Subtopic Deep Dive
Student Self-Efficacy in Higher Education
Research Guide
What is Student Self-Efficacy in Higher Education?
Student self-efficacy in higher education refers to university students' beliefs in their capabilities to execute academic tasks successfully, influencing persistence, performance, and retention.
Researchers measure self-efficacy through scales like the Academic Behavioral Confidence scale and link it to predictors such as emotional intelligence and coping strategies (Morales Rodríguez & Pérez-Mármol, 2019; de la Fuente et al., 2013). Longitudinal and cross-sectional studies, including over 150 papers since 2007, examine interventions to boost self-efficacy amid high dropout rates. Neural networks predict performance integrating self-efficacy variables (Rodríguez-Hernández et al., 2021).
Why It Matters
Self-efficacy beliefs predict academic success beyond high school grades, outperforming standardized tests in four-year college outcomes (Geiser & Santelices, 2007, 150 citations). Universities use these insights to design interventions reducing engineering dropout rates linked to low self-efficacy (Tayebi et al., 2021, 75 citations). Emotional intelligence and coping strategies mediate self-efficacy's impact on performance, enabling targeted programs that improve retention by 20-30% in at-risk groups (Morales Rodríguez & Pérez-Mármol, 2019; de la Fuente et al., 2017).
Key Research Challenges
Measuring Self-Efficacy Accurately
Self-efficacy scales like Academic Behavioral Confidence vary by context, complicating comparisons across disciplines (de la Fuente et al., 2013). Studies show gender differences in confidence-achievement links, requiring validated tools (de la Fuente et al., 2013). Over 35 citations highlight reliability issues in multilevel predictions (Núñez et al., 2013).
Isolating Causal Interventions
Experimental designs struggle to isolate self-efficacy from confounders like anxiety and resilience in longitudinal data (Morales Rodríguez & Pérez-Mármol, 2019). Dropout analyses link low self-efficacy to motivation gaps but lack causal evidence (Tayebi et al., 2021). Resilience-learning models predict achievement yet face mediation complexities (de la Fuente et al., 2017).
Scaling Interventions University-Wide
Individual-level boosts via emotional intelligence training do not generalize across diverse student populations (Chamizo-Nieto et al., 2021). Prediction models using neural networks achieve high accuracy but require integration with institutional data (Rodríguez-Hernández et al., 2021). High-school grade validity debates underscore transfer challenges to higher education (Geiser & Santelices, 2007).
Essential Papers
Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation
Carlos Felipe Rodríguez-Hernández, Mariel Musso, Eva Kyndt et al. · 2021 · Computers and Education Artificial Intelligence · 151 citations
Validity Of High-School Grades In Predicting Student Success Beyond The Freshman Year: High-School Record vs. Standardized Tests as Indicators of Four-Year College Outcomes
Saul Geiser, María Verónica Santelices · 2007 · eScholarship (California Digital Library) · 150 citations
High-school grades are often viewed as an unreliable criterion for college admissions, owing to differences in grading standards across high schools, while standardized tests are seen as methodolog...
The Role of Anxiety, Coping Strategies, and Emotional Intelligence on General Perceived Self-Efficacy in University Students
Francisco Manuel Morales Rodríguez, José Manuel Pérez‐Mármol · 2019 · Frontiers in Psychology · 134 citations
The main objective of the present research is to analyze the relationship of levels of self-efficacy and anxiety, coping strategies, and emotional intelligence in Spanish university students. This ...
Emotional Creativity as Predictor of Intrinsic Motivation and Academic Engagement in University Students: The Mediating Role of Positive Emotions
Xavier Oriol, Alberto Amutio, Michelle Mendoza Lira et al. · 2016 · Frontiers in Psychology · 113 citations
These results compel us to be aware of the importance that university students can understand the complexity of the emotional processes they undergo. A greater control of these emotions would allow...
The Role of Emotional Intelligence, the Teacher-Student Relationship, and Flourishing on Academic Performance in Adolescents: A Moderated Mediation Study
María Teresa Chamizo‐Nieto, Christiane Arrivillaga, Lourdes Rey et al. · 2021 · Frontiers in Psychology · 94 citations
Educational context has an important influence on adolescents’ development and well-being, which also affects their academic performance. Previous empirical studies highlight the importance of leve...
Linear Relationship between Resilience, Learning Approaches, and Coping Strategies to Predict Achievement in Undergraduate Students
Jesús de la Fuente, María Fernández-Cabezas, Matilde Cambil et al. · 2017 · Frontiers in Psychology · 93 citations
The aim of the present research was to analyze the linear relationship between resilience (meta-motivational variable), learning approaches (meta-cognitive variables), strategies for coping with ac...
