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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
Garcia, Victor; Conesa, Jordi; Perez-Navarro, Antoni – Journal of Science Education and Technology, 2022
Videos created with the hands of teachers filmed have been perceived as useful educational resource for students of Physics in undergraduate courses. In previous works, we analyzed the students' perception about educational videos by asking them about their experiences. In this work, we analyze the same facts, but from a learning analytics…
Descriptors: Physics, Science Instruction, Teaching Methods, Video Technology
Gallego-Romero, Jesús Manuel; Alario-Hoyos, Carlos; Estévez-Ayres, Iria; Delgado Kloos, Carlos – Educational Technology Research and Development, 2020
Massive Open Online Courses (MOOCs) can be enhanced with the so-called learning-by-doing, designing the courses in a way that the learners are involved in a more active way in the learning process. Within the options for increasing learners' interaction in MOOCs, it is possible to integrate (third-party) external tools as part of the instructional…
Descriptors: Learner Engagement, Student Behavior, Learning Analytics, Online Courses
Ait-Adda, Samia; Bousbia, Nabila; Balla, Amar – E-Learning and Digital Media, 2023
Our aim in this paper is to improve the efficiency of a learning process by using learners' traces to detect particular needs. The analysis of the semantic path of a learner or group of learners during the learning process can allow detecting those students who are in needs of help as well as identify the insufficiently mastered concepts. We…
Descriptors: Semantics, Learning Processes, Learning Analytics, Models
Chen, Chen; Sonnert, Gerhard; Sadler, Philip M.; Sasselov, Dimitar D.; Fredericks, Colin; Malan, David J. – Distance Education, 2020
Participants' engagement in massive online open courses (MOOCs) is highly irregular and self-directed. It is well known in the field of television media that substantial parts of the audience tend to drop out at major episodic, or seasonal, closures, which makes creating cliff-hangers a crucial strategy to retain viewers (Bakker, 1993; Cazani,…
Descriptors: Online Courses, Dropouts, Learning Analytics, Dropout Rate
Victor Manuel Corza-Vargas; Roberto Martinez-Maldonado; Boris Escalante-Ramirez; Jimena Olveres – Journal of Learning Analytics, 2024
While teachers often monitor and adjust their learning design based on students' emotional states in physical classrooms, synchronous online environments often limit their ability to perceive the emotional climate of the class. Drawing from the concept of social translucence, it is suggested that making students' emotional states…
Descriptors: Foreign Countries, Undergraduate Students, Privacy, Cultural Awareness
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
Lundqvist, Karsten Ø.; Liyanagunawardena, Tharindu; Starkey, Louise – International Review of Research in Open and Distributed Learning, 2020
Many course designers trying to evaluate the experience of participants in a MOOC will find it difficult to track and analyse the online actions and interactions of students because there may be thousands of learners enrolled in courses that sometimes last only a few weeks. This study explores the use of automated sentiment analysis in assessing…
Descriptors: Feedback (Response), Online Courses, Student Experience, Peer Relationship
González-Castro, Nuria; Muñoz-Merino, Pedro J.; Alario-Hoyos, Carlos; Delgado Kloos, Carlos – Australasian Journal of Educational Technology, 2021
Massive open online courses (MOOCs) pose a challenge for instructors when trying to provide personalised support to learners, due to large numbers of registered participants. Conversational agents can be of help to support learners when working with MOOCs. This article presents an adaptive learning module for JavaPAL, a conversational agent that…
Descriptors: Online Courses, Learning Modules, Computer Science Education, Programming
Matcha, Wannisa; Gasevic, Dragan; Uzir, Nora'ayu Ahmad; Jovanovic, Jelena; Pardo, Abelardo; Lim, Lisa; Maldonado-Mahauad, Jorge; Gentili, Sheridan; Perez-Sanagustin, Mar; Tsai, Yi-Shan – Journal of Learning Analytics, 2020
Generalizability of the value of methods based on learning analytics remains one of the big challenges in the field of learning analytics. One approach to testing generalizability of a method is to apply it consistently in different learning contexts. This study extends a previously published work by examining the generalizability of a learning…
Descriptors: Learning Analytics, Learning Strategies, Instructional Design, Delivery Systems