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Jaudon Ron Foiret; Mariette Volschenk – Anatomical Sciences Education, 2024
Digital anatomy learning platforms hold potential academic benefits, yet there is currently no universally accepted pedagogical framework guiding their utilization. This study applied the lens of Laurillard's conversational framework to explore second-year medical students' experiences with a digital anatomy learning platform at a South African…
Descriptors: Medical Students, Medical Education, Anatomy, Learning Management Systems
Biedermann, Daniel; Ciordas-Hertel, George-Petru; Winter, Marc; Mordel, Julia; Drachsler, Hendrik – Journal of Learning Analytics, 2023
Learners use digital media during learning for a variety of reasons. Sometimes media use can be considered "on-task," e.g., to perform research or to collaborate with peers. In other cases, media use is "off-task," meaning that learners use content unrelated to their current learning task. Given the well-known problems with…
Descriptors: Learning Processes, Learning Analytics, Information Technology, Behavior Patterns
Rotelli, Daniela; Monreale, Anna – Journal of Learning Analytics, 2023
The increased adoption of online learning environments has resulted in the availability of vast amounts of educational log data, which raises questions that could be answered by a thorough and accurate examination of students' online learning behaviours. Event logs describe something that occurred on a platform and provide multiple dimensions that…
Descriptors: Learning Analytics, Learning Management Systems, Time on Task, Student Behavior
Yangyang Luo; Xibin Han; Chaoyang Zhang – Asia Pacific Education Review, 2024
Learning outcomes can be predicted with machine learning algorithms that assess students' online behavior data. However, there have been few generalized predictive models for a large number of blended courses in different disciplines and in different cohorts. In this study, we examined learning outcomes in terms of learning data in all of the…
Descriptors: Prediction, Learning Management Systems, Blended Learning, Classification
Garg, Anchal; Rajendran, Ramkumar – Education and Information Technologies, 2023
Procrastination is one of the issues affecting more than half of the student population and is known to impact them negatively. It is also one of the major reasons for failure and dropout. Therefore, several studies have been conducted in this domain to understand when and why students procrastinate. The existing studies use self-reported…
Descriptors: Behavior Patterns, Time Management, Web Sites, Editing
Mohammed Jebbari; Bouchaib Cherradi; Soufiane Hamida; Abdelhadi Raihani – Education and Information Technologies, 2024
With the advancements in technology and the growing demand for online education, Virtual Learning Environments (VLEs) have experienced rapid development in recent years. This demand was especially evident during the COVID-19 pandemic. The incorporation of new technologies in VLEs provides new opportunities to better understand the behaviors of…
Descriptors: MOOCs, Algorithms, Computer Simulation, COVID-19
Mohd Fazil; Angelica Rísquez; Claire Halpin – Journal of Learning Analytics, 2024
Technology-enhanced learning supported by virtual learning environments (VLEs) facilitates tutors and students. VLE platforms contain a wealth of information that can be used to mine insight regarding students' learning behaviour and relationships between behaviour and academic performance, as well as to model data-driven decision-making. This…
Descriptors: Learning Analytics, Learning Management Systems, Learning Processes, Decision Making
Gökhan Akçapinar; Erkan Er; Alper Bayazit – International Review of Research in Open and Distributed Learning, 2024
Lecture capture videos, a popular type of instructional content used by instructors to share course recordings online, play a significant role in educational settings. Compared to other educational videos, these recordings require minimal time and effort to produce, making them a preferred choice for disseminating course materials. Despite their…
Descriptors: Learner Engagement, Video Technology, Lecture Method, Student Behavior
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
Mohammadi Zenouzagh, Zohre – Educational Technology Research and Development, 2023
This study investigated student interaction patterns and their co-regulation practices in text-based and multimodal computer mediated collaborative (CMC) writing. To this end, 30 EFL (English as foreign language) participants' online collaborative writing performances were analyzed. The analysis included conversation analysis on the transcription…
Descriptors: Interaction, Behavior Patterns, Computer Uses in Education, Collaborative Writing
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
Lars de Vreugd; Anouschka van Leeuwen; Renée Jansen; Marieke van der Schaaf – Journal of Learning Analytics, 2024
For university students, self-regulation of study behaviour is important. However, students are not always capable of effective self-regulation. Providing study behaviour information via a learning analytics dashboard (LAD) may support phases within self-regulated learning (SRL). However, it is unclear what information a LAD should provide, how to…
Descriptors: Learning Management Systems, Learning Analytics, Student Behavior, Behavior Patterns
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
Çebi, Ayça; Araújo, Rafael D.; Brusilovsky, Peter – Journal of Research on Technology in Education, 2023
Online learning systems allow learners to freely access learning contents and record their interactions throughout their engagement with the content. By using data mining techniques on the student log data of those systems, it is possible to examine learning behavior and reveal navigation patterns through learning contents. This study was aimed at…
Descriptors: Individual Characteristics, Electronic Learning, Student Behavior, Learning Management Systems
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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