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Taihe Cao; Zhaoli Zhang; Wenli Chen; Jiangbo Shu – Interactive Learning Environments, 2023
Online learning with the characteristics of flexibility and autonomy has become a widespread and popular mode of higher education in which students need to engage in self-regulated learning (SRL) to achieve success. The purpose of this study is to utilize clickstream data to reveal the time management of SRL. This study adopts learning analytics…
Descriptors: Time Management, Self Management, Online Courses, Learning Analytics
Using Analytics to Predict Students' Interactions with Learning Management Systems in Online Courses
Ali Alshammari – Education and Information Technologies, 2024
In online education, it is widely recognized that interaction and engagement have an impact on students' academic performance. While previous research has extensively explored interactions between students, instructors, and content, there has been limited exploration of course design elements that promote the fourth type of interaction:…
Descriptors: Learning Analytics, Learning Management Systems, Academic Achievement, Correlation
Sonja Kleter; Uwe Matzat; Rianne Conijn – IEEE Transactions on Learning Technologies, 2024
Much of learning analytics research has focused on factors influencing model generalizability of predictive models for academic performance. The degree of model generalizability across courses may depend on aspects, such as the similarity of the course setup, course material, the student cohort, or the teacher. Which of these contextual factors…
Descriptors: Prediction, Models, Academic Achievement, Learning Analytics
Aleksandra Maslennikova; Daniela Rotelli; Anna Monreale – Journal of Learning Analytics, 2023
Students organize and manage their own learning time, choosing when, what, and how to study due to the flexibility of online learning. Each person has unique learning habits that define their behaviours and distinguish them from others. To investigate the temporal behaviour of students in online learning environments, we seek to identify suitable…
Descriptors: Learning Analytics, Online Courses, Time Management, Self Management
Men, Qiwei; Gimbert, Belinda; Cristol, Dean – International Journal of Mobile and Blended Learning, 2023
With the rapid expansion of mobile, blended, and seamless learning, researchers claim two factors, lack of self-discipline and poor time management, adversely impact learning performance. In online educational environments, reduced social interactions and low engagement levels generate high dropout rates. Self-regulated learning (SRL), the…
Descriptors: Metacognition, Independent Study, Dropout Rate, Time Management
Mutahar Qassem; Buthainah M. Al Thowaini – Education and Information Technologies, 2024
The optimal measurement of the effectiveness of online translation training courses necessitates a comprehensive evaluation of the translation process and product to fully understand the impact of such courses on trainee translators' behavior. In this study, we employed a one-group pretest-posttest design to assess the effect of a 12-week online…
Descriptors: Learning Analytics, Intervention, Translation, Online Courses
Li, Shuang; Wang, Shuang; Du, Junlei; Pei, Yu; Shen, Xinyi – Journal of Computer Assisted Learning, 2022
Background: Failure to effectively organize and manage learning time is an important factor influencing online learners' performance. Investigation of time-investment patterns for online learning will provide educators with useful knowledge of how learners engage in and regulate their online learning and support them in tailoring online course…
Descriptors: Online Courses, Time Management, Time Factors (Learning), Learning Strategies
Antonio Estevan Martinez; Mary E. Pilgrim – International Journal of Mathematical Education in Science and Technology, 2022
In this paper, six interviews with course coordinators from two mathematics departments are analysed to better understand the relationship between course coordination and local data in the Precalculus to Calculus 2 sequence with respect to online grading platforms. We highlight several potential coordinator decisions and present illustrative…
Descriptors: Grading, Administrator Attitudes, Mathematics Instruction, Calculus
Zhongzhou Chen; Tom Zhang; Michelle Taub – Journal of Learning Analytics, 2024
The current study measures the extent to which students' self-regulated learning tactics and learning outcomes change as the result of a deliberate, data-driven improvement in the learning design of mastery-based online learning modules. In the original design, students were required to attempt the assessment once before being allowed to access…
Descriptors: Learning Analytics, Algorithms, Instructional Materials, Course Content
Er, Erkan – Online Submission, 2022
Time management is an important self-regulation strategy that can improve student learning and lead to higher performance. Students who can manage their time effectively are more likely to exhibit consistent engagement in learning activities and to complete course assignments in a timely manner. Well planning of the study time is an essential part…
Descriptors: Programming, Time Management, Computer Science Education, Integrated Learning Systems
Gafni, Ruti; Goldstein, Anat – Interdisciplinary Journal of e-Skills and Lifelong Learning, 2020
Aim/Purpose: The purpose of this study is to discover usage differences in task performance by students of different cultures, by examining procrastination patterns from a national cultural perspective and exploring the effect of multicultural virtual teamwork on students' individual procrastination. Background: This study aims to examine…
Descriptors: Teamwork, Time Management, Intercultural Communication, Online Courses
Wan, Han; Zhong, Zihao; Tang, Lina; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2023
Small private online courses (SPOCs) have influenced teaching and learning in China's higher education. Learning management systems (LMSs) are important components in SPOCs. They can collect various data related to student behavior and support pedagogical interventions. This research used feature engineering and nearest neighbor smoothing models…
Descriptors: Online Courses, Learning Management Systems, Higher Education, Student Behavior
Olive, David Monllao; Huynh, Du Q.; Reynolds, Mark; Dougiamas, Martin; Wiese, Damyon – IEEE Transactions on Learning Technologies, 2019
A significant amount of research effort has been put into finding variables that can identify students at risk based on activity records available in learning management systems (LMS). These variables often depend on the context, for example, the course structure, how the activities are assessed or whether the course is entirely online or a…
Descriptors: Prediction, Identification, At Risk Students, Online Courses