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Mthokozisi Masumbika Ncube; Patrick Ngulube – Discover Education, 2025
Despite the increasing interest in data analytics applications within postgraduate education research, there remains a significant gap in research dedicated to exploring mixed methods research for such investigations. This study undertook to bridge this gap by exploring the application and use of mixed methods research to examine data analytics…
Descriptors: Data Analysis, Graduate Students, Educational Research, Mixed Methods Research
Mengke Wang; Taotao Long; Na Li; Yawen Shi; Zengzhao Chen – Education and Information Technologies, 2025
Feedback plays an indispensable role in pre-service teachers' microteaching practice. It provides essential information about their microteaching performance, which is of great significance in their reflection and improvement. As AI and teaching analytics advance, feedback is no longer exclusively human-generated. AI technologies are increasingly…
Descriptors: Feedback (Response), Preservice Teachers, Microteaching, Reflection
Mthokozisi Masumbika Ncube; Patrick Ngulube – Discover Education, 2025
The potential of data analytics in higher education is well acknowledged. Yet, there is a notable gap in the literature regarding the practical application of theoretical frameworks for its implementation and evaluation. While research has investigated data analytics for personalised learning, student success prediction, and programme assessment,…
Descriptors: Data Analysis, Learning Analytics, Higher Education, Postsecondary Education as a Field of Study
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
S. Asha; V. P. Joshith – Journal of Educational Technology Systems, 2025
Learning analytics (LA) has become a critical field for transforming educational practices through data-driven insights. This study presents a comprehensive bibliometric analysis of LA research in higher education from 2013 to 2023, utilizing the Scientific Procedures and Rationales for Systematic Literature Review (SPAR-4-SLR) approach. By…
Descriptors: Bibliometrics, Learning Analytics, Educational Change, Educational Practices
Jun Oshima; Ritsuko Oshima; Anthony J. Taiki Kawakubo – Journal of Computer Assisted Learning, 2025
Background: This study aimed to develop and test new analytics for knowledge-building practices from the transactive perspective. Based on a literature review, network analysis was identified as a promising analytical tool for these practices. We observed two aspects of network analysis that could be further developed: the multilayers of networks…
Descriptors: Network Analysis, Concept Formation, Learning Processes, Performance
Lars de Vreugd; Anouschka van Leeuwen; Marieke van der Schaaf – Journal of Computer Assisted Learning, 2025
Background: University students need to self-regulate but are sometimes incapable of doing so. Learning Analytics Dashboards (LADs) can support students' appraisal of study behaviour, from which goals can be set and performed. However, it is unclear how goal-setting and self-motivation within self-regulated learning elicits behaviour when using an…
Descriptors: Learning Analytics, Educational Technology, Goal Orientation, Learning Motivation
Ridwan Whitehead; Andy Nguyen; Sanna Järvelä – Journal of Learning Analytics, 2025
Incorporating non-verbal data streams is essential to understanding the dynamics of interaction within collaborative learning environments in which a variety of verbal and non-verbal modes of communication intersect. However, the complexity of non-verbal data -- especially gathered in the wild from collaborative learning contexts -- demands…
Descriptors: Case Studies, Nonverbal Communication, Video Technology, Data Analysis
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
Rotem Abdu; Shai Olsher – Mathematics Teacher Education and Development, 2025
Group composition affects learning by individuals. Dialogic pedagogy approaches demonstrate that this is particularly true when each grouped student knows something others do not (i.e., "mutuality" grouping). Learning analytics can help grouping by providing teachers with data on students' content-specific learning. What are mathematics…
Descriptors: Mathematics Teachers, Grouping (Instructional Purposes), Learning Analytics, Mathematics Instruction
Yousri Attia Mohamed Abouelenein; Shaimaa Abdul Salam Selim; Tahani Ibrahim Aldosemani – Smart Learning Environments, 2025
Learning analytics provides valuable data to inform the best decisions for each learner. This study, based on adaptive environment (AE) learning analytics dashboards, examines how instructor interventions affect student self-regulation abilities and academic performance. It identifies the self-regulation categories requiring the most support to…
Descriptors: Foreign Countries, Higher Education, Preservice Teachers, Learning Analytics
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
Wen-shuang Fu; Jia-hua Zhang; Di Zhang; Tian-tian Li; Min Lan; Na-na Liu – Journal of Educational Computing Research, 2025
Cognitive ability is closely associated with the acquisition of programming skills, and enhancing learners' cognitive ability is a crucial factor in improving the efficacy of programming education. Adaptive feedback strategies can provide learners with personalized support based on their learning context, which helps to stimulate their interest…
Descriptors: Feedback (Response), Cognitive Ability, Programming, Computer Science Education
Kasra Lekan; Zachary A. Pardos – Journal of Learning Analytics, 2025
Choosing an undergraduate major is an important decision that impacts academic and career outcomes. In this work, we investigate augmenting personalized human advising for major selection using a large language model (LLM), GPT-4. Through a three-phase survey, we compare GPT suggestions and responses for undeclared first- and second-year students…
Descriptors: Technology Uses in Education, Artificial Intelligence, Academic Advising, Majors (Students)
Khalid Alalawi; Rukshan Athauda; Raymond Chiong; Ian Renner – Education and Information Technologies, 2025
Learning analytics intervention (LAI) studies aim to identify at-risk students early during an academic term using predictive models and facilitate educators to provide effective interventions to improve educational outcomes. A major impediment to the uptake of LAI is the lack of access to LAI infrastructure by educators to pilot LAI, which…
Descriptors: Intervention, Learning Analytics, Guidelines, Prediction