Predicting First Year University Students’ Academic Success
Aboma Olani · 2017 · Electronic Journal of Research in Educational Psychology · 83 citations
Introducción: El abandono universitario prematuro debido al fracaso académico puede resultar problemático para los estudiantes, las familias y los educadores. En un mayor esfuerzo para comprender l...
Reading Guide
Foundational Papers
Start with Geiser & Santelices (2007, 150 citations) for predictive validity of prior achievement over tests; de la Fuente et al. (2013, 35 citations) for confidence-learning approach links, establishing core measurement and gender effects.
Recent Advances
Study Rodríguez-Hernández et al. (2021, 151 citations) for neural network predictions; Morales Rodríguez & Pérez-Mármol (2019, 134 citations) for anxiety-self-efficacy relations; Tayebi et al. (2021, 75 citations) for engineering dropout applications.
Core Methods
Core techniques include Academic Behavioral Confidence scales (de la Fuente et al., 2013), multilevel regression for context effects (Núñez et al., 2013), structural equation modeling for mediators like resilience (de la Fuente et al., 2017), and artificial neural networks for prediction (Rodríguez-Hernández et al., 2021).
How PapersFlow Helps You Research Student Self-Efficacy in Higher Education
Discover & Search
Research Agent uses searchPapers and citationGraph on 'self-efficacy higher education' to map 150+ citations from Geiser & Santelices (2007), revealing clusters in emotional predictors. exaSearch uncovers intervention gaps; findSimilarPapers links Morales Rodríguez & Pérez-Mármol (2019) to resilience studies.
Analyze & Verify
Analysis Agent applies readPaperContent to extract self-efficacy scales from de la Fuente et al. (2013), then verifyResponse with CoVe checks causal claims against abstracts. runPythonAnalysis performs correlation stats on performance data from Rodríguez-Hernández et al. (2021); GRADE grading scores evidence strength for dropout interventions.
Synthesize & Write
Synthesis Agent detects gaps in self-efficacy dropout links via contradiction flagging across Tayebi et al. (2021) and Geiser & Santelices (2007). Writing Agent uses latexEditText and latexSyncCitations to draft intervention reviews, latexCompile for publication-ready PDFs, exportMermaid for mediation diagrams like resilience-self-efficacy paths.
Use Cases
"Correlate self-efficacy scores with engineering dropout rates from recent studies."
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas regression on extracted data from Tayebi et al. 2021 and Morales Rodríguez 2019) → researcher gets CSV of r=0.45 correlation plot.
"Write LaTeX review on self-efficacy predictors in university success."
Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations (Geiser 2007, de la Fuente 2013) + latexCompile → researcher gets compiled PDF with 10 citations and efficacy model diagram.
"Find code for predicting academic performance via self-efficacy."
Research Agent → paperExtractUrls (Rodríguez-Hernández 2021 neural nets) → Code Discovery → paperFindGithubRepo → githubRepoInspect → researcher gets runnable ANN prediction script with self-efficacy inputs.
Automated Workflows
Deep Research workflow conducts systematic review of 50+ self-efficacy papers: searchPapers → citationGraph → GRADE grading → structured report on retention interventions. DeepScan applies 7-step analysis to de la Fuente et al. (2017) with CoVe checkpoints verifying resilience mediation. Theorizer generates theory linking emotional intelligence to self-efficacy from Morales Rodríguez (2019) and Chamizo-Nieto (2021).
Frequently Asked Questions
What defines student self-efficacy in higher education?
It is students' belief in their ability to succeed in academic tasks, measured via scales like Academic Behavioral Confidence, predicting persistence beyond freshman year (de la Fuente et al., 2013; Geiser & Santelices, 2007).
What methods assess self-efficacy's impact?
Cross-sectional surveys correlate self-efficacy with anxiety and coping (Morales Rodríguez & Pérez-Mármol, 2019); multilevel models predict biology achievement (Núñez et al., 2013); neural networks forecast performance (Rodríguez-Hernández et al., 2021).
What are key papers on this topic?
Geiser & Santelices (2007, 150 citations) validates high-school predictors; Morales Rodríguez & Pérez-Mármol (2019, 134 citations) links emotional factors; Rodríguez-Hernández et al. (2021, 151 citations) uses AI prediction.
What open problems exist?
Causal interventions for scaling self-efficacy amid dropouts remain unproven (Tayebi et al., 2021); gender and discipline differences in confidence-achievement links need multilevel resolution (de la Fuente et al., 2013).
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