Impact of intrapersonal and interpersonal emotional intelligence and self-directed learning on academic performance among pre-university science students

A classification system based on improved global exploration and convergence to examine student psychological fitness

Anxiety is an important issue that affects their academic performance, mental health, and overall educational journey. To address this issue, it is important to accurately assess anxiety levels and provide evidence-based techniques. However, due to the complexity of anxiety and individual differences, analyzing clustering algorithms to efficiently classify psychological levels is challenging. Traditional clustering techniques face certain challenges in accurately classifying anxiety levels, such as slow convergence, sensitivity to initial conditions, and difficulties in handling constraints. To address these issues, clustering with an improved Mayfly-based optimization algorithm (IMOA) is proposed based on the dynamic variable for better performance to classify psychological levels. Initially, IMOA is validated using 23 standard benchmark functions, confirming its ability to find optimal solutions. Then, IMOA is applied to the student dataset, classifying them into Cluster A and Cluster B. The average scores for both clusters across all test cases are 76.7% and 53.07%, respectively. These results demonstrate the formation of dissimilar student groups with homogeneous emotions and performance, highlighting the importance of addressing emotional stress. Finally, by assigning students to clusters, educators and mental health professionals can better support those who may struggle, ensuring they receive the attention and resources they need. The obtained results show that IMOA with a dynamic variable effectively classifies student anxiety, improving the learning environment and helping teachers better understand students' needs. This identification allows them to provide more effective support and adapt their teaching to meet the specific needs of those seeking support.

Impact of emotional intelligence and academic self-concept on the academic performance of educational sciences undergraduates

Over the last few years, the inclusion of psychosocial factors in the teaching and learning processes has become increasingly important due to their proven influence on students' academic performance, especially at the university stage. In this regard, the aim of this study is to analyse the impact of emotional intelligence and academic self-concept on the students' academic achievement. The results obtained revealed some differences according to gender in all the variables considered. Specifically, women presented higher levels of emotional attention, academic self-concept and performance, while men stood out in emotional clarity and emotional repair. The findings obtained show the importance of including psychosocial factors in university training plans.

Fuzzy clustering algorithm for university students' psychological fitness and performance detection

Students' psychological fitness is unavoidable, hindering personal development, social interactions, peer influence, and adolescence. Academic stress may be the most dominant factor affecting college students' mental well-being. Therefore, improving the monitoring of mental health issues among college students is a vital topic for study. However, identifying the student's stress level is challenging, leading to uncertainty. Hence, this paper suggests Heuristic Fuzzy C-means Clustering Algorithm (HFCA) for analyzing college students' stress levels, psychological well-being and academic performance detection. The data are collected from the Kaggle stress dataset for predicting student mental health. This study investigates the psychological factors affecting students' academic performance using the suggested HFCA. Students' performance may be predicted using the Fuzzy Cognitive Map (FCM) in this study. This study used fuzzy clustering algorithms to discover the most crucial aspects of student success, such as student involvement and satisfaction. A better understanding of the risk factors for and protective factors against poor mental health can serve as the basis for developing policies and targeted interventions to prevent mental health problems and guarantee that at-risk students can access the help they need. The experimental analysis shows the proposed method HFCA to achieve a high student performance ratio of 96.7%, cognitive development ratio of 97.2%, student engagement ratio of 97.5% and prediction ratio of 95.1% compared to other methods.

How social media and peer learning influence student-teacher self-directed learning in an online world under the 'New Normal'

The study aimed to investigate three aspects of Thai student-teacher self-directed learning (SDL) competency. These were the student-teachers opinions concerning their use of social media (SM), self-management (SM), and learning desire (LD). The sample group was 468 student-teachers enrolled in a Bachelor of Industrial Education Program at the King Mongkut's Institute of Technology Ladkrabang in Bangkok, Thailand, in the Academic Year 2021. The research instrument consisted of an SDL competency questionnaire whose discrimination (corrected item-total correlation) values were determined to be between 0.37 and 0.69, which also had a confidence level of 0.91. Data analysis used LISREL 9.10 for the study's second-order confirmatory factor analysis (CFA). Descriptive statistics analysis included the mean and standard deviation (SD), which was accomplished using IBM's® SPSS® for Windows Version 21. Three models were developed for the study. These included the social media (SM) model containing 285 participants, the peer learning (PL) model, which contained 183 participants, and the total group (TG) model, which contained everyone surveyed (n = 468). The final analysis from the second-order CFAs showed that student-teacher SDL competency for self-control (SC) (0.96) was valued most by the student-teachers. However, their learning desire (LD) (0.87) and self-management (SM) (0.80) skills were somewhat behind. Moreover, in the Pearson Product Moment Correlation (PPMC) (r) analysis of the 24 variable relationships, the strongest was related to each student-teacher's learning desire. However, the weakest variable relationship was related to their ability to set high personal standards and the self-discipline to achieve them. Finally, quite interestingly, 60.90% of the student-teachers indicated that their SDL is gotten from social media (SM) resources compared to learning from their peers (PL) around them.

