for
understanding student success. Based on the Self-System Model of Motivational Development (SSMMD),
engagement represents active and constructive participation in academic activities, while disengagement
manifests through behavioral and emotional components such as passivity, anxiety, and frustration.
Measuring these constructs requires careful consideration of validity and fairness, especially when comparing
different student groups. Additionally, there is a need for a deeper understanding of how various factors in
students' university experiences influence their levels of engagement and disengagement. This project
contributes to improving the measurement quality of these constructs and analyzing their relationship with
personal and contextual factors in first-generation students.
The general objective is to explore engagement and disengagement in first-generation higher education
students in Chile, evaluating their measurement quality and explanatory factors. The specific objectives
include (1) analyzing how educational research defines validity and fairness when studying these constructs,
(2) examining to what extent the meaning of responses to the (dis)engagement scale varies in firstgeneration students, (3) examining associations between internal and contextual factors of students and
their levels of engagement and disengagement, and (4) exploring the existence of student profiles based on
patterns of their levels of engagement and disengagement.
The methodological design includes multiple approaches. For the first objective, a systematic literature review
will be conducted following Campbell Collaboration methodology. The second objective involves Multigroup
Confirmatory Factor Analysis to evaluate measurement invariance. The third objective employs Structural
Equation Modeling to examine relationships between variables, while the fourth objective includes Latent
Profile Analysis. Data for objectives 2, 3, and 4 will be collected through the VOCES survey, administered to
students from various higher education institutions in Chile.
This project contributes theoretically by deepening the understanding of validity and fairness in measuring
engagement and disengagement while considering students from different sociodemographic groups. Its
multidimensional approach allows for analyzing heterogeneous profiles of first-generation university students
based on their perceived levels of engagement and disengagement, offering a nuanced perspective on how
they face academic and social demands according to their institutional and social context. Finally, its practical
implications are relevant for educational institutions to promote student involvement through effective
support strategies tailored to their specific needs.
PROPOSAL FORMULATION
1. Social relevance and study context
Higher education is fundamental for both individual development and societal progress. At a personal level,
it offers significant opportunities, such as better job prospects, higher income, and active participation in
civic life (OECD, 2023). In Latin America, higher education also plays a key role in fostering youth
entrepreneurship and developing skills such as risk-taking ability and complex planning (Hémbuz et al.,
2020), which is essential for the region's economic and social growth. In this context, student adaptation
emerges as a crucial factor for student success. Those who manage to adapt to the academic and social
demands of university tend to show better academic performance and lower dropout rates (González & Báez,
2024). However, the transition to higher education can be challenging due to various factors, such as
adapting to a new environment and managing greater responsibilities (Haktanir et al., 2023), which
underscores the importance of supporting students in these processes.
In this sense, student engagement and disengagement are key to understanding students' adaptation to
higher education. Engagement predicts university adaptation (Cobo-Rendón et al., 2022) and is associated
with fewer absences and disciplinary problems (O'Donnell et al., 2023). In contrast, high levels of emotional
and behavioral disengagement are associated with poor academic performance, academic failure, and
dropout (Lombardero et al., 2023; Wilding, 2015). Pérez-Salas et al. (2021) emphasize that disengagement
involves maladaptive processes that go beyond the absence of engagement, highlighting the importance of
addressing both constructs to reduce student dropout. Although engagement has been widely studied,
disengagement has received less attention, especially in higher education (Chipchase et al., 2017; Darmody
& Thornton, 2015), making it difficult to develop preventive strategies. This is especially critical for students
from vulnerable socioeconomic contexts, who face additional barriers that affect their performance and
adaptation, demonstrating the need to investigate its impact on this group (Henry & Thorsen, 2020).
Therefore, studying engagement and disengagement processes is fundamental to understanding how
students involve themselves in their learning and how this affects their performance, adaptation, and
persistence in higher education. These processes are especially relevant in diverse contexts such as Latin
America, where socioeconomic factors and cultural capital play a crucial role in the educational experience
(Guerrero et al., 2021).
2. Conceptualization and measurement of engagement and disengagement in higher
education
Engagement as motivation in action and its antithesis: disengagement
Student engagement has been defined from various theoretical perspectives, with Self-Determination Theory
(SDT) being one of the main ones. Within this framework, engagement is conceived as the observable
component of intrinsic motivation, which is based on the satisfaction of basic needs for autonomy,
competence, and relatedness (Reve, 2012). This theory emphasizes that when these needs are satisfied,
students develop higher and sustained engagement. Other authors describe it as a multidimensional
construct, encompassing behavioral, emotional, and cognitive aspects (Taboada Barber et al., 2017; Zhoc et
al., 2019). In this line, Skinner et al. (2009) define engagement as a dynamic state resulting from student
interactions, which includes behaviors and emotions associated with the quality of their participation in
academic work. This definition has been adopted and developed by other researchers (Acosta-Gonzaga,
2023; González & Paoloni, 2014; Lombardero et al., 2023; Reeve, 2012; Taboada Barber et al., 2017),
emphasizing its multidimensional nature that encompasses behavioral, emotional, cognitive, and agentic
dimensions. Other authors propose dimensions such as social engagement (with peers and teachers) and
engagement in virtual environments (Laranjeira & Texeira, 2024; Zhoc et al., 2019), reflecting the complexity
of the construct in the current context of higher education.
