"An Analysis on the Research Trends on Online Learning Engagement Using CiteSpace"

2021 ◽  
Vol 65 (3) ◽  
pp. 1-20
Author(s):  
Chen Yuanyuan ◽  
Kim Gina
Keyword(s):  
Online Learning ◽  
Research Trends ◽  
Learning Engagement

Teacher Presence

2018 ◽  
pp. 7922-7934
Author(s):  
Caroline M. Crawford
Keyword(s):  
Online Learning ◽  
Instructional Design ◽  
Learning Environment ◽  
Learning Environments ◽  
Mobile Learning ◽  
Support System ◽  
Distributed Learning ◽  
Learning Engagement ◽  
Teacher Presence ◽  
Instructional Facilitator

Teacher presence refers to the fully engaged instructional facilitator within a learning environment. Within this specific discussion of teacher presence, the focus is upon distributed learning environments that includes not only online learning environments but also mobile learning engagement efforts. Teacher presence engages not merely an instructional design and evaluative assessment effort, but integrally engages the learners within the instructional environment through discourse, reflective practices and supporting the motivational needs of the learner. Further, teacher presence directly impacts the motivational and cognitive support needs of learners, through instructionally appropriate actions of the instructor as a facilitative guide, as a self-regulatory maven, within a cognitive load support system, as well as mentor-focused instructional efforts.

Investigating English teachers' online learning engagement: A case study during COVID-19 pandemic

2021 ◽  
pp. 49-54
Author(s):  
Pratiwi Amelia ◽  
Dwi Rukmini ◽  
Januarius Mujiyanto ◽  
Dwi Anggani Linggar Bharati
Keyword(s):  
Online Learning ◽  
English Teachers ◽  
Learning Engagement

scholarly journals Online engagement and performance on formative assessments mediate the relationship between attendance and course performance

2022 ◽  
Vol 19 (1) ◽  
Author(s):  
Chang Lu ◽  
Maria Cutumisu
Keyword(s):  
Online Learning ◽  
Academic Success ◽  
Formative Assessment ◽  
Academic Outcomes ◽  
Formative Assessments ◽  
Learning Engagement ◽  
Class Attendance ◽  
And Performance ◽  
The Impact ◽  
The Relationship

AbstractIn traditional school-based learning, attendance was regarded as a proxy for engagement and key indicator for performance. However, few studies have explored the effect of in-class attendance in technology-enhanced courses that are increasingly provided by secondary institutions. This study collected n = 367 undergraduate students’ log files from Moodle and applied learning analytics methods to measure their lecture attendance, online learning activities, and performance on online formative assessments. A baseline and an alternative structural equation models were used to investigate whether online learning engagement and formative assessment mediated the relationship between lecture attendance and course academic outcomes. Results show that lecture attendance does not have a direct effect on academic outcomes, but it promotes performance by leveraging online learning engagement and formative assessment performance. Findings contribute to understanding the impact of in-class attendance on course academic performance and the interplay of in-class and online-learning engagement factors in the context of technology-enhanced courses. This study recommends using a variety of educational technologies to pave multiple pathways to academic success.

Data-driven Online Learning Engagement Detection via Facial Expression and Mouse Behavior Recognition Technology

2019 ◽  
Vol 58 (1) ◽  
pp. 63-86 ◽  
Author(s):  
Zhaoli Zhang ◽  
Zhenhua Li ◽  
Hai Liu ◽  
Taihe Cao ◽  
Sannyuya Liu
Keyword(s):  
Online Learning ◽  
Facial Expression ◽  
Fundamental Problem ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Expression Recognition ◽  
Learning Engagement ◽  
Movement Data ◽  
Efficient Detection ◽  
Mouse Movement

Online learning engagement detection is a fundamental problem in educational information technology. Efficient detection of students’ learning situations can provide information to teachers to help them identify students having trouble in real time. To improve the accuracy of learning engagement detection, we have collected two aspects of students’ behavior data: face data (using adaptive weighted Local Gray Code Patterns for facial expression recognition) and mouse interaction. In this article, we propose a novel learning engagement detection algorithm based on the collected data (students’ behavior), which come from the cameras and the mouse in the online learning environment. The cameras were utilized to capture students’ face images, while the mouse movement data were captured simultaneously. In the process of image data labeling, we built two datasets for classifier training and testing. One took the mouse movement data as a reference, while the other did not. We performed experiments on two datasets using several methods and found that the classifier trained by the former dataset had a better performance, and its recognition rate is higher than that of the latter one (94.60% vs. 91.51%).

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