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Hengtao Tang; Yeye Tang; Miao Dai; Xu Du; Jui-Long Hung; Hao Li – TechTrends: Linking Research and Practice to Improve Learning, 2024
Blended learning, integrating online and in-person components, has been increasingly adopted in higher education to enhance students' learning experience and outcomes. While the advantages of blended learning are well-evidenced, research has primarily focused on the online pre-learning component, neglecting the significance of in-class activities.…
Descriptors: Blended Learning, Behavior Patterns, Learning Processes, Learning
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Esteban Villalobos; Isabel Hilliger; Carlos Gonzalez; Sergio Celis; Mar Pérez-Sanagustín; Julien Broisin – Journal of Learning Analytics, 2024
Researchers in learning analytics have created indicators with learners' trace data as a proxy for studying learner behaviour in a college course. Student Approaches to Learning (SAL) is one of the theories used to explain these behaviours, distinguishing between deep, surface, and organized study. In Latin America, researchers have demonstrated…
Descriptors: Learning Analytics, Academic Achievement, Role Theory, Learning Processes
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Li, Xiaoyin; Clarke, Nickeisha; Kim, Su-Young; Ray, Anne E.; Walters, Scott T.; Mun, Eun-Young – Journal of American College Health, 2022
Objective: To examine race, gender, and alcohol use level as moderators of the association between protective behavioral strategies (PBS) and alcohol-related problems. Participants: A sample of 12,011 participants who reported recent drinking (87.7% White, 61% Women) from Project INTEGRATE, a study that combined individual participant data (IPD)…
Descriptors: Alcohol Abuse, Drinking, Student Behavior, Race
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Liu, Sannyuya; Kang, Lingyun; Liu, Zhi; Fang, Jing; Yang, Zongkai; Sun, Jianwen; Wang, Meiyi; Hu, Mengwei – Interactive Learning Environments, 2023
Computer-supported collaborative concept mapping (CSCCM) integrates technology and concept mapping to support students' knowledge understanding, and much research on the behavioral patterns involved in CSCCM activities has been conducted. However, there is limited understanding of the differences in knowledge understanding and behavioral patterns…
Descriptors: Computer Assisted Instruction, Concept Mapping, Student Attitudes, College Students
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Yangyang Luo; Xibin Han; Chaoyang Zhang – Asia Pacific Education Review, 2024
Learning outcomes can be predicted with machine learning algorithms that assess students' online behavior data. However, there have been few generalized predictive models for a large number of blended courses in different disciplines and in different cohorts. In this study, we examined learning outcomes in terms of learning data in all of the…
Descriptors: Prediction, Learning Management Systems, Blended Learning, Classification
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Kasakowskij, Regina; Haake, Joerg M.; Seidel, Niels – International Educational Data Mining Society, 2023
Improving competence requires practicing, e.g. by solving tasks. The Self-Assessment task type is a new form of scalable online task providing immediate feedback, sample solution and iterative improvement within the newly developed SAFRAN plugin. Effective learning not only requires suitable tasks but also their meaningful usage within the…
Descriptors: Self Evaluation (Individuals), Student Behavior, College Students, Learning Processes
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Tong Li; Chris Kirk; Leticia Oseguera – Journal of College Student Development, 2023
Academic achievement, often measured by GPA, has been extensively studied in the literature of science, technology, engineering, and mathematics (STEM) education, including its impact on student persistence and success in college (Rask, 2010). However, most research has only looked at students' performance at a single point in time, such as their…
Descriptors: STEM Education, Intention, College Students, Longitudinal Studies
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Iustina Alexandra Groza; Marius Ciprian Ceobanu; Cristina Maria Tofan – European Journal of Psychology of Education, 2024
Academic procrastination has been a subject of particular interest in research due to its frequent association with heightened levels of anxiety, stress, and the long-term risk of emotional and behavioural vulnerability (Hoge et al., 2013). Our study tests the correlation between motivational persistence as a trait and academic procrastination, as…
Descriptors: Study Habits, Females, Foreign Countries, Student Motivation
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Mohd Fazil; Angelica Rísquez; Claire Halpin – Journal of Learning Analytics, 2024
Technology-enhanced learning supported by virtual learning environments (VLEs) facilitates tutors and students. VLE platforms contain a wealth of information that can be used to mine insight regarding students' learning behaviour and relationships between behaviour and academic performance, as well as to model data-driven decision-making. This…
Descriptors: Learning Analytics, Learning Management Systems, Learning Processes, Decision Making
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Zhan, Zehui; Wu, Qianyi; Lin, Zhihua; Cai, Jiayi – Australasian Journal of Educational Technology, 2021
This study investigated the effect of classroom settings on teacher-student interaction in higher education by comparing the behavioural sequences in smart classrooms (SCs) and traditional multimedia classrooms (TMCs). Twenty in-classroom teaching sessions were randomly selected from six universities in South China, involving 1,043 students and 23…
Descriptors: Technology Uses in Education, Classroom Environment, Teacher Student Relationship, Behavior Patterns
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Lars de Vreugd; Anouschka van Leeuwen; Renée Jansen; Marieke van der Schaaf – Journal of Learning Analytics, 2024
For university students, self-regulation of study behaviour is important. However, students are not always capable of effective self-regulation. Providing study behaviour information via a learning analytics dashboard (LAD) may support phases within self-regulated learning (SRL). However, it is unclear what information a LAD should provide, how to…
Descriptors: Learning Management Systems, Learning Analytics, Student Behavior, Behavior Patterns
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Li, Yue; Jiang, Qiang; Xiong, Weiyan; Zhao, Wei – Education and Information Technologies, 2023
One of the recognized ways to enhance teaching and learning is having insights into the behavior patterns of students. Studies that explore behavior patterns in online self-directed learning (OSDL) are scant though. In addition, the focus is lacking on how high-achieving (HA) students' behavior patterns affect the academic performance of…
Descriptors: Student Behavior, Behavior Patterns, Electronic Learning, Online Courses
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Balti, Rihab; Hedhili, Aroua; Chaari, Wided Lejouad; Abed, Mourad – Education and Information Technologies, 2023
Since the COVID pandemic, universities propose online education to ensure learning continuity. However, the insufficient preparation led to a major drop in the learner's performance and his/her dissatisfaction with the learning experience. This may be due to several reasons, including the insensitivity of the virtual learning environment to the…
Descriptors: Cognitive Style, Pandemics, COVID-19, Distance Education
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Ives, Bob; Giukin, Lenuta – Journal of Academic Ethics, 2020
A total of 1390 university students from five public Moldovan universities completed a survey reporting their experiences and beliefs with respect to 22 types of academic misconduct. An interpretable five-factor solution to the frequencies of these behaviors accounted for more than half of the total variance. The two most reliable predictors were…
Descriptors: Foreign Countries, Behavior Patterns, Predictor Variables, Public Colleges
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Jeynes, William H. – Education and Urban Society, 2022
The meta-analysis, that included 75 studies, examined the relationship between illegal drug consumption, on the one hand, and student academic and behavioral outcomes, on the other, for the middle school to college grade levels. The meta-analysis first (research question #1) addressed whether there is a statistically significant relationship…
Descriptors: Middle School Students, High School Students, College Students, Drug Use
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