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Akram, Aftab; Chengzhou, Fu; Lin, Ronghua; Arooj, Ansif; Chengzhe, Yuan; Yuncheng, Jiang; Yong, Tang – Technology, Pedagogy and Education, 2021
Students using a Learning Management System (LMS) as a learning support have been observed to demonstrate different learning behaviours. Studies have reported students exhibiting different procrastination tendencies, distinct social behaviours and system usage patterns. Students can be clustered together based on similarity in their learning…
Descriptors: Student Behavior, Integrated Learning Systems, Multivariate Analysis, Interaction
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Avci, Ümmühan; Ergün, Esin – Interactive Learning Environments, 2022
The purpose of this study was to examine online students' LMS activities and the effect on their engagement, information literacy, and academic performance. The participants of the study were 65 undergraduate students enrolled to an online "Computer Literacy" course. Cluster analysis was performed on the log data gathered from LMS…
Descriptors: Electronic Learning, Distance Education, Integrated Learning Systems, Learning Activities
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Duncan, Markus J.; Patte, Karen A.; Leatherdale, Scott T. – Canadian Journal of School Psychology, 2021
Course grades, as an indicator of academic performance, are a primary academic concern at the secondary school level and have been associated with various aspects of mental health status. The purpose of this study is to simultaneously assess whether symptoms of mental illness (depression and anxiety) and mental well-being (psychosocial well-being)…
Descriptors: Mental Health, Predictor Variables, Academic Achievement, Student Behavior
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van den Beemt, Antoine; Buys, Joos; van der Aalst, Wil – International Review of Research in Open and Distributed Learning, 2018
The increasing use of digital systems to support learning leads to a growth in data regarding both learning processes and related contexts. Learning Analytics offers critical insights from these data, through an innovative combination of tools and techniques. In this paper, we explore students' activities in a MOOC from the perspective of personal…
Descriptors: Online Courses, Student Behavior, Behavior Patterns, Academic Achievement
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Hensley, Lauren C.; Wolters, Christopher A.; Won, Sungjun; Brady, Anna C. – Journal of College Reading and Learning, 2018
Effective time management often undergirds students' success in college, and many postsecondary learning centers offer services to help students assess and improve this aspect of their learning skills. In the context of a college success course, we gathered insights from assignments to consider various facets of students' time-related behaviors…
Descriptors: Time Management, Academic Achievement, Academic Probation, Assignments
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Stimpson, Matthew T.; Janosik, Steven M. – Journal of College Student Development, 2015
In this study, 7 items were used to define a composite variable that measures the perceived effectiveness of student conduct systems. Multivariate Analysis of Variance (MANOVA) was used to test the relationship between perceived level of system effectiveness and self-reported student learning. In the analyses, 49% of the variance in reported…
Descriptors: Student Behavior, Academic Achievement, Correlation, Multivariate Analysis
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Sulak, Tracey N. – Educational Studies, 2018
A positive school climate impacts students by promoting positive relations among students, staff and faculty of the school. The current study used latent class analysis and multinomial regression with R3STEP to analyse patterns of negative behaviours in schools and test the association of these patterns with structural variables like school size,…
Descriptors: Educational Environment, Regression (Statistics), Bullying, Student Behavior
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Wright, Kim B.; Shields, Samantha M.; Black, Katie; Waxman, Hersh C. – School Community Journal, 2018
This study's purpose was to determine if a teacher home visit program implemented by a Texas-based charter school system resulted in differences in K-12 students' classroom behavior, academic achievement, and parent involvement in school. Study findings indicate positive behavioral, academic, and parent involvement outcomes for students who…
Descriptors: Student Behavior, Academic Achievement, Parent Participation, Charter Schools
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Mindrila, Diana L. – Psychology in the Schools, 2016
To describe and facilitate the identification of child school behavior patterns, we developed a typology of child school behavior (ages 6-11 years) using the norming data (N = 2,338) for the second edition of the Behavior Assessment System for Children Teacher Rating-Child form). Latent profile analysis was conducted with the entire data set,…
Descriptors: Classification, Student Behavior, Statistical Analysis, Multivariate Analysis
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Janosik, Steven M.; Stimpson, Matthew T. – Journal of Student Affairs Research and Practice, 2017
Researchers have demonstrated the influence of the perceived efficacy of a conduct system on student learning (King, 2012; Stimpson & Janosik, 2015). Multivariate Analysis of Variance (MANOVA) was used to test the relationship between perceived level of conduct system efficacy, institutional culture, and self-reported student learning. More…
Descriptors: College Environment, Campuses, Multivariate Analysis, Organizational Culture
Özeke, Vildan; Akçapina, Gökhan – International Association for Development of the Information Society, 2016
There are many computer games, learning environments, online tutoring systems or computerized tools which keeps the track of the user while learning or engaging in the activities. This paper presents results from an exploratory study and aims to group students regarding their behavior data while solving the Einstein's riddle. 45 undergraduate…
Descriptors: Puzzles, Educational Games, Humor, Foreign Countries
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Kahan, Tali; Soffer, Tal; Nachmias, Rafi – International Review of Research in Open and Distributed Learning, 2017
In recent years there has been a proliferation of massive open online courses (MOOCs), which provide unprecedented opportunities for lifelong learning. Registrants approach these courses with a variety of motivations for participation. Characterizing the different types of participation in MOOCs is fundamental in order to be able to better…
Descriptors: College Students, Student Behavior, Online Courses, Large Group Instruction
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Kelchner, Viki P.; Evans, Kathy; Brendell, Kathrene; Allen, Danielle; Miller, Cassandre; Cooper-Haber, Karen – Professional Counselor, 2017
This investigation examined the potential impact of a school-based youth intervention program on the attitudes and behavioral patterns of at-risk youth. The sample size used in this study was 52; 24 participants received the school-based intervention and 28 participants did not receive the intervention. A two-group pretest-posttest design approach…
Descriptors: Youth, Nontraditional Education, Student Attitudes, At Risk Students
Agnihotri, Lalitha; Aghababyan, Ani; Mojarad, Shirin; Riedesel, Mark; Essa, Alfred – International Educational Data Mining Society, 2015
Student login data is a key resource for gaining insight into their learning experience. However, the scale and the complexity of this data necessitate a thorough exploration to identify potential actionable insights, thus rendering it less valuable compared to student achievement data. To compensate for the underestimation of login data…
Descriptors: Data Analysis, Web Based Instruction, Student Behavior, Correlation
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Han, Seunghee – Educational Research for Policy and Practice, 2014
This study examined the effects of corporal punishment on student outcomes in rural schools by analyzing 1,067 samples from the School Survey on Crime and Safety 2007-2008. Results of descriptive statistics and multivariate regression analyses indicated that schools with corporal punishment may decrease students' violent behaviors and…
Descriptors: Punishment, Outcomes of Education, Academic Achievement, School Surveys
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