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Xinhong Zhang; Xiangyu Wang; Jiayin Zhao; Boyan Zhang; Fan Zhang – IEEE Transactions on Education, 2024
Contribution: This study proposes a student dropout prediction model, named image convolutional and bi-directional temporal convolutional network (IC-BTCN), which makes dropout prediction for learners based on the learning clickstream data of students in massive open online courses (MOOCs) courses. Background: The MOOCs learning platform attracts…
Descriptors: MOOCs, Dropout Characteristics, Dropout Research, Predictor Variables
Helena Granziera; Rebecca J. Collie; Andrew J. Martin – Social Psychology of Education: An International Journal, 2024
Personal best (PB) goal setting refers to the pursuit of individual improvement through striving to outperform a previous best level of performance or effort. Although promising evidence has been building, numerous empirical questions remain to be answered, including how PB goal setting may operate alongside various contextual predictors of…
Descriptors: Goal Orientation, Teaching Styles, Homework, Student Improvement
Meg Kapil; Ramin Rostampour; Allyson Hadwin; Mariel Miller; Stuart W.S. MacDonald – Canadian Journal of Higher Education, 2025
The interplay between mental health and academic behaviours has been understudied. This study examined the relationship between foundational academic behaviours (e.g., attending class and meeting assignment deadlines), student mental health and well-being, and academic performance. Participants consisted of 229 students (52.6% female) who…
Descriptors: Mental Health, Student Behavior, Academic Achievement, Grade Point Average
Madison Lee Mason; Caleb Vatral; Clayton Cohn; Eduardo Davalos; Mary Ann Jessee; Gautam Biswas; Daniel T. Levin – Cognitive Research: Principles and Implications, 2025
Although the "eye-mind link" hypothesis posits that eye movements provide a direct window into cognitive processing, linking eye movements to specific cognitions in real-world settings remains challenging. This challenge may arise because gaze metrics such as fixation duration, pupil size, and saccade amplitude are often aggregated…
Descriptors: Eye Movements, Nursing Students, Student Experience, Nursing Education
Valentine Joseph Owan; Ibrahim Abba Mohammed; Ahmed Bello; Tajudeen Ahmed Shittu – Contemporary Educational Technology, 2025
Despite the increasing interest in artificial intelligence technologies in education, there is a gap in understanding the factors influencing the adoption of ChatGPT among Nigerian higher education students. Research has not comprehensively explored these factors in the Nigerian context, leaving a significant gap in understanding technology…
Descriptors: Student Behavior, Foreign Countries, Artificial Intelligence, Natural Language Processing
Yangqian Wang; Gang Cheng; Xianhong Zhou; Haili Yang; Niuniu Fu; Fangyuan Ding – Psychology in the Schools, 2024
The present study examined the relationship between adult attachment and subjective social status (SSS) in college students through a cross-sectional survey (Study 1) and a longitudinal study (Study 2). In Study 1, 1300 college students were recruited via research flyers and online campus advertisements. They completed measures of adult attachment…
Descriptors: Attachment Behavior, Social Status, Anxiety, Psychological Patterns
Diana M. Doumas; Raissa Miller; Susan Esp – Psychology in the Schools, 2025
Sensation seeking is a significant predictor of alcohol-related consequences among high school students. High school seniors report the highest prevalence rates of alcohol use and binge drinking among students in high school. Among high sensation-seeking students, higher rates of Protective Behavioral Strategies (PBS) use are associated with fewer…
Descriptors: Risk, Predictor Variables, Drinking, High School Seniors
Yoon Lee; Gosia Migut; Marcus Specht – British Journal of Educational Technology, 2025
Learner behaviours often provide critical clues about learners' cognitive processes. However, the capacity of human intelligence to comprehend and intervene in learners' cognitive processes is often constrained by the subjective nature of human evaluation and the challenges of maintaining consistency and scalability. The recent widespread AI…
Descriptors: Artificial Intelligence, Cognitive Processes, Student Behavior, Cues
Juan Andrés Talamás-Carvajal; Héctor G. Ceballos; Isabel Hilliger – Journal of Learning Analytics, 2025
Artificial intelligence (AI) is currently leading an industrial revolution in most aspects of human life, and education is no exception. With the increasing ratio of students to faculty, AI could be an extremely beneficial tool for individual mentoring; for example, for cases of dropout and for student retention. While many models have already…
Descriptors: Higher Education, Artificial Intelligence, Research Methodology, Student Subcultures
Camille Guimond; Stéphanie Boutin; Michèle Déry; Caroline Temcheff – School Mental Health, 2025
While direct aggression negatively affects teacher-student relationships, the association with indirect aggression (IA), a form of aggression used to damage victims' interpersonal relationships, is understudied and could vary due to its covert nature. Furthermore, the directionality of the association (i.e., whether students' behaviors affect the…
Descriptors: Aggression, Teacher Student Relationship, Victims, Student Behavior
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
Xiao Wen; Hu Juan – Interactive Learning Environments, 2024
To address three issues identified in previous research this study proposes a clustering-based MOOC dropout identification method and an early prediction model based on deep learning. The MOOC learning behavior of self-paced students was analyzed, and two well-known MOOC datasets were used for analysis and validation. The findings are as follows:…
Descriptors: MOOCs, Dropouts, Dropout Characteristics, Dropout Research
Mohamad Saifudin Mohamad Saleh; Ali Mehellou; Miao Huang; Rizki Briandana – Journal of Applied Research in Higher Education, 2025
Purpose: The use of social media for sustainable information is important since it has the potential to influence people's intentions and behaviour towards sustainability. As previous studies on social media and sustainable development have primarily focussed on Western viewpoints, this study presents a comprehensive Asian perspective by…
Descriptors: Social Media, Information Dissemination, Sustainability, Foreign Countries
Ilhan Ilter; Gökhan Izgar; Mensure Alkis Küçükaydin – European Journal of Psychology of Education, 2025
This study, grounded in expectancy-value theory, examined the relationships between self-efficacy, effort, and value beliefs within the context of social studies. For this purpose, the study tested the mediating and moderating roles of the value attributed to the social studies course in the relationship between self-efficacy and effort in the…
Descriptors: Middle School Students, Self Efficacy, Social Studies, Student Attitudes
Elena C. Papanastasiou; Michalis P. Michaelides – Large-scale Assessments in Education, 2024
Test-taking behavior is a potential source of construct irrelevant variance for test scores in international large-scale assessments where test-taking effort, motivation, and behaviors in general tend to be confounded with test scores. In an attempt to disentangle this relationship and gain further insight into examinees' test-taking processes,…
Descriptors: Grade 4, Testing, Student Behavior, Test Wiseness

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