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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025
Self-Directed Learning in Higher Education: An Integrated Mixed-
Methods Study
Jesús Alberto Sánchez Valtierra
Universidad Virtual del Estado de Guanajuato
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000196
Received: 20 October 2025; Accepted: 27 October 2025; Published: 07 November 2025
ABSTRACT
This study examined self-directed learning competence in undergraduate students through an integrated mixed-
methods design. The qualitative phase included semi-structured interviews with six students identified as
competent in self-directed learning; the quantitative phase administered a 30-item Likert scale to 50 employed
students (mean age 24.3 years; 56% female). Cronbach's alpha analyses revealed acceptable reliability for
personal motivations (α = 0.849) and social skills (α = 0.677), though cognitive competencies showed limitations
(α = 0.485). Thematic analysis of interviews identified that self-directed learning emerges from personal
initiative, depends on intrinsic motivation and preexisting abilities, and varies by disciplinary context. The
findings suggest that personal motivations, persistence, and self-regulation are key antecedents of self-directed
learning in university contexts. Implications for curriculum design and future research are discussed.
Keywords: self-directed learning, competencies, higher education, motivation, self-regulation
INTRODUCTION
Context and relevance
Global educational transformation demands that higher education institutions cultivate autonomous learners
capable of self-regulating their learning processes. When students assume active participation as agents of their
own learning, they develop metacognition, self-awareness, and tools to address complex and changing contexts
(Knowles, 1975; Zimmerman, 2000).
In Mexico, despite progress in international assessments, significant challenges persist. El Colegio de México
reported that approximately half of adolescents lack basic competencies to meet everyday demands (Servín,
2014). This context motivated recent educational reforms: the Comprehensive Reform of Upper Secondary
Education (RIEMS, 2008) and the New Mexican School (NEM, 2019), both prioritizing self-directed learning
as a cross-cutting axis.
Competency-based education emphasizes not merely what a student knows, but how that knowledge is applied
in authentic contexts. To achieve this, understanding the psychological, motivational, and contextual
mechanisms underlying self-directed learning in higher education is essential.
Research objectives
To characterize how undergraduate students perceive and deploy self-directed learning
To identify cognitive, motivational, and social factors associated with competence in self-directed learning
To integrate qualitative and quantitative findings to propose a descriptive model of self-directed learning in
university contexts
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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THEORETICAL FRAMEWORK
Definition and conceptualization
Self-directed learning is a systematic process encompassing cognitive, conative, and behavioral dimensions
(Knowles, 1975). In this process, the learner assumes initiative to diagnose learning needs, formulate objectives,
identify resources, and apply learning strategies while reflexively evaluating outcomes.
Merriam and Caffarella (1999) emphasize that self-directed learning is context-dependent: an individual may be
autonomous in certain disciplinary domains but not in others, depending on prior knowledge, motivations, and
situational factors.
For this study, the following operational definition is adopted:
Self-directed learning is a self-regulated, learner-initiated process in which cognitive competencies (analysis,
synthesis, evaluation), intrinsic motivations (interest, curiosity, personal goals), and socioemotional skills
(communication, persistence, receptivity to feedback) are combined, enabling autonomous acquisition of
knowledge, skills, and attitudes transferable to diverse contexts.
Influential theoretical models
Knowles (1975) characterizes self-directed learning through: individual initiative, diagnosis of needs,
formulation of objectives, identification of resources, selection of strategies, and self-evaluation.
Guglielmino (1977) identifies eight factors characterizing competent autonomous learners: openness to
learning, positive self-concept as a learner, initiative and independence, informed acceptance of responsibility,
love of learning, creativity, future orientation, and problem-solving ability.
Zimmerman (2000) emphasizes self-regulation as a cyclical process: goal setting, metacognitive monitoring,
strategic adjustment, and reflection on outcomes.
Merriam and Caffarella (1999) stress the contextual and situated nature of self-directed learning, rejecting
conceptions of absolute universal autonomy.