In contrast, student disengagement lacks a unified definition in literature. Skinner et al. (2009), who use the
term disaffection, describe it as the absence of effort or persistence, manifested through passivity, lack of
initiative and dropout, accompanied by negative emotions such as boredom or anxiety. Chipchase et al.
(2017) conclude that disengagement is a complex and multifaceted construct that encompasses, analogously
to engagement, behavioral, emotional, and cognitive dimensions. Additionally, it is a fluid motivational state,
influenced by various personal and contextual factors. This conceptualization suggests that disengagement
is not merely the absence of engagement, but a state that can vary in intensity and manifest in different
ways across educational contexts (Hoi, 2024; Laranjeira & Teixeira, 2024; Lawson & Masyn, 2015).
For Skinner and Raine (2022), the lack of conceptual clarity regarding disengagement is evident in different
ways. On one hand, there are different terms that seek to refer to the same phenomenon. For example,
terms such as disaffection (González & Paoloni, 2014; Lombardero et al., 2023; Skinner et al., 2008; Taboada
Barber et al., 2017) or maladaptive engagement (Yin, 2018) are used. On the other hand, there is ambiguity
in distinguishing between indicators of the construct and indicators of associated factors. For example, when
discussing engagement and disengagement, Zhoc et al. (2019) state that the concept of student engagement
becomes ambiguous when it represents both the time and energy students invest in educationally purposeful
activities and also the effort institutions devote to using effective educational practices. This mixing of student
actions and institutional efforts muddies the waters, making it hard to separate what indicates engagement
from what causes it. Furthermore, the literature shows a disparity in the study of the construct's dimensions:
the cognitive and emotional dimensions are less studied than the behavioral dimension (González & Paoloni,
2014; Lombardero et al., 2023).
A review of the literature reveals a theoretical gap in the conceptualization of academic engagement and
disengagement in higher education. The lack of consensus in definitions (Chipchase et al., 2017), the variety
of proposed dimensions (O'Donnell et al., 2023; Pham et al., 2022), and the ambiguity in distinguishing
between indicators and facilitators underscore the need for greater conceptual clarity. This gap not only
affects the precise measurement of these constructs but also hinders the comparison and integration of
results across different studies (Chipchase et al., 2017; Wilding, 2015).
To facilitate the understanding of disengagement in higher education, this project adopts the definition based
on Ellen Skinner's work within the Self-System Model of Motivational Development (SSMMD) framework. For
Skinner and Raine, disengagement as a maladaptive motivational state emerges when basic psychological
needs are not satisfied (Skinner et al., 2009). This state manifests through behaviors such as passivity, lack
of initiative, and dropout, as well as negative emotions such as boredom, anxiety, or frustration. This
definition positions disengagement as an opposing phenomenon that, like engagement, is multidimensional
and influenced by personal and contextual factors (Skinner & Raine, 2022).
Validity of engagement and disengagement measurement
Validity refers to the degree to which a measurement instrument accurately measures the construct it intends
to measure, ensuring that the results obtained with said instrument are precise and meaningful (Messick,
1995). To strengthen construct validity evidence, the use of multiple methods is recommended (Laranjeira
& Texeira, 2023; Ritosa, 2022).
For measuring engagement and disengagement, confirmatory factor analysis is primarily used with the
objective of finding evidence to support models with a specific number of factors (Buric et al., 2024;
Laranjeira & Texeira, 2023; Martin, 2007; O'Donnell, 2023; Zhoc et al., 2019). Exploratory factor analysis is
also used to identify factors that could be part of engagement or disengagement (Laranjeira & Texeira,
2023), to test different solutions on how to organize items into factors (O'Donnell, 2023), or to explore
factors when confirmatory factor analysis fails to provide support (Ritosa, 2022). Additionally, methods have
been applied to measure concurrent validity through correlations between construct dimensions and
academic performance (Laranjeira & Texeira, 2023; Zhoc et al., 2019).
According to various authors, measurement invariance is a relevant method for validity evidence of
constructs such as engagement and disengagement (Maasen et al. 2023; Martin, 2007; Poort et al., 2020;
Yin, 2018). This is because it ensures that observed differences in the data are attributable to variations in
the studied variables and not to differences in how questionnaire items are interpreted or answered by
different cultural groups of students (Poort et al., 2020). Additionally, to ensure that the measurement of
these constructs is equivalent across different cultural or demographic groups (Yin, 2018). However, recent
research has shown that measurement invariance is frequently ignored or inadequately reported in
psychology studies, which compromises construct validity (Maasen et al., 2023).
Despite the importance of measurement invariance for comparing diverse student groups, research on
engagement and disengagement has predominantly focused on comparisons based on gender (Laranjeira &
Texeira, 2024; Martin, 2007; O'Donnell et al., 2023; Zhoc et al., 2019) or education levels (basic or higher)
(Martin, 2007; O'Donnell, 2023), as well as evaluation through multiple measurements in the same group
over time (Buric et al., 2024). This absence suggests a significant gap in existing literature, which limits the
understanding of how engagement and disengagement occur in specific student groups. Contributing to this
gap is important for the theoretical understanding of engagement and disengagement, and for the
applicability of results obtained in their measurement (Maassen et al., 2023).