Examining pre-service teachers regulation in distance and traditional preschool design and technology education

The significance of adapting to a rapidly changing world is quite evident in the current day; thus, the awareness of how to teach students so that they can be ready to face challenges in the future is very important. Early education has a huge impact on the further development of children, so preschool teachers must be competent and use appropriate teaching and educational methods. In this study, the development of self-directed learning (SDL) of future preschool teachers is investigated by considering two variables, namely the type of study (full-time and part-time students) and the learning modalities caused by the COVID-19 pandemic (pre-, during and post-COVID-19 confinement). We collected data from 418 participants and analysed them using descriptive statistics, 2 × 3 factorial analysis of variance (ANOVA) and a two-step cluster analysis. The results show the status of pre-service preschool teachers' perceptions of their SDL development and how the variables influenced it. There were significant differences in the students' self-reported SDL skills, depending on the learning environment and the type of study. The status indicator helps educators identify and change the curriculum and how they work with students. It allows the faculty to highlight the positive aspects of the different educational modalities encountered, as well as the characteristics of the study types and their impact on the learning process to improve students' SDL skills. The results of the study may help in the design of tailored metacognitive scaffolds that take into account different modalities. Further studies are needed to investigate the effectiveness of digital open learning environments that address both SDL and preschool educational practices.

Types of Intelligence and Academic Performance: A Systematic Review and Meta-Analysis

The concept of intelligence has been extensively studied, undergoing an evolution from a unitary concept to a more elaborate and complex multidimensional one. In addition, several research studies have focused their efforts for decades on the study of intelligence as a predictor of academic performance of students at different educational stages, being a stable and highly relevant predictor along with other variables such as executive functions, social context, culture or parental guardianship. Thus, the present study, based on a systematic review and meta-analysis, includes 27 studies with a total sample of 42,061 individuals. The main objective was to analyse the relationship between intelligence and academic performance using different predictive models that include moderating variables such as country of origin, type of intelligence, gender and age. The findings of this research highlight the significant, positive and moderate relationship between intelligence and academic performance (r = 0.367; p < 0.001), highlighting the predictive capacity on school performance when the type of intelligence (general and implicit; 35%) or the country of origin (45%) is taken as a moderating variable, with the explanatory models on age or sex not being significant. Therefore, it can be concluded that intelligence, in addition to being a good predictor of academic performance, is influenced depending on the type of intelligence or theoretical model taken as a reference, and also depending on the country or culture of origin.

Contribution of perceived faculty caring (FC) and student engagement (SE) to lifelong learning (LLL) of post-secondary remediated (PSR) science students

Literature is unequivocal about the relevance of promoting lifelong learning (LLL) intentions among adult learners. However, what is less certain in remedial education literature is how faculty members play a critical role in motivating the tendencies for LLL among remediated science students, especially in the developing countries. Therefore, this study investigated the contributions of faculty caring and student engagement to remediated science students' perceived LLL tendencies. Correlational research design was used to measure and gauge the level of the relationships amongst the studying variables. A total of 443 continuing education programme students in Nigeria participated in the study. By using AMOS v. 24 and SPSS v. 26 statistical tools for data analyses, we found a high level of student-perceived faculty caring, student engagement components and LLL tendencies. Multilevel regression analyses indicated that the dimensions of students' LLL tendencies (motivation and perseverance) were positively predicted by faculty caring and student engagement dimensions, such as vigour, absorption and dedication. In the final models, the predictor variables could explain some substantive proportions of motivation and perseverance dimensions of LLL tendencies. Our study findings reveal that faculty caring plays a significant role in motivating students' academic engagement and the tendencies for LLL in higher education. Therefore, educational intervention that gears towards improving student academic engagement has a practical implication in enhancing LLL tendencies amongst higher education science students. Hence, the study findings could inspire various educational practitioners to encourage effective academic engagement amongst higher education science students. Directions for further research were suggested.