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Shiqi Liu; Sannyuya Liu; Xian Peng; Jianwen Sun; Zhi Liu – Journal of Educational Computing Research, 2025
Forum discussions in Massive Open Online Courses (MOOCs) play a crucial role in promoting learning engagement and academic achievement. In particular, discussion topics significantly influence learners' emotional and cognitive engagement. However, the complex interrelationships among these factors remain underexplored. This study introduces an…
Descriptors: MOOCs, Difficulty Level, Learner Engagement, Academic Achievement
Hyomin Kim; Gyunam Park; Minsu Cho – Education and Information Technologies, 2024
Learning analytics, located at the intersection of learning science, data science, and computer science, aims to leverage educational data to enhance teaching and learning. However, as educational data increases, distilling meaningful insights presents challenges, particularly concerning individual learner differences. This work introduces a…
Descriptors: Learner Engagement, Academic Achievement, Learning Processes, Learning Analytics
Nina Bergdahl; Melissa Bond; Jeanette Sjöberg; Mark Dougherty; Emily Oxley – International Journal of Educational Technology in Higher Education, 2024
Educational outcomes are heavily reliant on student engagement, yet this concept is complex and subject to diverse interpretations. The intricacy of the issue arises from the broad spectrum of interpretations, each contributing to the understanding of student engagement as both complex and multifaceted. Given the emergence and increasing use of…
Descriptors: Learner Engagement, College Students, Student Behavior, Educational Technology
Flora Ji-Yoon Jin; Bhagya Maheshi; Wenhua Lai; Yuheng Li; Danijela Gasevic; Guanliang Chen; Nicola Charwat; Philip Wing Keung Chan; Roberto Martinez-Maldonado; Dragan Gaševic; Yi-Shan Tsai – Journal of Learning Analytics, 2025
This paper explores the integration of generative AI (GenAI) in the feedback process in higher education through a learning analytics (LA) tool, examined from a feedback literacy perspective. Feedback literacy refers to students' ability to understand, evaluate, and apply feedback effectively to improve their learning, which is crucial for…
Descriptors: College Students, Student Attitudes, Artificial Intelligence, Learning Analytics
Yuqin Yang; Yewen Chen; Xueqi Feng; Daner Sun; Shiyan Pang – Journal of Computing in Higher Education, 2024
Helping students gradually develop collective knowledge is critical but generally faces great challenges. Employing a quasi-experimental design, this study investigated the impacts and mechanisms of analytics-supported reflective assessment on the collective knowledge advancement of undergraduates. The experimental group (n = 55) engaged in…
Descriptors: Undergraduate Students, Learning Processes, Learning Analytics, Learner Engagement
Eamon Vale; Garry Falloon – Online Learning, 2024
This research investigated the potential of learning analytics (LA) as a tool for identifying and evaluating K-12 student behaviors associated with active learning when using video learning objects within an online learning environment (OLE). The study focused on the application of LA for evaluating K-12 student engagement in videobased…
Descriptors: Learning Analytics, Elementary School Students, Secondary School Teachers, Electronic Learning
Neil Dixon; Rob Howe; Uwe Matthias Richter – Research in Learning Technology, 2025
Learning analytics (LA) provides insight into student performance and progress, allowing for targeted interventions and support to improve the student learning experience. Uses of LA are diverse, including measuring student engagement, retention, progression, student well-being and curriculum development. This article provides perspectives on the…
Descriptors: Learning Analytics, Educational Benefits, Case Studies, Higher Education
Bhagya Maheshi; Wei Dai; Roberto Martinez-Maldonado; Yi-Shan Tsai – Journal of Computer Assisted Learning, 2024
Background: Feedback is central to formative assessments but aligns with a one-way information transmission perspective obstructing students' effective engagement with feedback. Previous research has shown that a responsive, dialogic feedback process that requires educators and students to engage in ongoing conversations can encourage student…
Descriptors: Feedback (Response), Learning Analytics, Dialogs (Language), Learner Engagement
Taolin Zhang; Shuwen Jia; Charoula Angeli – International Journal of Web-Based Learning and Teaching Technologies, 2024
Considering the shortcomings of large evaluation errors, long time, human, and material resources in the evaluation process of the current college teaching mode to improve the accuracy of the evaluation of college teaching mode and reduce the cost of the evaluation, this study proposes an evaluation method for college teaching methods based on…
Descriptors: Evaluation Methods, Educational Change, Learning Analytics, Educational Technology
Hatice Yildiz Durak – Education and Information Technologies, 2025
Feedback is critical in providing personalized information about educational processes and supporting their performance in online collaborative learning environments. However, giving effective feedback and monitoring its effects, which is especially important in online environments, is a complex issue. Although providing feedback by analyzing…
Descriptors: Feedback (Response), Online Systems, Electronic Learning, Learning Analytics
Nick Hopwood; Tracey-Ann Palmer; Gloria Angela Koh; Mun Yee Lai; Yifei Dong; Sarah Loch; Kun Yu – International Journal of Research & Method in Education, 2025
Student emotions influence assessment task behaviour and performance but are difficult to study empirically. The study combined qualitative data from focus group interviews with 22 students and 4 teachers, with quantitative real-time learning analytics (facial expression, mouse click and keyboard strokes) to examine student emotional engagement in…
Descriptors: Psychological Patterns, Student Evaluation, Learning Analytics, Learner Engagement
Robert J. Summers; Adrian P. Burgess; Helen E. Higson; Elisabeth Moores – Studies in Higher Education, 2024
To explore potential effects of disadvantage on engagement and attainment under different teaching and assessment regimes, the influence of pedagogic changes implemented during the COVID-19 pandemic on attainment and engagement of students from different backgrounds were compared using a cohort-study design. Learner analytics and attainment data…
Descriptors: Foreign Countries, Learning Analytics, Disadvantaged, College Freshmen
Jinnie Shin; Bowen Wang; Wallace N. Pinto Junior; Mark J. Gierl – Large-scale Assessments in Education, 2024
The benefits of incorporating process information in a large-scale assessment with the complex micro-level evidence from the examinees (i.e., process log data) are well documented in the research across large-scale assessments and learning analytics. This study introduces a deep-learning-based approach to predictive modeling of the examinee's…
Descriptors: Prediction, Models, Problem Solving, Performance
Elena Drugova; Irina Zhuravleva; Ulyana Zakharova; Adel Latipov – Journal of Computer Assisted Learning, 2024
Background: Driven by the ongoing need to provide high-quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to…
Descriptors: Learning Analytics, Instructional Design, Higher Education, Instructional Improvement
Priya Sharma; Mahir Akgun; Qiyuan Li – Educational Technology Research and Development, 2024
Networked and digital technologies are increasingly being used for learning in formal and informal contexts, and participant engagement occurs primarily via online discussions. In this paper, we describe an ongoing research project that focuses on examining and understanding student engagement in collaborative online discussions within a formal…
Descriptors: Social Networks, Network Analysis, Discourse Analysis, Classification