3. Factors associated with engagement and disengagement in higher education
In the literature studying both engagement and disengagement, researchers have sought to identify their
relationship with other relevant factors of students' experience during their university education (Alharbi,
2019; Chipchase et al., 2017; Darmody & Thorton, 2015). This search translates into studies that have
related engagement and disengagement to other variables (variable-centered), but also into studies that
focus on identifying different student profiles (person-centered).
Within the Self-System Model of Motivational Development (SSMMD) framework, the relationships between
engagement, disengagement, and other variables are understood through the interaction between
contextual factors -for example, teaching styles in the student's classroom- and personal factors -for
example, self-efficacy- that manifest in behavioral and emotional forms of student engagement and
disengagement (Skinner et al., 2008). Li and Xue (2023) also define personal factors as individual
characteristics of students, while contextual factors belong to their educational and social environment.
Within the educational environment, contextual factors include, for example, expectations about higher
education. This is because the transition from school to university can be challenging for first-year students,
and their expectations about university studies can significantly impact their levels of engagement and
disengagement (Hoi, 2023). Another relevant contextual factor is perceived academic quality: teaching
styles, classroom environment, and the nature of learning tasks can significantly shape students' perceptions
of academic quality and their levels of engagement and disengagement (Hoi, 2023; Lombardero Posada et
al., 2023; Taboada Barber et al., 2017). Meanwhile, Pham et al. (2022) found that the perception of academic
quality, including teaching quality and provided academic resources, influences levels of engagement and
disengagement through student satisfaction.
Additionally, the perception of institutional support services influences student engagement and
disengagement. López et al. (2023) indicate that a welcoming and inclusive university climate that values
diversity and provides opportunities for student participation can improve students' sense of belonging and
their perception of support, thus fostering engagement. Furthermore, Kahu et al. (2019) found that
perceptions of social belonging and support networks within the institution can significantly affect student
engagement and disengagement. Finally, difficulties experienced during education can influence student
engagement and disengagement, such as lack of academic preparation or demanding workload (López et
al., 2023). These authors also point out that financial difficulties and personal or family responsibilities can
pose significant challenges for students, affecting their ability to fully participate in their studies.
As part of the social environment, a relationship was found between engagement and disengagement with
contextual factors such as family cultural and economic capital (Wang & Huang, 2021). The context is also
described in terms of sociodemographic factors and through studies that analyze the influence of these types
of variables on engagement and disengagement. For example, Tomaszeski et al. (2020) indicate that
socioeconomic status can influence student engagement. Specifically, it was found that students of lower
socioeconomic status showed lower levels of behavioral, emotional, and cognitive engagement. Additionally,
Yu et al. (2019) found that students from higher quality schools and with highly educated parents showed
higher levels of persistence, the latter being an indicator of engagement.
On the other hand, among the personal factors that have shown significant relationships as causes of
engagement and disengagement, various studies from Self-Determination Theory have pointed to intrinsic
motivation (Acosta-Gonzaga, 2023; Yin, 2018), extrinsic motivation (Acosta-Gonzaga, 2023; Ganotice et al.,
2022), as well as the satisfaction of basic needs for competence (González & Paoloni, 2014; KuchinskiDonnelly & Krause, 2020), autonomy (González & Paoloni, 2014; Taboada-Barber et al., 2017; Widing, 2015;
Yang et al., 2022), and relatedness (Mystkowska-Wiertelak, 2022; Wilding, 2015). Additionally, social and
emotional competencies are positively associated with engagement in its emotional, behavioral, and cognitive
dimensions (Santos et al., 2023). These authors include within these competencies skills such as selfawareness, self-management, social awareness, relationship skills, and responsible decision-making.
The study of contextual and personal factors related to engagement and disengagement considers the
influence of gender and disciplinary area as control variables. For example, research by Lingo and Chen
(2023), Martin (2007), and Virtanen et al. (2023) has found significant associations between gender and
membership in certain engagement and burnout profiles. The disciplinary area is also key due to its impact
on academic motivation and participation. Studies such as Jiang et al. (2024) evaluated measurement
invariance across disciplines, while Lingo and Chen (2023) found differences in extracurricular participation
patterns according to discipline. Both studies underscore the importance of these variables for ensuring valid
and precise analyses.
Person-centered literature regarding engagement and disengagement highlights, first, that there are
inconsistent patterns between dimension levels of both engagement and disengagement, leading to profiles
that reflect different combinations of these, being experienced simultaneously by the student (Jiang et al,
2024; Salmela-Aro et al., 2016; Schnitzel et al., 2020). Second, the importance of a holistic approach to
understanding engagement and disengagement that considers both individual and contextual factors to
predict student profile membership, such as their sociodemographic factors (Lingo & Chen, 2022; Virtnanen
et al., 2018) and classroom context (Vo Ngoc, 2023).
The relevance of studying these constructs from a person-centered approach is supported by the
heterogeneity present in the university student population according to their different contexts. Identifying
profiles facilitates the analysis of complex patterns in engagement and disengagement dimensions (Lawson
& Masyn, 2015; Lingo & Chen, 2023; Maloshonok, 2023; Virtanen et al., 2018; Yang et al., 2022), allowing
for the recognition and description of variability within specific groups (e.g., first-generation students).
Additionally, identifying student profiles with distinctive patterns of engagement and disengagement enables
institutions to design personalized interventions according to the specific needs of each profile (Lawson &
Masyn, 2015; Vo Ngoc, 2023). However, literature that seeks to identify student profiles based on their levels
of engagement and disengagement in higher education is still scarce, as most studies found focus on basic
education students.
