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Segundo Salatiel Malca-Peralta; Rosa Marilú Velarde Ruiz; Hilda Raquel Alarcón Lescano – Electronic Journal of Research in Educational Psychology, 2024
Introduction: The objective of the present study was to determine if emotional intelligence and family stress are predictors of satisfaction with studies in university students. Method: Cross-sectional predictive study with a quantitative approach. The population was made up of 414 university students of both sexes who applied the TMMS-24…
Descriptors: Emotional Intelligence, College Students, Stress Variables, Predictor Variables
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Robison, Matthew K.; Brewer, Gene A. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2022
The present study examined individual differences in 3 cognitive abilities: attention control (AC), working memory capacity (WMC), and fluid intelligence (gF) as they relate the tendency to experience task-unrelated thoughts (TUTs) and the regulation of arousal. Cognitive abilities were measured with a battery of 9 laboratory tasks, TUTs were…
Descriptors: Individual Differences, Short Term Memory, Attention Control, Intelligence
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Michael Generalo Albino; Femia Solomon Albino; John Mark R. Asio; Ediric D. Gadia – International Journal of Technology in Education, 2025
Technology has contributed so much to the development and innovation of humankind. Artificial Intelligence (AI) is an off-shoot of such. This article explored the influence of AI anxiety on AI self-efficacy among college students. The investigators used a cross-sectional research design for 695 purposively chosen college students in one higher…
Descriptors: Anxiety, Artificial Intelligence, Self Efficacy, College Students
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Junxian Shen; Hongfeng Zhang; Jiansong Zheng – Psychology in the Schools, 2024
Online learning is becoming more and more common, so how to maintain learners' online learning engagement is very important. This study aims to explore the impact of future self-continuity on college students' online learning engagement and its underlying mechanism of action. We utilized the Future Self-Continuity Questionnaire, the Learning…
Descriptors: College Students, Learner Engagement, Electronic Learning, Predictor Variables
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Kirikkanat, Berke – International Journal for Educational and Vocational Guidance, 2023
Having effective career planning attitudes is a significant psychological resource for dealing with occupational burdens, unanticipated conflicts, and ambivalences in business area. The major purpose of the study was to reveal whether Turkish undergraduates' career planning attitudes were shaped by their trait emotional intelligence, cognitive…
Descriptors: Foreign Countries, Predictor Variables, Undergraduate Students, Student Attitudes
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Sadia Anwar; Ummi Naiemah Saraih – Journal of Applied Research in Higher Education, 2024
Purpose: Numerous studies have been conducted on psychological empowerment's effects on individual and organizational outcomes. This research study investigates the effects of emotional intelligence (EI) on psychological empowerment (PE) directly and indirectly through digital leadership (DL) in higher educational institutions (HEIs) in Pakistan.…
Descriptors: Emotional Intelligence, Psychological Patterns, Empowerment, Higher Education
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Alzubi, Emad Mohamad; Attiat, Madher Mohammad; Al-Adamat, Omar Atallah – Cypriot Journal of Educational Sciences, 2022
This study aimed to investigate the role of systemic intelligence factors in explaining cognitive flexibility and cognitive holding power among university students using measures of the aforementioned phenomena. A random sample of (519) students participated in this research, and it was found that factors relating to systemic intelligence could…
Descriptors: Intelligence, Predictor Variables, Cognitive Ability, College Students
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Patricia Everaert; Evelien Opdecam; Hans van der Heijden – Accounting Education, 2024
In this paper, we examine whether early warning signals from accounting courses (such as early engagement and early formative performance) are predictive of first-year progression outcomes, and whether this data is more predictive than personal data (such as gender and prior achievement). Using a machine learning approach, results from a sample of…
Descriptors: Accounting, Business Education, Artificial Intelligence, College Freshmen
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Marco Lünich; Birte Keller; Frank Marcinkowski – Technology, Knowledge and Learning, 2024
Artificial intelligence in higher education is becoming more prevalent as it promises improvements and acceleration of administrative processes concerning student support, aiming for increasing student success and graduation rates. For instance, Academic Performance Prediction (APP) provides individual feedback and serves as the foundation for…
Descriptors: Predictor Variables, Artificial Intelligence, Computer Software, Higher Education
Emily J. Barnes – ProQuest LLC, 2024
This quantitative study investigates the predictive power of machine learning (ML) models on degree completion among adult learners in higher education, emphasizing the enhancement of data-driven decision-making (DDDM). By analyzing three ML models - Random Forest, Gradient-Boosting machine (GBM), and CART Decision Tree - within a not-for-profit,…
Descriptors: Artificial Intelligence, Higher Education, Models, Prediction
Evette Lloyd Bridges – ProQuest LLC, 2023
The problem was that Historically Black Colleges/Universities (HBCU) stakeholders must observe ways that student support professionals increases organizational effectiveness, for there is a need to understand the correlation of emotional intelligence (EI) and job satisfaction. The purpose of this quantitative, causal-comparative study was to…
Descriptors: School Personnel, Black Colleges, Emotional Intelligence, Job Satisfaction
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Pursun, Tugba; Efilti, Erkan – European Journal of Educational Research, 2019
This study aims to analyse the emotional intelligence scores of the special education teacher candidates for the predictor of multiple intelligences areas. This study was conducted through relational scanning model. 211 teacher candidates, 106 females and 105 males, participated in the study. Data were collected through Personal Information Form,…
Descriptors: Emotional Intelligence, Preservice Teachers, Special Education Teachers, Multiple Intelligences
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Afef Saihi; Mohamed Ben-Daya; Moncer Hariga – Education and Information Technologies, 2025
The integration of AI-chatbots into higher education offers the potential to enhance learning practices. This research aims to explore the factors influencing AI-chatbots adoption within higher education, with a focus on the moderating roles of technological proficiency and academic discipline. Utilizing a survey-based approach and advanced…
Descriptors: Technology Uses in Education, Artificial Intelligence, Higher Education, Technology Integration
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Tony Robinson – Journal of Educational Technology, 2025
Generative artificial intelligence (AI) is increasingly transforming higher education by enhancing teaching methodologies, automating administrative tasks, and supporting research initiatives. Faculty adoption of generative AI is crucial for maximizing its potential benefits; however, its acceptance remains inconsistent due to factors such as…
Descriptors: Artificial Intelligence, Technology Uses in Education, Higher Education, Technology Integration
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Gallego, María Gómez; Perez de los Cobos, Alfonso Palazón; Gallego, Juan Cándido Gómez – Education Sciences, 2021
A main goal of the university institution should be to reduce the desertion of its students, in fact, the dropout rate constitutes a basic indicator in the accreditation processes of university centers. Thus, evaluating the cognitive functions and learning skills of students with an increased risk of academic failure can be useful for the adoption…
Descriptors: Identification, At Risk Students, Potential Dropouts, Cognitive Processes
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