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Xia, Xiaona – Interactive Learning Environments, 2023
The interactive learning is a continuous process, which is full of a large number of learning interaction activities. The data generated between learners and learning interaction activities can reflect the online learning behaviors. Through the correlation analysis among learning interaction activities, this paper discusses the potential…
Descriptors: Behavior Patterns, Learning Analytics, Decision Making, Correlation
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
Araka, Eric; Oboko, Robert; Maina, Elizaphan; Gitonga, Rhoda – International Review of Research in Open and Distributed Learning, 2022
With the increased emphasis on the benefits of self-regulated learning (SRL), it is important to make use of the huge amounts of educational data generated from online learning environments to identify the appropriate educational data mining (EDM) techniques that can help explore and understand online learners' behavioral patterns. Understanding…
Descriptors: Data Analysis, Metacognition, Comparative Analysis, Behavior Patterns
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
Ni Li – International Journal of Web-Based Learning and Teaching Technologies, 2025
In depth exploration of how the pandemic has reshaped the education ecosystem over the past three years, especially in the context of the surge in demand for online education courses and learning platforms, this article focuses on the field of student ideological and political education, and innovatively constructs a moral and political education…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, Algorithms
Fan, Si; Chen, Lihua; Nair, Manoj; Garg, Saurabh; Yeom, Soonja; Kregor, Gerry; Yang, Yu; Wang, Yanjun – Education Sciences, 2021
This study aimed to identify factors influencing student engagement in online and blended courses at one Australian regional university. It applied a data science approach to learning and teaching data gathered from the learning management system used at this university. Data were collected and analysed from 23 subjects, spanning over 5500 student…
Descriptors: Learner Engagement, Learning Analytics, Integrated Learning Systems, Adoption (Ideas)
Bailie, Jeffrey L. – Journal of Instructional Pedagogies, 2020
For close to three decades. the positive effects of online learner engagement in asynchronous discussions have been reported. Given the many positive effects of asynchronous discussion that have been conveyed in the literature, a preponderance of today's online courses include the activity as a part of the learning experience. It seems only…
Descriptors: Learning Analytics, Asynchronous Communication, Predictor Variables, Grades (Scholastic)
Bezerra, Luis Naito Mendes; Silva, Márcia Terra – International Journal of Distance Education Technologies, 2020
In the current context of distance learning, learning management systems (LMSs) make it possible to store large volumes of data on web browsing and completed assignments. To understand student behavior patterns in this type of environment, educators and managers must rethink conventional approaches to the analysis of these data and use appropriate…
Descriptors: Learning Analytics, Data Collection, Class Size, Online Courses
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
Robert L. Peach; Sophia N. Yaliraki; David Lefevre; Mauricio Barahona – npj Science of Learning, 2019
The widespread adoption of online courses opens opportunities for analysing learner behaviour and optimising web-based learning adapted to observed usage. Here, we introduce a mathematical framework for the analysis of time-series of online learner engagement, which allows the identification of clusters of learners with similar online temporal…
Descriptors: Learning Analytics, Web Based Instruction, Online Courses, Learner Engagement
Williams-Dobosz, Destiny; Azevedo, Renato Ferreira Leitão; Jeng, Amos; Thakkar, Vyom; Bhat, Suma; Bosch, Nigel; Perry, Michelle – Grantee Submission, 2021
Little is known about the online learning behaviors of students traditionally underrepresented in STEM fields (i.e., UR-STEM students), as well as how those behaviors impact important learning outcomes. The present study examined the relationship between online discussion forum engagement and success for UR-STEM and non-UR-STEM students, using the…
Descriptors: Learner Engagement, Undergraduate Students, Student Improvement, Disproportionate Representation