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Sonsoles Lopez-Pernas; Kamila Misiejuk; Rogers Kaliisa; Mohammed Saqr – IEEE Transactions on Learning Technologies, 2025
Despite the growing use of large language models (LLMs) in educational contexts, there is no evidence on how these can be operationalized by students to generate custom datasets suitable for teaching and learning. Moreover, in the context of network science, little is known about whether LLMs can replicate real-life network properties. This study…
Descriptors: Students, Artificial Intelligence, Man Machine Systems, Interaction
Zhongcheng Lei; Hong Zhou; Wenshan Hu; Guo-Ping Liu – IEEE Transactions on Learning Technologies, 2024
Online laboratories have been widely used in education, research, and industrial applications. For online laboratories, various architectures have been constructed to provide good user experience and powerful capabilities for online experimentation, in which the controller is a crucial part to connect the server with a controlled test rig.…
Descriptors: Online Courses, Laboratories, Distance Education, Computers
Yuang Wei; Bo Jiang – IEEE Transactions on Learning Technologies, 2024
Understanding student cognitive states is essential for assessing human learning. The deep neural networks (DNN)-inspired cognitive state prediction method improved prediction performance significantly; however, the lack of explainability with DNNs and the unitary scoring approach fail to reveal the factors influencing human learning. Identifying…
Descriptors: Cognitive Mapping, Models, Prediction, Short Term Memory
Emiko Tsutsumi; Yiming Guo; Ryo Kinoshita; Maomi Ueno – IEEE Transactions on Learning Technologies, 2024
Knowledge tracing (KT), the task of tracking the knowledge state of a student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines item response theory (IRT) with a deep learning method, provides superior performance. It can express the abilities of each student…
Descriptors: Item Response Theory, Academic Ability, Intelligent Tutoring Systems, Artificial Intelligence