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Kim, Yunsung; Sreechan; Piech, Chris; Thille, Candace – International Educational Data Mining Society, 2023
Dynamic Item Response Models extend the standard Item Response Theory (IRT) to capture temporal dynamics in learner ability. While these models have the potential to allow instructional systems to actively monitor the evolution of learner proficiency in real time, existing dynamic item response models rely on expensive inference algorithms that…
Descriptors: Item Response Theory, Accuracy, Inferences, Algorithms
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
Hua Ma; Wen Zhao; Yuqi Tang; Peiji Huang; Haibin Zhu; Wensheng Tang; Keqin Li – IEEE Transactions on Learning Technologies, 2024
To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to students by analyzing their interactions through an e-learning system. In existing research, the correlations between learning risks and students' changing cognitive abilities or…
Descriptors: College Students, Learning Analytics, Learning Management Systems, Academic Achievement
Kil, David; Baldasare, Angela; Milliron, Mark – Current Issues in Education, 2021
Student success, both during and after college, is central to the mission of higher education. Within the higher-education and, more specifically, the student-success context, the core raison d'ĂȘtre of machine learning (ML) is to help institutions achieve their social mission in an efficient and effective manner. While there should be synergy…
Descriptors: Learning Analytics, Academic Achievement, College Students, Electronic Learning
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
Francesca Gottschalk; Crystal Weise – OECD Publishing, 2023
Digital technologies can be used to support the inclusion of diverse student groups in education in a number of ways including enhancing accessibility of educational content, increasing personalisation and providing distance learning opportunities, as was the case during the COVID-19 pandemic. However, persistent digital inequalities can undermine…
Descriptors: Access to Computers, Educational Technology, Inclusion, Equal Education