The relationship between personal and contextual factors with engagement and disengagement remains an
insufficiently explored area. Wilding (2015) highlights the lack of understanding about how contextual factors
contribute to disengagement, while Kuchinski-Donnelly and Krause (2020) emphasize the lack of evidence
about predictors of emotional engagement. Pham et al. (2022) stress the need to investigate how the quality
of educational services impacts different dimensions of engagement and disengagement. Additionally, the
analysis of student profiles based on their levels of engagement and disengagement offers a comprehensive
perspective of the phenomenon and the characteristics of students more likely to experience them during
their higher education (Vo Ngoc, 2023).
4. First-generation students as a relevant group for the study of engagement and
disengagement
The population of first-generation students (those whose parents have not completed university studies) has
grown within the framework of massification, privatization, and diversification of university offerings in
numerous countries, including Chile (Jarpa-Arriagada & Cárcamo-Vásquez, 2023). Furthermore, it has
become a relevant group due to certain challenges they face, associated with their parents' educational level.
For example, one challenge identified in this group of students is that they tend to show a lower sense of
belonging to the educational institution, higher levels of depression and stress, and lower use of psychological
counseling services compared to their peers (Stebleton et al., 2014). Additionally, they face a higher risk of
dropout due to adverse environments that affect their self-efficacy and their limited familiarity with university
processes, which negatively impacts their self-confidence (Gillen-O'Neel & Fuligni, 2019; Rosales et al.,
2024). In Chile, first-generation students face a particular challenge related to the cultural discourses that
structure and influence their educational experience (Guerrero, Gallardo & Valenzuela, 2021).
In contrast, more recent literature regarding this group of students, specifically in the Chilean context,
challenges the deficit perspective of this group, emphasizing opportunities and positive aspects that are also
characteristic of this group and are relevant during their higher education. Specifically, the literature
mentions aspects such as higher academic motivation (Espinoza et al., 2024), resilience and determination
to be the first in their family with a university degree (Rosales et al., 2024). Additionally, because they seek
to repay their parents for their effort in their education (Soto Hernández, 2016). This motivation and
resilience, combined with their academic abilities, result in study strategies that show persistence and involve
overcoming fears regarding their capacity to remain in the university system (Espinoza et al., 2022; Webb,
2019).
Therefore, it is important to study how engagement and disengagement manifest in first-generation students,
as these challenges and opportunities can influence their academic participation (or levels of engagement
and disengagement) and, consequently, their academic performance (Rosales et al., 2024).
5. Importance, scientific contribution, and novelty of the project
The significance of this project centers on studying engagement and disengagement processes in firstgeneration university students in Chile. By investigating how these processes develop in a relevant social
group, the study contributes to understanding the dynamics involved in their adaptation to the university
context. Additionally, it analyzes how personal and contextual factors influence these processes, allowing
institutions to design effective support strategies.
Regarding its theoretical contribution, the study addresses a significant gap through detailed analysis of
engagement and disengagement constructs, deepening the measurement of both, with special emphasis on
disengagement, which has been less explored. Furthermore, by applying measurement invariance, the
project facilitates scale replicability, allowing more reliable comparisons between students from different
contexts. In terms of practical applicability, the study's findings will be useful for higher education institutions,
as they enable the identification of specific student needs and inform decision-making to improve their levels
of involvement in their education.
Finally, the novelty of this project lies in its multidimensional approach, as it not only examines how
engagement and disengagement constructs relate to other factors in higher education but also explores the
heterogeneity of these processes within the first-generation student group. Analyzing heterogeneous profiles
in their levels of these dimensions will allow a deeper and more nuanced understanding of how these students
face the academic and social demands of university. This perspective allows for unraveling the particularities
of their educational experience in relation to their socioeconomic and cultural context.
In this sense, the research question that will be answered in this project encompasses the multiple gaps
identified around engagement and disengagement in higher education: What is the measurement quality of
engagement and disengagement measures, and what personal and contextual factors explain them in firstgeneration higher education students?
HYPOTHESES AND OBJECTIVES
Hypotheses
Based on the reviewed theoretical-conceptual and empirical foundations, which highlight the relevance of
disengagement in higher education and its relationship with various personal and contextual factors affecting
students, especially first-generation students, this project proposes the following hypotheses:
1. Educational research on engagement and disengagement in higher education shows significant
variability in the definition and operationalization of the concept of validity and fairness.
2. The meaning of responses to the engagement and disengagement scale will be consistent with the
proposed theoretical model and will show good fit in the group of first-generation university students,
indicating the validity of the measurement of these constructs in this population.
3. Personal and contextual factors of first-generation students' university experience will be significantly
associated with their perceived level of engagement and disengagement.
4. There will be student profiles that share different patterns of engagement and disengagement.
Additionally, it is anticipated that personal and contextual variables of first-generation university
students will be significantly associated with their profile membership to the various identified profiles.
Objetives
General objective: Exploring engagement and disengagement in first-generation higher education students,
evaluating the measurement quality and personal and contextual factors that explain them.
Specific objectives:
1. Analyzing how educational research in higher education defines and operationalizes the concept of
validity and fairness when studying engagement and disengagement.
2. Examining to what extent the meaning of responses to the engagement and disengagement scale
differs in first-generation higher education students.
