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Katerina Berková; Martina Chalupová; František Smrcka; Marek Musil; Dagmar Frendlovská – Education and Information Technologies, 2024
Learning analytics dashboards (LADs) are very important tools for contemporary education. Not only researchers, but also schools at different levels of education and students are evaluating in this way today. A large number of studies have addressed the issue, but there are few studies that have explored the possibilities of transferring the…
Descriptors: Learning Analytics, Formative Evaluation, Self Evaluation (Individuals), Universities
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
Serena Lee-Cultura; Kshitij Sharma; Michail N. Giannakos – IEEE Transactions on Learning Technologies, 2024
Teacher dashboards provide insights on students' progress through visualizations and scores derived from data generated during teaching and learning activities (e.g., response times and task correctness) to improve teaching. Despite the potential usefulness of enhancing teacher dashboards, and the respective teaching practices, with rich…
Descriptors: Educational Technology, Learning Analytics, Technology Uses in Education, Student Evaluation
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
Divjak, Blaženka; Svetec, Barbi; Horvat, Damir; Kadoic, Nikola – British Journal of Educational Technology, 2023
To ensure the validity of an assessment programme, it is essential to align it with the intended learning outcomes (LO). We present a model for ensuring assessment validity which supports this constructive alignment and uses learning analytics (LA). The model is based on LA that include a comparison between ideal LO weights (expressing the…
Descriptors: Test Validity, Learning Analytics, Student Centered Learning, Prerequisites
Anni Silvola; Amanda Sjöblom; Piia Näykki; Egle Gedrimiene; Hanni Muukkonen – Frontline Learning Research, 2023
An in-depth understanding of student experiences and evaluations of learning analytics dashboards (LADs) is needed to develop supportive learning analytics tools. This study investigates how students (N = 140) evaluated two student-facing LADs as a support for academic path-level self-regulated learning (SRL) through the concrete processes of…
Descriptors: Learning Analytics, Student Evaluation, Student Experience, Student Attitudes
Darvishi, Ali; Khosravi, Hassan; Sadiq, Shazia; Gaševic, Dragan – British Journal of Educational Technology, 2022
Peer assessment has been recognised as a sustainable and scalable assessment method that promotes higher-order learning and provides students with fast and detailed feedback on their work. Despite these benefits, some common concerns and criticisms are associated with the use of peer assessments (eg, scarcity of high-quality feedback from peer…
Descriptors: Artificial Intelligence, Learning Analytics, Peer Evaluation, Student Evaluation
Onur Karademir; Lena Borgards; Daniele Di Mitri; Sebastian Strauß; Marcus Kubsch; Markus Brobeil; Adrian Grimm; Sebastian Gombert; Nikol Rummel; Knut Neumann; Hendrik Drachsler – Journal of Learning Analytics, 2024
This paper presents a teacher dashboard intervention study in secondary school practice involving teachers (n = 16) with their classes (n = 22) and students (n = 403). A quasi-experimental treatment-control group design was implemented to compare student learning outcomes between classrooms where teachers did not have access to the dashboard and…
Descriptors: Learning Analytics, Intervention, Educational Technology, Secondary School Students
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
Sohum Bhatt; Katrien Verbert; Wim Van Den Noortgate – Journal of Learning Analytics, 2024
Computational thinking (CT) is a concept of growing importance to pre-university education. Yet, CT is often assessed through results, rather than by looking at the CT process itself. Process-based assessments, or assessments that model how a student completed a task, could instead investigate the process of CT as a formative assessment. In this…
Descriptors: Learning Analytics, Student Evaluation, Computation, Thinking Skills
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
Ruiperez-Valiente, Jose A.; Kim, Yoon Jeon; Baker, Ryan S.; Martinez, Pedro A.; Lin, Grace C. – IEEE Transactions on Learning Technologies, 2023
Previous research and experiences have indicated the potential that games have in educational settings. One of the possible uses of games in education is as game-based assessments (GBA), using game tasks to generate evidence about skills and content knowledge that can be valuable. There are different approaches in the literature to implement the…
Descriptors: Affordances, Game Based Learning, Student Evaluation, Multivariate Analysis
Stanislav Pozdniakov; Jonathan Brazil; Mehrnoush Mohammadi; Mollie Dollinger; Shazia Sadiq; Hassan Khosravi – Journal of Learning Analytics, 2025
Engaging students in creating high-quality novel content, such as educational resources, promotes deep and higher-order learning. However, students often lack the necessary training or knowledge to produce such content. To address this gap, this paper explores the potential of incorporating generative AI (GenAI) to review students' work and…
Descriptors: Student Evaluation, Artificial Intelligence, Student Developed Materials, Feedback (Response)
Xu, Liangbei; Davenport, Mark A. – International Educational Data Mining Society, 2020
The goal of knowledge tracing is to track the state of a student's knowledge as it evolves over time. This plays a fundamental role in understanding the learning process and is a key task in the development of an intelligent tutoring system. In this paper we propose a novel approach to knowledge tracing that combines techniques from matrix…
Descriptors: Artificial Intelligence, Learning Analytics, Computer Assisted Instruction, Student Evaluation
Sergio Tirado-Olivares; Rocio Minguez-Pardo; Javier del Olmo-Munoz; Jose A. Gonzalez-Calero – IEEE Transactions on Learning Technologies, 2025
Decimal misconceptions are a persistent challenge in mathematics education, often hindering students' long-term understanding. This study examines how learning analytics (LA) can be effectively integrated into instructional sequences to address these misconceptions, providing teachers with real-time insights for formative assessment. Despite the…
Descriptors: Learning Analytics, Elementary School Students, Elementary School Mathematics, Mathematics Education