Recent research
Díaz Vásquez (2024) demonstrated that flipped classroom methodologies facilitate self-directed learning,
particularly in pre-class phases. Moreno Betancourt (2024) found that techniques such as active reading,
summaries, and graphic organizers improve academic performance. Olguín-Guzmán (2024) emphasizes that
both face-to-face and virtual environments require explicit structuring of goals, activity planning, and continuous
feedback to consolidate effective self-directed learning.
METHODOLOGY
General design
An integrated sequential mixed-methods design was employed (qualitative → quantitative), wherein qualitative
findings informed the construction of quantitative instruments.
Qualitative phase: Case study
Participants: Six undergraduate students (mean age 23.5 years; 50% female) identified by peers and instructors
as competent in self-directed learning. Inclusion criteria: (a) superior academic performance (GPA ≥ 3.5/4.0),
(b) evidence of metacognitive self-regulation, (c) documented participation in independent learning activities.
Exclusion criteria: (a) academic probation status, (b) undisclosed cognitive disability diagnoses.
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Data collection: Semi-structured interviews (45–60 minutes) with 8 predetermined questions:
Do you perceive yourself as competent in self-directed learning?
How do you describe yourself in terms of mastery? (strengths and limitations)
Can you provide specific examples of using this competence?
How did you develop this competence?
What motivated you to develop it?
Where will you direct future learning efforts?
What evidence demonstrates your self-directed learning?
What level of satisfaction do you report with your performance?
Interviews were audio-recorded (with consent), transcribed verbatim, and coded using inductive thematic
analysis following Braun and Clarke (2006) procedures.
Qualitative analysis: Two researchers independently coded 100% of transcripts to ensure reliability. First-order
themes (codes emergent from text) and second-order themes (conceptual groupings) were established.
Consensus was achieved through iterative discussion. Disagreements were documented (Cohen's kappa: 0.82).
Ethical approval: The protocol was approved by the institutional ethics committee.
Quantitative phase: Survey
Participants: 50 undergraduate students in distance education (age range 19–31 years, M = 24.3, SD = 3.2; 22
males = 44%, 28 females = 56%; all employed). Sampling: non-probabilistic purposive. Location: Irapuato,
Guanajuato. Inclusion criteria: (a) enrolled in higher education, (b) age ≥ 18 years, (c) informed consent
provided. Exclusion criteria: (a) age < 18 years, (b) inability to complete instrument.
Instrument: A 30-item Likert scale organized into three dimensions of 10 items each:
Cognitive competencies (α = 0.485): metacognitive self-regulation, critical thinking, problem-solving (e.g., "I
critically analyze information before integrating it into my learning")
Personal motivations (α = 0.849): academic goals, intrinsic interest, persistence (e.g., "My learning objectives
align with my personal interests")
Social skills (α = 0.677): communication, openness to feedback, collaboration (e.g., "I seek peer feedback to
improve my performance")
Response scale: 4 points (1 = Strongly disagree; 4 = Strongly agree). Score ranges: 10–40 per dimension, 30–
120 globally.
Instrument development: Items were generated from (a) theoretical literature on self-directed learning, (b)
preliminary qualitative thematic analysis, and (c) review by five experts in higher education. A pilot test with 15
students was conducted; item analyses were performed and items with item-total correlation < 0.30 were
eliminated.
Administration: In-class administration (15–20 minutes). One hundred percent of participants completed the
entire scale; no missing data.
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Quantitative analyses: SPSS 26.0 was used. Descriptive statistics (M, SD, range), reliability analysis
(Cronbach's alpha), correlational analysis (Pearson), and analysis of variance (ANOVA) for gender comparisons
are reported. Given sample size and distribution, assumptions of normality (Shapiro-Wilk) and homogeneity of
variance (Levene's test) were verified.
Triangulation
Qualitative and quantitative findings were integrated through systematic comparison: emergent themes from
interviews were compared with dimensions of the Likert scale, identifying convergences, divergences, and
complementarities.
