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Cömert, Zeynep; Samur, Yavuz – Interactive Learning Environments, 2023
Almost in every aspect of life, classification and categorization make it easier for humans to analyze complex structures and systems. In games, the classification of the players based on their demographics, behaviors, expectations and preferences of the game is important to increase players' motivation and satisfaction. Likewise, knowing the…
Descriptors: Classification, Student Characteristics, Models, Student Motivation
Ashraf, Erum; Manickam, Selvakumar; Karuppayah, Shankar; Malik, Sufiana Khatoon – Journal of Educators Online, 2023
As the drive to move from traditional face-to-face classroom learning to e-learning is ever in demand, the knowledge corpus exposed to students can be overwhelming because there is a need to automate certain functions of the e-learning framework. One of these functions is the course recommendation feature. Course recommendations help students save…
Descriptors: Electronic Learning, Cognitive Style, Student Behavior, Course Selection (Students)
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
Pei, Bo; Xing, Wanli – Journal of Educational Computing Research, 2022
This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for…
Descriptors: At Risk Students, Learning Analytics, Information Retrieval, Models
Wang, Ying; Wang, Hong; Albert, Leslie Jordan – Journal of Information Systems Education, 2023
The MOOC (Massive Open Online Course) providers promote their courses as education that builds marketable skills. However, little research examines the role of relevance in the success of MOOCs or how this relevance influences learner behaviors. This study highlights the importance of MOOC relevance by decomposing it into personal relevance and…
Descriptors: MOOCs, Adult Students, Student Behavior, Relevance (Education)
Heathcote, Dean; Savage, Simon; Hosseinian-Far, Amin – Education Sciences, 2020
Although regulations and established practices in academia have focused on a data-rich model of performance information, both to evidence operational capability and to support recruitment, it is considered that this approach has been largely ineffective in addressing student choice behaviour. Historical studies, business, psychology, and…
Descriptors: College Choice, Influences, Decision Making, Foreign Countries
Hlioui, Fedia; Aloui, Nadia; Gargouri, Faiez – International Journal of Web-Based Learning and Teaching Technologies, 2021
Nowadays, the virtual learning environment has become an ideal tool for professional self-development and bringing courses for various learner audiences across the world. There is currently an increasing interest in researching the topic of learner dropout and low completion in distance learning, with one of the main concerns being elevated rates…
Descriptors: At Risk Students, Withdrawal (Education), Dropouts, Distance Education
Reinke, Wendy M.; Herman, Keith C.; Thompson, Aaron; Copeland, Christa; McCall, Chynna S.; Holmes, Shannon; Owens, Sarah A. – Grantee Submission, 2021
Many youth experience mental health problems. Schools are an ideal setting to identify, prevent, and intervene in these problems. The purpose of this study was to investigate patterns of student social, emotional, and behavioral risk over time among a community sample of 3rd through 12th grade students and the association of these risk patterns…
Descriptors: Mental Disorders, Models, Mental Health, Prevention
Pursel, B. K.; Zhang, L.; Jablokow, K. W.; Choi, G. W.; Velegol, D. – Journal of Computer Assisted Learning, 2016
Massive open online courses (MOOCs) continue to appear across the higher education landscape, originating from many institutions in the USA and around the world. MOOCs typically have low completion rates, at least when compared with traditional courses, as this course delivery model is very different from traditional, fee-based models, such as…
Descriptors: Online Courses, Graduation Rate, Delivery Systems, Models
Reinke, Wendy M.; Herman, Keith C.; Thompson, Aaron; Copeland, Christa; McCall, Chynna S.; Holmes, Shannon; Owens, Sarah A. – School Psychology Review, 2020
Many youth experience mental health problems. Schools are an ideal setting to identify, prevent, and intervene in these problems. The purpose of this study was to investigate patterns of student social, emotional, and behavioral risk over time among a community sample of 3rd through 12th grade students and the association of these risk patterns…
Descriptors: Mental Disorders, Models, Mental Health, Prevention
Kinahan, Mary P. – ProQuest LLC, 2017
Seating assignments and arrangements are utilized in every school classroom. This qualitative study explored the perceptions that teachers have on seating assignments and arrangements to gain a better understanding of how they make design considerations which impact their students. The questions that guide this study are: How do elementary…
Descriptors: Classroom Environment, Space Utilization, Classroom Design, Elementary School Teachers
Coleman, Chad; Baker, Ryan S.; Stephenson, Shonte – International Educational Data Mining Society, 2019
Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…
Descriptors: Prediction, At Risk Students, Predictor Variables, Elementary Secondary Education
Grunschel, Carola; Schopenhauer, Lena – Journal of College Student Development, 2015
In light of the drawbacks of academic procrastination, it is surprising that not all students want to decrease academic procrastination. To find out why students are motivated (or not) to change academic procrastination, we investigated the characteristics of 377 German students with different motivations to change based on the Transtheoretical…
Descriptors: Foreign Countries, Measures (Individuals), College Students, Student Motivation
Rosenqvist, Erik – Sociology of Education, 2018
Peers have a paradoxical influence on each other's educational decisions. On one hand, students are prone to conform to each other's ambitious educational decisions and, on the other hand, are discouraged from ambitious decisions when surrounded by successful peers. In this study I examine how peers influence each other's decision to apply to an…
Descriptors: Foreign Countries, Peer Influence, Secondary School Students, Achievement
Ning, Hoi Kwan; Downing, Kevin – Studies in Higher Education, 2015
Based on self-reported cognitive, metacognitive, and behavioural strategy measures obtained from 828 final-year students from a university in Hong Kong, latent profile analysis (LPA) identified four distinct types of students with differential self-regulated learning strategy orientations: "competent self-regulated learners",…
Descriptors: Profiles, Metacognition, Learning Strategies, College Seniors

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