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Showing 1 to 15 of 16 results Save | Export
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Tlili, Ahmed; Wang, Huanhuan; Gao, Bojun; Shi, Yihong; Zhiying, Nian; Looi, Chee-Kit; Huang, Ronghuai – Interactive Learning Environments, 2023
Online and open learning has recently been made prevalent in many regions in order to mitigate educational inequality and to enhance students' learning experiences and outcomes. Previous studies showed that students perform differently in the learning process, where cultural differences matter. However, little is known about how cultural…
Descriptors: Diversity, Cultural Differences, Behavior Patterns, Electronic Learning
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Haruna Abe; Kay Colthorpe; Pedro Isaias – Discover Education, 2025
To improve the online learning experience, adaptive learning technologies are being used to personalise learning content to suit individual learning needs, with learning analytics being integrated to collect data about the student usage behaviour on the platform. Research indicates that the adaptive learning platforms promote a supportive learning…
Descriptors: Physiology, Science Instruction, Instructional Design, Learning Management Systems
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Li, Yue; Jiang, Qiang; Xiong, Weiyan; Zhao, Wei – Education and Information Technologies, 2023
One of the recognized ways to enhance teaching and learning is having insights into the behavior patterns of students. Studies that explore behavior patterns in online self-directed learning (OSDL) are scant though. In addition, the focus is lacking on how high-achieving (HA) students' behavior patterns affect the academic performance of…
Descriptors: Student Behavior, Behavior Patterns, Electronic Learning, Online Courses
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Balti, Rihab; Hedhili, Aroua; Chaari, Wided Lejouad; Abed, Mourad – Education and Information Technologies, 2023
Since the COVID pandemic, universities propose online education to ensure learning continuity. However, the insufficient preparation led to a major drop in the learner's performance and his/her dissatisfaction with the learning experience. This may be due to several reasons, including the insensitivity of the virtual learning environment to the…
Descriptors: Cognitive Style, Pandemics, COVID-19, Distance Education
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Nan Yang; Patrizia Ghislandi – Higher Education: The International Journal of Higher Education Research, 2024
The two main trends in the development of higher education worldwide are universal access and digital transformation. These trends are bringing about an increase in class sizes and the growth of online higher education. Previous studies indicated that both the large-class setting and online delivery threaten the quality, and the exploration of…
Descriptors: Foreign Countries, Required Courses, Educational Quality, Teaching Methods
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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)
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Liu, Sanya; Hu, Zhenfan; Peng, Xian; Liu, Zhi; Cheng, H. N. H.; Sun, Jianwen – International Journal of Distance Education Technologies, 2017
In a MOOC environment, each student's interaction with the course content is a crucial clue for learning analytics, which offers an opportunity to record learner activity of unprecedented scale. In online learning, the educators and the administrators need to get informed with students' learning states since the performance of unsupervised…
Descriptors: Online Courses, Electronic Learning, Cognitive Style, Educational Research
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Liu, Zhi; Zhang, Wenjing; Cheng, Hercy N. H.; Sun, Jianwen; Liu, Sannyuya – International Journal of Distance Education Technologies, 2018
As an overt expression of internal mental processes, discourses have become one main data source for the research of interactive learning. To deeply explore behavioral regularities among interactions, this article firstly adopts the content analysis method to summarize students' engagement patterns within a course forum in a small private online…
Descriptors: Discourse Analysis, Correlation, Academic Achievement, Learner Engagement
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Jo, Il-Hyun; Park, Yeonjeong; Yoon, Meehyun; Sung, Hanall – International Review of Research in Open and Distributed Learning, 2016
The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a learning management system (LMS). As the psychological variable, time and study environment management (TSEM), one of the sub-constructs of MSLQ, was chosen to verify a set of time-related…
Descriptors: Online Courses, Time Management, Correlation, Behavior Patterns
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Kim, Jeonghyun; Jo, Il-Hyun; Park, Yeonjeong – Asia Pacific Education Review, 2016
The learning analytics dashboard (LAD) is a newly developed learning support tool for virtual classrooms that is believed to allow students to review their online learning behavior patterns intuitively through the provision of visual information. The purpose of this study was to empirically validate the effects of LAD. An experimental study was…
Descriptors: Correlation, Computer Simulation, Online Courses, Behavior Patterns
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Bendjebar, Safia; Lafifi, Yacine; Zedadra, Amina – International Journal of Distance Education Technologies, 2016
In e-learning systems, tutors have a significant impact on learners' life to increase their knowledge level and to make the learning process more effective. They are characterized by different features. Therefore, identifying tutoring styles is a critical step in understanding the preference of tutors on how to organize and help the learners. In…
Descriptors: Tutors, Tutoring, Tutor Training, Tutorial Programs
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Fanfarelli, Joseph R.; McDaniel, Rudy – Journal of Educational Technology Systems, 2015
Badge use has rapidly expanded in recent years and has benefited a variety of applications. However, a large portion of the research has applied a binary "useful" or "not useful" approach to badging. Few studies examine the characteristics of the user and the impact of those characteristics on the effectiveness of the badging…
Descriptors: College Students, Recognition (Achievement), Individual Differences, Student Characteristics
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Gilmore, Dawn – Journal of Learning Analytics, 2014
This research applies Goffman's Presentation of Self in Everyday Life to analyze online and offline student participation in two online subjects. Mixed-methods will be used to produce a fuller account of student experiences.
Descriptors: Online Courses, Student Participation, Mixed Methods Research, Student Experience
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Cheng, Kun-Hung; Hou, Huei-Tse; Wu, Sheng-Yi – Interactive Learning Environments, 2014
In the social interactions among individuals of learning communities, including those individuals engaged in peer assessment activities, emotion may be a key factor in learning. However, research regarding the emotional response of learners in online peer assessment activities is relatively scarce. Detecting learners' emotion when they make…
Descriptors: Online Courses, Electronic Learning, Peer Evaluation, Emotional Response
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Li, Nan; Verma, Himanshu; Skevi, Afroditi; Zufferey, Guillaume; Blom, Jan; Dillenbourg, Pierre – Distance Education, 2014
Research suggests that massive open online course (MOOC) students prefer to study in groups, and that social facilitation within the study groups may render the learning of difficult concepts a pleasing experience. We report on a longitudinal study that investigates how co-located study groups watch and study MOOC videos together. The study was…
Descriptors: Online Courses, Video Technology, Cooperative Learning, Large Group Instruction
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