3. Examining the association between first-generation higher education students' perception of their
engagement and disengagement and personal and contextual factors of their university experience.
4. Exploring the existence of student profiles based on their perception of engagement and
disengagement and explore the relationship between profile membership with other contextual and
sociodemographic characteristics of first-generation university students.
METHODOLOGY
The methodological aspects of this project will be separately described for the first objective, and
subsequently for objectives 2, 3, and 4:
O1: Methodological aspects
a. Design and procedure
A systematic review of literature will be conducted, based on the Campbell Collaboration methodology
(Kugley et al., 2017). This review involves the collection and analysis of evidence to answer a research
question, through the extraction and analysis of relevant studies, using systematic and explicit methods
(Purssell & McCrae, 2020). This review seeks to answer two questions:
1. To what extent does educational research on engagement and disengagement in higher education
define and operationalize the concept of validity?
2. To what extent does educational research on engagement and disengagement in higher education
define and operationalize the concept of fairness?
The second question is linked to the objective of this project since, according to the regional issues identified
in groups such as first-generation university students, it becomes relevant to consider how student identity
is considered for the validity of construct measurement, specifically for engagement and disengagement.
This relevance is highlighted by the AERA Standards (2014), which mentions the interrelation between
fairness and validity, requiring fair tests that take into account students' individual and contextual
characteristics.
The review procedure consists of three stages: search, selection, and analysis. The search process will be
carried out in relevant academic databases in social sciences and education: Web of Science, Scopus, EBSCO,
ERIC, and PsycINFO. For the feasibility of the review, the age of the studies will be based on the standards
for educational and psychological testing published by AERA (1999), therefore studies from 2000 onwards
will be considered. The terms used will be relevant to answer the question: engagement, disengagement,
disaffection, higher education, tertiary education, university, validity, measurement, validation.
The inclusion criteria for the search will be: quantitative empirical studies that use scales/measurement
instruments for engagement and disengagement in higher education and studies published from 2000 to
date. On the other hand, the exclusion criteria will be, firstly, articles with theoretical approaches or literature
reviews, studies conducted at other educational levels, and articles that do not meet the inclusion criteria.
The study selection process will be carried out in three stages: the first will consist of filtering the academic
databases to obtain empirical articles using the search terms and eliminating duplicates. Then, titles and
abstracts will be reviewed to ensure the mentioned inclusion criteria. Finally, the full texts of the preselected
studies will be reviewed to extract and analyze the relevant information.
b. Data analysis
The information extracted from the studies will be coded for subsequent analysis using NVivo software. First,
exploratory open coding will be conducted to identify and label the main concepts linked to the research
question: construct definition, instrument validation process, adherence to validity standards, and
representation of different student profiles in terms of disciplines, years of study, as well as sociodemographic
characteristics. A codebook will be developed that considers these concepts, as well as other relevant aspects
of the studies such as: context/region, authors and year of publication, participant characteristics. Then, the
identified concepts will be organized and related, considering emerging categories, matches, and distinctions
between the different studies analyzed. A supporting researcher will be available for both the selection
process and the development of the codebook and information analysis to iterate the findings collaboratively.
c. Expected results
Results are expected to contribute to the current discussion regarding the conceptualization and validity of
engagement and disengagement measurement in higher education, showing existing trends and gaps in the
literature, especially considering whether these studies contemplate the diversity of student groups within
their context and institution.
O2, O3, O4: Methodological aspects
a. Design and procedure
For specific objectives 2, 3, and 4, a descriptive-correlational design will be implemented. This design
encompasses the three analysis techniques to be carried out: evaluating the validity of the engagement and
disengagement scale, exploring relationships between latent and observed variables, and exploring the
existence of latent profiles, and is relevant to answer the research question.
To address objective 2, Multigroup Confirmatory Factor Analysis (MGCFA) will be used. This method will
provide evidence of validity for the engagement and disengagement scale, comparing its factor structure
between first-generation students and non-first-generation students. It seeks to provide evidence that the
items measure the same construct in both groups, to ensure the comparability of results between them.
MGCFA is relevant in the educational field, supported by previous studies that have used it to evaluate
measurement invariance in engagement and disengagement scales (Torres et al., 2024).
To determine the relationship between personal and contextual factors of the university experience
associated with engagement and disengagement (objective 3), Structural Equation Modeling (SEM) will be
employed. This technique will allow exploration of relationships between latent and observed variables,
analyzing how various factors that are part of first-generation students' university experience are linked to
their level of engagement and disengagement. Within this framework, a theoretical model is proposed based
on relationships found in the literature regarding personal and contextual factors that can influence their
perception of engagement and disengagement during their higher education.
To address objective 4, Latent Profile Analysis (LPA) technique will be used, which is relevant when there
is unobservable heterogeneity in the population, in this case, among first-generation university students.
This technique will allow identification of student profiles that report different patterns of disengagement.
Additionally, once profiles are identified, it will be possible to explore whether students' personal and
contextual attributes explain their profile membership, that is, to analyze how these profiles relate to
predictor variables (Spurk et al., 2020).
b. Data collection and sample selection
To address the questions in Objectives 2, 3, and 4, access will be granted to the VOCES Survey (Assessment
of Student Opinions and Characteristics) database. This survey is an instrument designed by the Millennium
Nucleus for Student Experience in Higher Education, belonging to the Pontificia Universidad Católica de Chile.