RESULTS
Qualitative results
Theme 1: Self-perception of competence
All six interviewees reported perceiving themselves as competent in self-directed learning, though with
important nuance: all acknowledged differentiated mastery according to disciplinary contexts. One participant
noted: "In methodology courses I feel I control my learning well, but in mathematics I still depend heavily on
tutoring." This pattern confirms Merriam and Caffarella's (1999) findings on context-dependency.
Theme 2: Origins of self-directed learning
Three participants attributed development to family factors ("values of responsibility instilled at home"), two
negative prior academic experiences that promoted autonomy, and one to specific professional motivations. One
participant stated: "When I entered university, I realized no one was going to teach me everything; I had to seek
resources on my own."
Theme 3: Role of motivations
All interviewees emphasized that intrinsic motivations (curiosity, long-term personal goals) sustained persistent
effort. When asked about obstacles, five mentioned that original motivations were critical for overcoming
difficulties: "If you don't really care about the topic, it's hard to keep learning when things get complicated."
Theme 4: Strategies employed
Participants described combinations of strategies: self-directed resource seeking (books, videos, online tutorials),
temporal organization of activities, reflection on errors, and requesting feedback from instructors and peers. One
participant highlighted: "After each exam, I sit down and write what I didn't understand so I can address that
later."
Quantitative results
Descriptive statistics by dimension:
Dimension
M
SD
Observed range
Possible range
Cognitive Competencies
29.4
4.2
18–38
10–40
Personal Motivations
34.8
3.6
24–40
10–40
Social Skills
32.1
4.1
20–40
10–40
Global Score
96.3
9.8
68–119
30–120
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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Participants showed higher scores on Personal Motivations (M = 34.8), followed by Social Skills (M = 32.1).
Cognitive Competencies showed the greatest relative variability (CV = 14.3%), suggesting heterogeneity in
metacognitive regulation.
Reliability analysis
Personal Motivations: α = 0.849 (excellent)
Social Skills: α = 0.677 (acceptable)
Cognitive Competencies: α = 0.485 (questionable)
The low reliability of the cognitive scale suggests that the 10 items do not coherently capture unidimensional
construct. Post-hoc item analysis revealed that three items (20, 14, 7) demonstrated item-total correlations <
0.25. This indicates a problematic operationalization of cognitive competencies requiring attention in future
research.
Correlations between dimensions:
r
p
0.34
0.018
0.28
0.052
0.61
< 0.001
The strong correlation between Personal Motivations and Social Skills (r = 0.61, p < 0.001) suggests that students
with clear goals actively seek peer and instructor feedback. The weaker correlation between Cognitive
Competencies and other dimensions may reflect the reliability problems identified.
Analysis by Gender
One-way ANOVA compared scores by gender:
Dimension
Males (M, SD)
Females (M, SD)
F
p
Cognitive
29.0 (4.5)
29.7 (4.0)
0.38
0.537
Motivations
34.2 (3.9)
35.2 (3.4)
1.24
0.271
Social
31.4 (4.3)
32.6 (3.9)
1.68
0.201
Global
94.6 (10.8)
97.5 (9.0)
1.06
0.309
No significant gender differences were detected in any dimension (all p > 0.05), partially aligning with recent
findings but diverging from some prior research.
Qualitative-quantitative integration
Triangulation revealed convergences and complementarities:
Convergence: The themes of "personal motivations" and "origins of self-directed learning" in qualitative
analysis aligned with the quantitative Personal Motivations dimension (M = 34.8), underscoring the centrality
of intrinsic motivation. Both data phases emphasize that personal initiative is a prerequisite.
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Complementarity: Qualitative data provided explanatory mechanisms absent in quantitative data. For example,
while Motivations and Social Skills correlate (r = 0.61), interviews revealed that seeking feedback is a deliberate
strategy to sustain motivation during adversity.
Divergence: The low reliability of Cognitive Competencies contrasts with the sophistication of metacognitive
strategies described in interviews. This suggests that operationalization of cognitive constructs requires
conceptual refinement.
