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Miao Chao; Weiyi Sun; Jie Liu; Jiahui Ding; Ye Zhu – Journal of Computer Assisted Learning, 2025
Background: The use of social media among students has become debatable concern due to both positive and negative effects on academic performance. Yet, understanding of the diverse patterns of social media use and their influence on actual and perceived academic performance remains limited. Objectives: This study distinguishes between academic and…
Descriptors: Social Media, Performance, Influence of Technology, Predictor Variables
Chih-Hung Wang; Fu-Yuan Hong; Tzu-Yuan Hsu – Psychology in the Schools, 2025
Previous studies have explored the relationship between students' school stress and resilience but have largely overlooked the relationship between students' daily academic hassles and buoyancy. Although the close relationship between academic buoyancy and achievement has been confirmed, using the academic buoyancy perspective to explain the…
Descriptors: Foreign Countries, Stress Variables, Barriers, Mental Health
Relationship between Students' Academic Self-Concept, Intrinsic Motivation, and Academic Performance
Seth Sunu; David Baidoo-Anu – International Journal of School & Educational Psychology, 2024
This study examined the relationship between academic self-concept, intrinsic motivation, and academic performance of Senior High School students. A quantitative approach using a descriptive cross-sectional survey design was used in the study. A multi-stage sampling technique was used to select 346 participants. The results showed that students…
Descriptors: High School Students, Self Concept, Student Motivation, Academic Achievement
Nihan Arslan; Serhat Arslan; Asli Kartol; Mehmet Akcaalan – International Journal of Learning and Change, 2024
This research aims to examine the relationship between science motivation and social emotional learning. The relational analysis method was used in the research within the scope of quantitative research. The research scopes the relationship between high school students' science motivation and social emotional learning. According to the results, a…
Descriptors: High School Students, Social Emotional Learning, Predictor Variables, Foreign Countries
Inuusah Mahama; Isaac Amoako; Aliu Nandzo; Peter Eshun – Cogent Education, 2024
In every learning environment, several factors such as mindfulness, self-regulation, school engagement, and resilience may compel students to move beyond their limits to sustain learning and improve their academic performance. The study, therefore, investigated the predictive roles of academic mindfulness, self-regulated learning, and school…
Descriptors: Metacognition, Independent Study, Learner Engagement, Predictor Variables
Lee, Hyejeong; Closser, Florentina; Alghamdi, Khadijah; Ottenbreit-Leftwich, Anne; Brown, Matthew; Koressel, Jacob – TechTrends: Linking Research and Practice to Improve Learning, 2023
This study aims to examine the current experiences of high school students in computer science (CS) courses and the factors that motivated them to continue their future enrollment. The participants were 603 high school students in grades 9 through 12 in Indiana, all of whom enrolled in at least one CS course during the 2020-2021 academic year.…
Descriptors: Student Experience, Predictor Variables, Enrollment, Computer Science Education
Teng Chen; Wan Ding; Qingfeng Yang; Yanling Chen; Weijian Li; Ruibo Xie – Social Psychology of Education: An International Journal, 2025
Prior studies showed that general basic psychological need satisfaction or social support affected academic engagement. However, it remains unknown how the mutual reinforcement between the perception of internal needs and external support influences adolescents' academic engagement, as well as whether academic engagement can shape psychological…
Descriptors: Social Support Groups, Need Gratification, High School Students, Learner Engagement
John Ranellucci; Joshua M. Rosenberg – Educational Psychology, 2024
Though documented extensively using self-report measures - especially in post-secondary course contexts - the relations among students' interest, engagement, and academic achievement in high school science courses has been studied far less - especially when considering online versions of these courses. Self-reported interest collected at the…
Descriptors: STEM Education, Distance Education, High School Students, Science Interests
Lauren Gibson; Kathryn Stevenson; K. C. Busch; Bethany Cutts; Erin Seekamp; Sarah Krementz – Journal of Environmental Education, 2024
Environmental education often advocates for individual pro-environmental behavior--which, while beneficial to a degree, fails to match the large scale of today's capitalism-fueled socio-ecological challenges. Rather, collective action holds promise as a means for the large-scale changes required in the Capitalocene. These actions can take the…
Descriptors: Climate, Predictor Variables, High School Students, Student Behavior
Tzu-Hua Huang – Asia Pacific Education Review, 2024
It is essential that students develop the skill of reading comprehension during their schooling, although it has been defined in various ways. The Programme for International Student Assessment indicates that a substantial proportion of students fail to develop sufficient reading skills during their schooling. While it is easy to understand the…
Descriptors: Foreign Countries, High School Students, Indigenous Populations, Reading Comprehension
Lisa Meerts-Brandsma; Bryn Spielvogel; Hilary Lambert; Jim Sibthorp – Journal of Experiential Education, 2024
Background: Practitioners want to create meaningful experiences for youth because they provide positive benefits. Immersion semester high schools are likely to provide meaningful experiences. Purpose: This study sought to identify what types of activities are most meaningful to youth, whether activities are more meaningful in immersion semester…
Descriptors: Experiential Learning, High School Students, Learning Activities, Psychological Patterns
Samantha M. van Rens; Cristina Lemelin; Patricia H. Kloosterman; Laura J. Summerfeldt; James D. A. Parker – Canadian Journal of School Psychology, 2025
Although previous research has found trait emotional intelligence (TEI) to be a moderate predictor of bullying behaviors in adolescents, this work has limited generalizability. The current study is the first to use a multidimensional approach to both TEI and bullying behaviors when looking at their relationship in high school students. The study…
Descriptors: Bullying, High School Students, Emotional Intelligence, Predictor Variables
Maisyaroh; Juharyanto; Maulana Amirul Adha; Abdullah Mohd Nawi – International Journal of Educational Methodology, 2024
Increasing the number of entrepreneurs is not just a school task, it requires joint work between educational institutions and the community. Using structural equation modeling, this research aims to investigate the role of community in promoting entrepreneurial careers for students, by adding achievement motivation, entrepreneurial attitudes, and…
Descriptors: Entrepreneurship, Career Choice, Community Support, High School Students
Arlene Bjugstad; Jodi Berger Cardoso; Tzuan A. Chen; Kalina M. Brabeck; Sharon Borja – Psychology in the Schools, 2024
Using a multidimensional, integrative approach this study examined the influence of social position factors (nativity, economic hardship, and language) and environmental contexts (community trauma, geographic location, and discrimination) on three domains of school engagement (cognitive, behavioral, and relational) among a community sample of…
Descriptors: Social Influences, Environmental Influences, Learner Engagement, Predictor Variables
Kurtyilmaz, Yildiz; Ergün-Basak, Bircan – International Journal of Contemporary Educational Research, 2023
School dropout is a devastating problem leading to negative consequences not only for individuals but also society. Therefore, numerous preventive measures and interventions have been implemented, but the expected outcomes have not been obtained despite vigorous efforts. This may indicate that the dropout process is complicated and not limited to…
Descriptors: Dropouts, Predictor Variables, Self Control, Self Efficacy