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A. N. Varnavsky – IEEE Transactions on Learning Technologies, 2024
The most critical parameter of audio and video information output is the playback speed, which affects many viewing or listening metrics, including when learning using tutoring systems. However, the availability of quantitative models for personalized playback speed control considering the learner's personal traits is still an open question. The…
Descriptors: Hierarchical Linear Modeling, Intelligent Tutoring Systems, Individualized Instruction, Electronic Learning
Aydin, Mustafa – Education and Information Technologies, 2022
With the rapid technological advancements, schools and teachers have great responsibilities to educate students with regard to technological transformations. Students' ease of access to information and communications technology (ICT) tools provides ample opportunities for the development of these skills, not solely limited to schools. On the other…
Descriptors: Hierarchical Linear Modeling, Computer Literacy, Information Literacy, Foreign Countries
Jiaqi Jackie Shi – ProQuest LLC, 2024
One of the many impacts of the COVID-19 pandemic has been the increasing prevalence and accessibility of online education. This trend has also introduced challenges for students, instructors, and institutions. This study examines factors affecting online course satisfaction, focusing on individual, instructor, and institutional level…
Descriptors: Prediction, Online Courses, Higher Education, Student Attitudes
Zhou, Hao; Ma, Xin – Sociological Methods & Research, 2023
Hierarchical linear modeling (HLM) is often used to estimate the effects of socioeconomic status (SES) on academic achievement at different levels of an educational system. However, if a prior academic achievement measure is missing in a HLM model, biased estimates may occur on the effects of student SES and school SES. Phantom effects describe…
Descriptors: Simulation, Hierarchical Linear Modeling, Socioeconomic Status, Institutional Characteristics
Xu, Jianzhong; Corno, Lyn – Metacognition and Learning, 2022
Informed by two theoretical models of homework effects, we extended a model of homework on mathematics achievement in a large sample of Chinese eighth graders. Our model incorporated six clusters of homework variables -- student background factors, homework characteristics, teacher variables, parent variables, student motivation, and homework…
Descriptors: Models, Homework, Hierarchical Linear Modeling, Foreign Countries
Pov, Sokunrith; Kawai, Norimune; Murakami, Rie – International Journal of Inclusive Education, 2022
In recent years, the Royal Government of Cambodia has strongly emphasised the issue of school dropout. To date, many Cambodian workers have still not completed at least nine years of basic education due to the high dropout rate. Additionally, a great number of students at the secondary level continue to leave school early. To deal with the high…
Descriptors: Identification, Middle School Students, Dropouts, Dropout Research
Wang, Xi; Dai, Minhao; Mathis, Robin – International Journal of STEM Education, 2022
Background: Given the relatively low graduation and retention rate in undergraduate engineering programs in the United States, the factors that influence student success outcomes need to be examined. However, limited research systematically studied both student- and school-level factors and how they influenced undergraduate engineering student…
Descriptors: Student Characteristics, Institutional Characteristics, Influences, Engineering Education
Lee LeBoeuf; Jacob Goldstein-Greenwood; Angeline S. Lillard – Journal of Research on Educational Effectiveness, 2024
Common methods of measuring discipline disproportionality can produce contradictory results and obscure base-rate information. In this paper, we show how using multilevel modeling to analyze discipline disparities resolves ambiguities inherent in traditional measures of disparities: relative rate ratios and risk differences. One previous study…
Descriptors: Discipline, Disproportionate Representation, Measurement Techniques, Hierarchical Linear Modeling
Keyes, Tasha Seneca; Heath, Ryan D. – School Community Journal, 2023
Past research suggests that a sense of belonging to a community is developmentally important for adolescents and affects their engagement in school, especially during the transition to high school. However, little research examines the teaching practices that simultaneously foster classroom belonging and behavioral engagement to create a classroom…
Descriptors: Teaching Methods, Student Attitudes, Learner Engagement, Classroom Environment
Lee LeBoeuf; Jacob Goldstein-Greenwood; Angeline S Lillard – Grantee Submission, 2023
Common methods of measuring discipline disproportionality can produce contradictory results and obscure base-rate information. In this paper, we show how using multilevel modeling to analyze discipline disparities resolves ambiguities inherent in traditional measures of disparities: relative rate ratios and risk differences. One previous study…
Descriptors: Discipline, Disproportionate Representation, Measurement Techniques, Hierarchical Linear Modeling
Tiffany Wu; Christina Weiland – Society for Research on Educational Effectiveness, 2024
Background/Context: Chronic absenteeism is a serious problem that has been linked to lower academic achievement, diminished socioemotional skills, and an increased likelihood of high school dropout (Allensworth et al., 2021; Gottfried, 2014). As a result, many schools have begun to embrace early warning systems (EWS) as a tool to identify and flag…
Descriptors: Attendance, Early Childhood Education, Intervention, Artificial Intelligence
Guo, Qing; Qiao, CuiLan; Ibrahim, Bashirah – Journal of Science Education and Technology, 2022
Information and communication technology (ICT) is key to educational development. This study explores the mechanism influencing the use of ICT on students' science literacy. We utilized two-level hierarchical linear models and structural equation models to analyze data collected from the 2015 Program for International Student Assessment (PISA) in…
Descriptors: Correlation, Scientific Literacy, Information Technology, Personal Autonomy

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