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Nuo Cheng; Wei Zhao; Xiaoqing Xu; Hongxia Liu; Jinhong Tao – Education and Information Technologies, 2024
Learning analytics dashboards are becoming increasingly common tools for providing feedback to learners. However, there is limited empirical evidence regarding the effects of learning analytics dashboard design features on learners' cognitive load, particularly in digital learning environments. To address this gap, we developed goal-based,…
Descriptors: Learning Analytics, Learning Management Systems, Cognitive Ability, Online Courses
Han, Insook; Obeid, Iyad; Greco, Devon – Technology, Knowledge and Learning, 2023
This report describes the use of electroencephalography (EEG) to collect online learners' physiological information. Recent technological advancements allow the unobtrusive collection of live neurosignals while learners are engaged in online activities. In the context of multimodal learning analytics, we discuss the potential use of this new…
Descriptors: Learning Analytics, Diagnostic Tests, Metacognition, Brain Hemisphere Functions
Plintz, Nicolai; Ifenthaler, Dirk – International Association for Development of the Information Society, 2023
Emotions are vital to learning success, especially in online learning environments. They make the difference between learning success and failure. Unfortunately, learners' emotional state is still rarely considered in online learning and teaching, although it is an important driver of learning success. This paper reports a work-in-progress…
Descriptors: Online Courses, Academic Achievement, Emotional Experience, Measurement
Chun Yan Enoch Sit; Siu-Cheung Kong – Journal of Educational Computing Research, 2024
Educational process mining aims (EPM) to help teachers understand the overall learning process of their students. Although deep learning models have shown promising results in many domains, the event log dataset in many online courses may not be large enough for deep learning models to approximate the probability distribution of students' learning…
Descriptors: Learning Processes, Learning Analytics, Algorithms, Guidelines
Sonja Kleter; Uwe Matzat; Rianne Conijn – IEEE Transactions on Learning Technologies, 2024
Much of learning analytics research has focused on factors influencing model generalizability of predictive models for academic performance. The degree of model generalizability across courses may depend on aspects, such as the similarity of the course setup, course material, the student cohort, or the teacher. Which of these contextual factors…
Descriptors: Prediction, Models, Academic Achievement, Learning Analytics
Kim, Hodam; Chae, Younsoo; Kim, Suhye; Im, Chang-Hwan – IEEE Transactions on Learning Technologies, 2023
Owing to the rapid development of information and communication technologies, online or mobile learning content is widely available on the Internet. Unlike traditional face-to-face learning, online learning exhibits a critical limitation: real-time interactions between learners and teachers are generally not feasible in online learning. To…
Descriptors: College Students, Control Groups, Attention, Comprehension
Floris, Francesco; Marchisio, Marina; Roman, Fabio; Sacchet, Matteo; Rabellino, Sergio – International Association for Development of the Information Society, 2022
Among the various kinds of learning analytics emerging especially in the latest decade, clicking patterns cover a prominent role, fostered by their success in analyzing several types of data concerning activity on the web. They can be defined as sets of clicks performed by users, in which every set is treated as the basic unit. Few research has…
Descriptors: Learner Engagement, Mathematics Instruction, Units of Study, Teaching Methods
Benjamin A. Motz; Öykü Üner; Harmony E. Jankowski; Marcus A. Christie; Kim Burgas; Diego del Blanco Orobitg; Mark A. McDaniel – Grantee Submission, 2023
For researchers seeking to improve education, a common goal is to identify teaching practices that have causal benefits in classroom settings. To test whether an instructional practice exerts a causal influence on an outcome measure, the most straightforward and compelling method is to conduct an experiment. While experimentation is common in…
Descriptors: Learning Analytics, Experiments, Learning Processes, Learning Management Systems
Jamal Eddine Rafiq; Abdelali Zakrani; Mohammed Amraouy; Said Nouh; Abdellah Bennane – Turkish Online Journal of Distance Education, 2025
The emergence of online learning has sparked increased interest in predicting learners' academic performance to enhance teaching effectiveness and personalized learning. In this context, we propose a complex model APPMLT-CBT which aims to predict learners' performance in online learning settings. This systemic model integrates cognitive, social,…
Descriptors: Models, Online Courses, Educational Improvement, Learning Processes
Tran, Tuan M.; Hasegawa, Shinobu – International Association for Development of the Information Society, 2022
A learner model reflects learning patterns and characteristics of a learner. A learner model with learning history and its effectiveness plays a significant role in supporting a learner's understanding of their strengths and weaknesses of their way of learning in order to make proper adjustments for improvement. Nowadays, learners have been…
Descriptors: Markov Processes, Learning Processes, Models, Scores
Montse Guitert Catasús; Teresa Romeu Fontanillas; Juliana E. Raffaghelli; Juan Pedro Cerro Martínez – Journal of Learning Analytics, 2025
This article systematically reviews the role of learning analytics (LA) in collaborative learning, particularly exploring how it can empower both teachers and students. Based on the analysis of 87 articles, selected by adopting the PRISMA workflow, the study discusses the intersection of LA with collaborative learning (CL), emphasizing the…
Descriptors: Learning Analytics, Teacher Empowerment, Student Empowerment, Cooperative Learning
Rizvi, Saman; Rienties, Bart; Rogaten, Jekaterina; Kizilcec, René F. – Journal of Computing in Higher Education, 2020
Studies on engagement and learning design in Massive Open Online Courses (MOOCs) have laid the groundwork for understanding how people learn in this relatively new type of informal learning environment. To advance our understanding of how people learn in MOOCs, we investigate the intersection between learning design and the temporal process of…
Descriptors: Online Courses, Learning Processes, Science Education, Learner Engagement
Gurcan, Fatih; Ozyurt, Ozcan; Cagiltay, Nergiz Ercil – International Review of Research in Open and Distributed Learning, 2021
E-learning studies are becoming very important today as they provide alternatives and support to all types of teaching and learning programs. The effect of the COVID-19 pandemic on educational systems has further increased the significance of e-learning. Accordingly, gaining a full understanding of the general topics and trends in e-learning…
Descriptors: Educational Trends, Electronic Learning, Models, Learning Analytics
Stanislav Pozdniakov; Roberto Martinez-Maldonado; Yi-Shan Tsai; Vanessa Echeverria; Zachari Swiecki; Dragan Gaševic – Journal of Learning Analytics, 2025
Recent research on learning analytics dashboards has focused on designing user interfaces that offer various forms of "visualization guidance" (often referring to notions such as "data storytelling" or "narrative visualization") to teachers (e.g., emphasizing data points or trends with colour and adding annotations),…
Descriptors: Visual Aids, Learning Analytics, Technological Literacy, Pedagogical Content Knowledge
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