This survey collects students' perceptions regarding different aspects of their experience during higher
education, through different modules: First year (transition to higher education), Learning module, Teaching
and Curriculum module, Learning resources, Institutional support services, Social aspects, Well-being, Time
use, and Final year (transition to the labor market). These modules contain subdimensions and items from
international scales and others developed in-house, resulting from a qualitative study conducted in 2019 by
Millennium Nucleus, involving participating higher education institutions. To address the objectives of this
study, items will be taken from the following modules: Learning, Teaching and Curriculum, Institutional
support services, and Transition to the labor market (expectations).
In 2020, a pilot was conducted, and subsequently, the first version of the questionnaire was administered in
2021 to eleven (11) higher education institutions. The latest version of VOCES will be administered in 2024
in both online and in-person modes to students from public and private institutions from different regions,
considering disciplines from both Social Sciences and Humanities as well as Natural Sciences, Technology,
and Health. For its administration, previously stratified student groups were determined, divided into
schedules (day, evening, and distance learning). Approximately 600 completed surveys per schedule are
expected from each institution. Quota structures with student representation by year of study, gender, and
disciplinary area are considered. Additionally, efforts will be made to include students with diverse
sociocultural characteristics.
Regarding the data collection process, Millennium Nucleus will have a team of surveyors who will attend the
institution according to previously coordinated dates and times to administer the survey. Subsequently,
Millennium Nucleus will request administrative data from the institution regarding those students who
responded to the survey (whether complete or incomplete). Informed consent will be considered to guarantee
the research use of the survey information and to later cross-reference with student administrative data held
by the institution. Relevant data confidentiality will be maintained according to the study's security protocol,
which was approved by the ethics committee of the Pontificia Universidad Católica de Chile.
The sample to be taken from the 2024 database is aligned with the research objectives, therefore responses
will be selected from first-generation university students, differentiated by gender, year of study, and type
of institution (public and private), to then be contrasted with responses from university students who do not
belong to the first-generation higher education group. It is worth mentioning that in the previous application
of VOCES (2021), the participation of 13,806 students was achieved, of which 28.7% were first-generation
students. This variable is indicated in the questionnaire as self-reported by each student.
c. Identification and operationalization of variables
For achieving objectives 2, 3, and 4, the constructs will be measured with variables operationalized in the
VOCES scales detailed below. First, engagement and disengagement will be measured using items adapted
from the Engagement vs Disaffection with Learning scale (Skinner et al., 2009) in VOCES. This scale is based
on the Self-System Model of Motivational Development (SSMMD) and conceptualizes the antithesis of
engagement -named disaffection- as a multidimensional construct, comprising behavioral and emotional
components (Skinner et al., 2009). This scale will be used because it represents a four-factor model that has
shown the best fit to the data (Skinner et al., 2009): behavioral engagement, emotional engagement,
behavioral disaffection, and emotional disaffection. Henceforth, we will refer to the last two factors as
behavioral disengagement and emotional disengagement. The complete factor structure of the scale is
detailed in Figure 1.
Figure 1
Representation of structural relationships in the Disengagement scale
Note. Image adapted from A motivational perspective on engagement and disaffection:
Conceptualization and assessment of children's behavioral and emotional participation
in academic activities in the classroom. Skinner, E. A., Kindermann, T. A., & Furrer, C.
J. (2009). Educational and psychological measurement, 69(3), 493-525
The behavioral disengagement subscale consists of five items that record behaviors indicative of maladaptive
participation, such as lack of effort, passivity, or ceasing to participate (Skinner et al., 2008). Some examples
of adapted items include: "I find it difficult to come to class" and "When I'm in class, I'm really doing other
things". The emotional disengagement subscale consists of twelve items that reflect various maladaptive
emotions regarding learning, specifically: boredom, anxiety, and frustration (Skinner et al., 2008). The
VOCES adaptation takes five of the twelve items from the original Skinner et al. (2008) scale. Some examples
of adapted items include "When I'm in class, I get bored." and "My classes stress me out". In addition to the
disengagement subscales, the VOCES survey includes items to evaluate behavioral engagement and
emotional engagement factors, also adapted from the Engagement vs Disaffection with Learning scale
(Skinner et al., 2009). Each factor corresponds to a subscale with five items. The behavioral engagement
subscale includes items such as: "I pay attention in my classes". The emotional engagement subscale
includes items like "I enjoy going to classes".
For objective 3, the proposed model is an adaptation of the SSMMD used by Skinner et al. (2008, 2009)
and places engagement and disengagement in the "action" section of it, in a dependent variable relationship
to the factors that precede it. The model examines how contextual factors influence self-systems, which in
turn predict students' levels of engagement or disengagement (Skinner et al., 2008). The model variables
were operationalized through items from the VOCES questionnaire, an instrument that integrates
internationally validated scales with in-house developed items. Figure 2 shows a visual representation of this
adapted model, highlighting the relationships between contextual factors, self-systems, and engagement
and disengagement variables.
Figure 2
Proposed theoretical model based on the SSMMD framework
Note. Image adapted from Engagement and disaffection in the classroom: Part of a
larger motivational dynamic?, by Skinner, E., Furrer, C., Marchand, G., & Kindermann,
T. (2008). Journal of educational psychology, 100(4), 765.
Regarding contextual factors, the model considers four factors: expectations about the value of higher
education, perceived academic quality, perceived accessibility of institutional support services, and difficulties
during their education.
