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Eurasian Society of Educational Research
Eurasian Society of Educational Research
Christiaan Huygensstraat 44, Zipcode:7533XB, Enschede, THE NETHERLANDS
Eurasian Society of Educational Research
Headquarters
Christiaan Huygensstraat 44, Zipcode:7533XB, Enschede, THE NETHERLANDS

'Agriculture' Search Results

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Psychological capital (PsyCap) emerges as a pivotal asset for mitigating student burnout in college settings, as it bolsters their learning empowerment and engagement. However, there have been inadequate empirical studies investigating the significance of these resources in promoting engagement and empowerment, ultimately leading to a reduction in students’ burnout within the context of higher education. To bridge this gap, we examined the extent to which PsyCap predicts student burnout through its impacts on academic engagement and learner empowerment. The sample of the study was college students (N = 562) who completed a battery of self-report questionnaires measuring their PsyCap, academic engagement, learner empowerment, and student burnout. We employed hierarchical multiple regression analyses and PROCESS macro to ascertain prediction and serial mediation effects. The results substantiated the hypotheses that PsyCap positively related to learner empowerment and academic engagement while negatively associated with student burnout. Further, students with higher levels of learner empowerment and engagement reported lower levels of burnout in their academic studies. The mediational results also revealed that engagement and learner empowerment acted as significant serial mediators between PsyCap and student burnout. The study’s findings underscore the critical significance of PsyCap within higher education, particularly in nurturing learner empowerment, and engagement, thereby reducing student burnout.

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10.12973/eu-jer.13.4.1933
Pages: 1933-1949
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This complex personality trait serves as the main topic of our paper due to the increasing prevalence of perfectionism as well as the rising demands from educational organizations. Our paper can fill a research gap by examining its definitions, models, components, and influencing elements (personality, gender, and immediate environment) in addition to the role of perfectionism in secondary and tertiary education. We assume that perfectionism in higher education is based on its development at secondary school, and it is becoming more intense in time. In 2023 the authors conducted a survey among Hungarian university students to determine the degree to which the participants pursue perfectionism in their professional and personal lives. The questionnaire finally resulted in 550 responses. The findings of our research suggest that women tend to be more perfectionist, but the picture is differently deemed by individuals than by their immediate surroundings. Another noteworthy result revealed that personal perfectionism also depends on the people with whom those who consider themselves perfectionists live. Our SEM model also showed that perfectionism is stronger throughout university studies and that it might be descended from secondary school perfectionism. Personality traits do affect perfectionism at school, which intensifies in higher education after graduating from secondary school.

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10.12973/eu-jer.14.1.1
Pages: 1-21
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Artificial intelligence (AI) has revolutionized higher education. The rapid adoption of artificial intelligence in education (AIED) tools has significantly transformed educational management, specifically in self-directed learning (SDL). This study examines the factors influencing Indonesian higher education students' intention to adopt AIED tools for self-directed learning using a combination of the Theory of Planned Behavior (TPB) with additional theories. A total of 322 university students from diverse academic backgrounds participated in the structured survey. This study utilized machine learning it was Artificial Neural Networks (ANN) to analyze nine factors, including attitude (AT), subjective norms (SN), perceived behavioral control (PBC), optimism (OP), user innovativeness (UI), perceived usefulness (PUF), facilitating conditions (FC), perception towards ai (PTA), and intention (IT) with a total of 41 items in the questionnaire. The model demonstrated high predictive accuracy, with SN emerging as the most significant factor to IT, followed by AT, PBC, PUF, FC, OP, and PTA. User innovativeness was the least influential factor due to the lowest accuracy. This study provides actionable insights for educators, policymakers, and technology developers by highlighting the critical roles of social influence, supportive infrastructure, and student beliefs in shaping AIED adoption for self-directed learning (SDL). This research not only fills an important gap in the literature but also offers a roadmap for designing inclusive, student-centered AI learning environments that empower learners and support the future of SDL in digital education.

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10.12973/eu-jer.14.3.805
Pages: 805-828
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