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Shuanghong Shen; Qi Liu; Zhenya Huang; Yonghe Zheng; Minghao Yin; Minjuan Wang; Enhong Chen – IEEE Transactions on Learning Technologies, 2024
Modern online education has the capacity to provide intelligent educational services by automatically analyzing substantial amounts of student behavioral data. Knowledge tracing (KT) is one of the fundamental tasks for student behavioral data analysis, aiming to monitor students' evolving knowledge state during their problem-solving process. In…
Descriptors: Student Behavior, Electronic Learning, Data Analysis, Models
Lasse X. Jensen; Margaret Bearman; David Boud – Teaching in Higher Education, 2025
Understanding how students engage with feedback is often reduced to a study of feedback messages that sheds little light on effects. Using the emerging notion of feedback encounters as an analytical lens, this study examines what characterizes productive feedback encounters when learning online. Drawing from a cross-national digital ethnographic…
Descriptors: Feedback (Response), Electronic Learning, Foreign Countries, College Students
Mangrich, Ashley E. – ProQuest LLC, 2023
This study focused on postsecondary students who participated in solely online educational programs. The study sought to determine if there was a correlation between student personality traits and their ability to persist in online programs. In addition, this study revealed if a certain personality profile, based on the dominant trait, correlated…
Descriptors: College Students, Personality Traits, Online Courses, Electronic Learning
Christopher Jutz; Kai-Michael Griese; Henrike Rau; Johanna Schoppengerd; Ines Prehn – International Journal of Sustainability in Higher Education, 2024
Purpose: Online education enables location-independent learning, potentially providing university students with more flexible study programs and reducing traffic-related CO2 emissions. This paper aims to examine whether online education can contribute to university-related sustainable everyday mobility, with particular consideration given to…
Descriptors: Electronic Learning, Online Courses, Sustainability, Mobility
Tong, Yao; Zhan, Zehui – Interactive Technology and Smart Education, 2023
Purpose: The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners' online learning behaviors, and comparing three algorithms -- multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART).…
Descriptors: MOOCs, Online Courses, Learning Analytics, Prediction
Yang, Tzu-Chi; Chen, Sherry Y. – Interactive Learning Environments, 2023
Individual differences exist among learners. Among various individual differences, cognitive styles can strongly predict learners' learning behavior. Therefore, cognitive styles are essential for the design of online learning. There are a variety of cognitive style dimensions and overlaps exist among these dimensions. In particular, Witkin's field…
Descriptors: Student Behavior, Educational Technology, Electronic Learning, Cognitive Style
Charlotte Parque; Brianna Wingard; Kayla Neumann; Chelsea Ebisuya; Sarah Zasso; Rosalie Dillon; Kathryn Bruchmann – Journal of American College Health, 2025
Objective: College students tend to have lower body image than other groups, in part because of comparisons they make with peers. The closing of college campuses due to the onset of the COVID-19 pandemic disrupted the ability to compare; thus, we investigate how the transition to and from virtual-learning influenced body image. Participants:…
Descriptors: Undergraduate Students, School Closing, COVID-19, Pandemics
Bessadok, Adel; Abouzinadah, Ehab; Rabie, Osama – Interactive Technology and Smart Education, 2023
Purpose: This paper aims to investigate the relationship between the students' digital activities and their academic performance through two stages. In the first stage, students' digital activities were studied and clustered based on the attributes of their activity log of learning management system (LMS) data set. In the second stage, the…
Descriptors: Learning Activities, Academic Achievement, Learning Management Systems, Data Analysis
Bagdi, Himanshu; Bulsara, Hemantkumar P. – Journal of Applied Research in Higher Education, 2023
Purpose: This research aims to look at how students feel about taking online learning (OL) while studying in higher education institutions (HEIs) using an extended technology acceptance model (TAM). The study looked into the factors that influence students' decisions to use OL, which helps meet their individual needs beyond the confines of the…
Descriptors: Online Courses, College Students, Student Attitudes, Decision Making
Bhati, Narender Singh; Srivastava, Sachin; Rathore, Jaivardhan Singh – Journal of Information Technology Education: Innovations in Practice, 2023
Aim/Purpose: The study aims to supplement existing knowledge of information systems by presenting empirical data on the factors influencing the intentions of doctoral students to learn through online platforms. Background: E-learning platforms have become popular among students and professionals over the past decade. However, the intentions of the…
Descriptors: Doctoral Students, Intention, Online Courses, Educational Technology
Dapeng Liu; Lemuria Carter; Jiesen Lin – Online Learning, 2024
The COVID-19 pandemic precipitated a global shift to fully remote learning via learning management systems (LMS). Despite this significant shift, there has been a paucity of research exploring how students of varying academic performance engage with online learning resources. This study investigates the utilization of LMS among students with…
Descriptors: Learning Management Systems, COVID-19, Pandemics, Electronic Learning
Nizar Saputra; Mulyani; Asirah – Journal of English Teaching, 2024
With the expanding realm of online education in Indonesia, determining factors influencing university students' participation in distinct modes of virtual learning is crucial to developing more effective digital pedagogy. This research aims to scrutinize factors affecting Indonesian EFL (English as a Foreign Language) university students' low…
Descriptors: Foreign Countries, College Students, Online Courses, Electronic Learning
Al-Azawei, Ahmed; Abdullah, Alharith A.; Mohammed, Mahmood K.; Abod, Zaid A. – International Review of Research in Open and Distributed Learning, 2023
Although online learning has become ubiquitous worldwide, earlier research has neglected the relationship between its actual use and security concerns. Learners' lack of security awareness while using learning technologies remains rarely studied. This paper integrates Delone and McLean's information system success (D&M-ISS) model with the…
Descriptors: Foreign Countries, Online Courses, Electronic Learning, Success
Jelena Andelkovic Labrovic; Nikola Petrovic; Jelena Andelkovic; Marija Meršnik – Journal of Computing in Higher Education, 2025
The focus of this study was on identifying patterns of student behavior to support data-informed decision-making which would then improve the learning experience and learning outcomes of online English language courses. Learning analytics approach (or more specifically cluster analysis) was used to identify engagement patterns in online learning.…
Descriptors: Electronic Learning, Online Courses, Behavior Patterns, Student Behavior
Broadbent, Jaclyn; Howe, Wren D. W. – Distance Education, 2023
Online help-seeking refers to a learner's willingness to seek help in online learning environments. Counterintuitively, studies of help-seeking have found mixed results for the relationship between help-seeking and academic achievement. We hypothesized that these mixed findings might, in part, be accounted for by the confidence level of the…
Descriptors: Foreign Countries, Electronic Learning, Distance Education, College Students