The reviewed literature indicates that expectations about education influence how students relate to their
educational experience, being malleable contextual factors that respond to the socioeconomic environment
(Gonzalez Paoloni, 2014; Li & Xue, 2023). This variable contains VOCES' own items that represent beliefs
and valuations that students attribute to higher education in terms of socioeconomic benefits, social
contribution, professional development, and personal well-being.
Institutional factors of educational quality comprise items that evaluate students' perception of teaching
quality, learning assessment practices, and structural characteristics of the curriculum. Evidence
demonstrates how these contextual elements directly influence students' academic commitment. Thus,
Chipchase et al. (2017) indicate that teaching quality and assessment practices are critical determinants that
can facilitate or hinder student engagement. Also, Pham et al. (2022) highlight that a well-designed curricular
structure, which balances workload and aligns content with practical applications, contributes to a more
satisfactory educational experience and promotes higher levels of engagement.
These institutional factors are evaluated in the VOCES survey through items that integrate adaptations from
the United Kingdom's National Student Survey (NSS), the Course Experience Questionnaire (CEQ) (McInnis
et al., 2001), the LEARN+ questionnaire (Parpala & Lindblom-Ylänne, 2012), and items specifically developed
for the questionnaire.
According to Chipchase et al. (2017), accessibility to institutional support services is a critical contextual
factor that can generate student disengagement. This factor is measured through proprietary items, which
gather students' perception of the availability and accessibility of various institutional support services, such
as administrative, health, psychological, social, financial, vocational, and academic services.
Regarding difficulties during their education, Chipchase et al. (2017) consider that there are certain
extrinsic factors that represent obstacles in education and influence student disengagement. The VOCES
questionnaire captures these obstacles through proprietary items that mention different types of difficulties
that can arise in higher education, such as academic, work-related, family, economic, and health issues.
As part of the SSMMD framework, self-systems mediate the relationship between the educational context
and the action component of the model (Skinner et al., 2008, 2009). These constructs have been proposed
mainly from Self-Determination Theory and are defined as three basic psychological needs that seek to be
satisfied: autonomy, competence, and sense of relatedness and belonging (Connell & Wellborn, 1991). The
degree to which a person needs to satisfy these needs and the behavioral patterns that have led them to
satisfy them are relatively stable factors, especially when compared to contextual factors (Skinner et al.,
2009). Student autonomy, competence, and sense of relatedness and belonging are measured through five
items each.
d. Data analysis plan
To address Objective 2, which aims to examine the meaning of responses to the Skinner et al. (2008) scale
and its invariance in first-generation students, the selected data from VOCES will be analyzed considering
the stages detailed below. In both cases, R software will be used to analyze the data.
Multigroup Confirmatory Factor Analysis (MGCFA) will be used to evaluate whether the factor structure of
disengagement behaves similarly in first-generation university students. According to Vandenberg and Lance
(2000), the three levels of invariance will be evaluated to establish the validity of a measure across different
groups—configural, metric, and scalar—organizing these levels hierarchically. The results will be interpreted
taking into account the level of invariance achieved.
To address Objective 3, Structural Equation Modeling (SEM) will be used, which will allow examination of
whether first-generation student status moderates the relationships between students' contextual and
personal factors with their perception of engagement and disengagement. For this, the selected VOCES data
will be analyzed considering the following stages:
First, the Structural Equation Model (SEM) will be specified based on the theoretical review of previous
literature linking students' contextual and personal factors with engagement and disengagement, having
previously defined the variables and their observed indicators in the measurement model (Muthén and
Asparouhov, 2015). Subsequently, the structural model will be evaluated, which will include hypothetical
relationships between latent variables, incorporating first-generation student status as a moderating variable
(MacKinnon et al., 2007).
First, model fit will be evaluated using indices such as CFI, TLI, RMSEA, and SRMR, with interpretation criteria
recommended by Little et al. (2007). Good fit will indicate that the proposed model adequately represents
the relationships between the studied variables. Then, parameter estimation will proceed using maximum
likelihood methods, as suggested by Preacher and Hayes (2008). This approach will allow obtaining
coefficient estimates that best reproduce the observed covariance matrix. Finally, to handle the moderating
variable (first-generation student status), strategies such as creating interaction terms between firstgeneration status and contextual factors will be applied, or a multigroup analysis will be conducted to
compare effects between first-generation students and those who are not. These techniques allow evaluation
of whether the strength of relationships between contextual factors and engagement and disengagement
varies according to first-generation status, providing a deeper understanding of the dynamics influencing
this phenomenon (Little et al., 2007; Muthén & Asparouhov, 2015).
To address missing data, multiple imputation (MI) will be used. MI is considered one of the most robust
methods for handling missing data. According to Graham (2009), MI maintains the natural variability of the
data by incorporating a random component that allows producing unbiased estimates and preserving
relationships that exist between variables at the population level. This method is appropriate when data can
be considered MAR (Missing At Random), that is, when the probability of missing data may depend on
observed data, but not on unobserved data (Schafer & Graham, 2002). Additionally, it allows use of all
available information, improving the validity of conclusions (Langkamp et al., 2010).