DISCUSSION
Integrative synthesis
Self-directed learning in higher education emerges as a multidimensional phenomenon integrating cognitive,
motivational, and socioaffective dimensions. The findings suggest a model wherein:
Personal motivations constitute the primary engine: Students with clear goals, intrinsic curiosity, and
sustained persistence demonstrate greater self-directed learning competence. This supports self-determination
theories (Ryan & Deci, 2000) in adult educational contexts.
Social skills amplify individual self-regulation: Seeking feedback, willingness to collaborate, and effective
communication enable individuals to transcend personal cognitive limitations through access to collective
knowledge.
Cognitive competencies require improved operationalization: Data suggest that measures of metacognitive
self-regulation must capture not only metacognitive knowledge but observable performance of monitoring and
strategic adjustment.
Theoretical implications
The results refine prior conceptualizations of self-directed learning. While Knowles (1975) emphasized initiative
as a defining element, this study underscores that initiative without intrinsic motivational substrate generates
superficial and unsustained self-directed learning. Contextual variability (domain-specific autonomy) supports
Merriam and Caffarella (1999) and highlights the futility of universal autonomy conceptions.
Practical implications for higher education
For instructors: Course design should include explicit phases of (a) connecting content to students' personal
goals, (b) modeling metacognitive strategies, and (c) structuring peer and instructor feedback opportunities.
For curriculum design: Transversal competencies such as self-directed learning require deliberate integration
across multiple courses with clear learning objectives alignment. RIEMS and NEM in Mexico have identified
this need; operational implementation requires instructor training and reconfiguration of learning spaces.
For virtual environments: Given the prevalence of distance education (the sample included online students),
educational platforms must facilitate resource seeking, task organization, metacognitive reflection, and
asynchronous feedback.
Study limitations
Limited external validity: Non-probabilistic sampling with N = 50 in a single geographic location restricts
generalization. All participants were employed; results may not transfer to full-time students.
Cognitive competencies reliability: The Cronbach's alpha of 0.485 indicates that items do not adequately
capture the construct. Future research must redesign this scale through item analysis and validation with
independent samples.
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Cross-sectional design: Data were collected at a single time point. Longitudinal designs would allow
examination of self-directed learning development trajectories.
Absence of contextual variables: The study did not capture institutional climate, teaching quality, academic
load, or access to technological resources—factors likely moderating self-directed learning.
Limited qualitative sample: Although six cases suffice for case study research, larger qualitative samples
wouldenable more robust conceptual saturation.
Future directions
Replicate the study with stratified samples (in-person students, online students, full-time students, multiple
institutions) to improve external validity.
Redesign the cognitive competencies scale incorporating observable indicators of metacognitive monitoring
(e.g., reflection journals, self-generated concept maps).
Conduct longitudinal studies tracing self-directed learning evolution across undergraduate careers.
Investigate implementation mechanisms of reforms such as NEM that cite self-directed learning as priority;
examine policy-practice gaps.
Explore the role of educational technologies (AI tutors, learning management platforms) as facilitators of self-
directed learning in distance education.
Perform quasi-experimental research comparing effectiveness of pedagogical interventions specifically designed
to develop metacognitive competencies of self-directed learning.
CONCLUSIONS
This study provided integrated evidence that self-directed learning in higher education is a multidimensional
process primarily sustained by intrinsic personal motivations, amplified by socioaffective skills, and expressed
through metacognitive competencies. Findings support the priority given to self-directed learning in recent
Mexican educational reforms while underscoring the need for clear operationalization in educational practice.
The integrated mixed-methods approach enabled capturing both qualitative underlying mechanisms (how and
why students develop self-directed learning?) and quantitative population profiles. However, results must be
interpreted within identified methodological limitations.
Future research must address contextual heterogeneity, refine psychometric instrumentation, and examine
longitudinal trajectories to consolidate a rigorous science of self-directed learning in higher education that
informs educational policy and teaching practice with empirical rigor.
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CONFLICTS OF INTEREST
The author declares no potential conflicts of interest.
Funding
No external financial support was provided for this article.
Note
This article is not derived from any previous publication.