In the present study, data collected by VOCES are nested, with students grouped within educational
institutions. This hierarchical structure must be considered to ensure the precision of data analysis (Lüdtke
et al., 2009; Huang, 2018). The evaluation of the nested structure will be carried out using the Intraclass
Correlation Coefficient (ICC), which indicates the proportion of total variance attributable to differences
between institutions (Snijders & Bosker, 2012). This approach helps identify the degree of dependency of
observations within each group (Shieh, 2016).
Control variables will be included to isolate associations of interest and avoid spurious relationships. These
variables have been selected based on empirical evidence indicating their influence on disengagement.
Gender will be included because previous studies have identified significant differences in motivation and
engagement levels between male and female students (Jiang et al., 2024; Darmody & Thornton, 2015). The
disciplinary area of study will also be controlled, which has proven to be a significant factor in variations of
motivation and engagement. Jiang et al. (2024), for example, found that science and social science students
show different patterns of engagement. Finally, institution type will be controlled. Jiang et al. (2024) found
that the institution students attend significantly influences their levels of motivation and engagement.
To address Objective 4, student profiles will be identified based on emerging patterns in the data according
to the engagement and disengagement scale. Models will be estimated with profiles added iteratively (e.g.,
2 profiles, 3 profiles, 4 profiles, etc.) and then these models will be evaluated considering a series of fit
indices (AIC, ABIC, VLMR, Adj. LRT) (Spurk et al., 2020), in addition to model entropy, to ensure that
identified profiles fit the data and reflect clearly differentiated patterns. Based on the above, in addition to
conceptual interpretation of patterns, the number of profiles that best represents the data will be determined.
Subsequently, a description and comparison of these profiles will be conducted, highlighting differences in
engagement and disengagement dimensions, as well as the size of each group. Finally, how these profiles
relate to students' contextual and personal variables will be explored through the application of multinomial
regression.
e. Expected results
For Objective 2, we expect to determine if the criteria for metric, scalar, and strict invariance are met in
the measurement of the engagement and disengagement scale to provide evidence of its validity, with the
purpose of reporting and discussing the implications of these findings as a contribution to the research of the
construct in the global educational field. For Objective 3, we expect to present an adjusted model from
Structural Equation Modeling (SEM), to contribute to the literature on engagement and disengagement in
higher education and to the relevance of considering the factors related to these constructs in specific groups
of students, such as first-generation higher education students. This is intended to provide relevant
information to educational institutions that allows them to provide specific support for their students' issues.
For Objective 4, we expect to identify latent profiles based on the engagement and disengagement scale,
contributing to the diagnosis of the situation of different groups of first-generation students in higher
education. This diagnosis will allow educational institutions to better understand the specific needs and
challenges of each profile, for the design of more appropriate intervention strategies, as well as for the
development of inclusive and relevant institutional policies and practices.
WORK PLAN
Below is the monthly plan to implement each of the three project objectives, considering a comprehensive
view that takes into account the fulfillment of all requirements for obtaining the degree in the required time
(2025-2026). Activities will begin in January 2025, following the candidacy exam.
Year I (2025)
Objetives
Activities
Jan
O1: Analyzing how
educational research
in higher education
defines
and
operationalizes
the
concept of validity and
fairness
when
studying engagement
and disengagement.
Submitting research proposal to ethics x
committee
Search and selection process of studies
Feb Mar
Ap
x
x
Writing the article, including results and
discussion
O2: Examining to
what
extent
the
meaning of responses
to the engagement
and disengagement
scale differs in firstgeneration
higher
education students
manuscript
to
Jun Jul
x
x
Aug Se
Oct Nov Dic
x
Data coding and analysis
Submitting
journals
May
x
academic
x
x
Requesting and accessing the VOCES
Survey database
x
x
Data extraction of responses to the model
variable items according to the student
sample
x
Data analysis using MGCFA
x
x
x
Writing the article, including results and
discussion
x
x
x
x
Submitting the manuscript to academic
journals
x
x
*Submitting proposals to international
conferences for participation as a speaker
x
x
x
x
Year II (2026)
Objetives
Activities
Jan
O3:
Examining
the
association between firstgeneration higher education
students' perception of their
engagement
and
disengagement and personal
and contextual factors of
their university experience.
Data extraction of responses to the x
model variable items according to
the student sample
O4: Exploring the existence
of student profiles based on
their
perception
of
engagement
and
disengagement
and
exploring the relationship
between profile membership
with other contextual and
Submitting the manuscript
academic journals
Data analysis (SEM + LPA)
Writing the article for O3, including
results and discussion
to
Writing the article for O4
Submitting the manuscript
academic journals
to
Feb Mar
Ap
May
Jun Jul
x
x
x
x
x
x
x
x
Aug Se
Oct Nov Dic
x
x
x
x
x
x
x
x
x
sociodemographic
*Attendance
at
international
characteristics
of
first- conference
generation
university
*International internship
students.
AVAILABLE RESOURCES
The researcher has the ANID scholarship for national doctoral studies (2023 to 2026) and will apply for
complementary benefits in the call at the beginning of 2025.
BUDGET
Summary
Year 1
Year 2
Total
Personnel
$ 50 000
$ 50 000
$ 100 000
International travel (conference)
$ 1 500 000
$0
$ 1 500 000
$0
$ 1 500 00
$ 1 500 000
$ 500 000
$ 500 000
$ 1 000 000
$0
$0
$0
$ 2 050 000
$ 2 050 000
$ 4 100 000
International cooperation travel (internship)
Operating expenses
Capital goods
Total
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