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Titlebook: Generative Intelligence and Intelligent Tutoring Systems; 20th International C Angelo Sifaleras,Fuhua Lin Conference proceedings 2024 The E

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樓主: 孵化
11#
發(fā)表于 2025-3-23 13:45:35 | 只看該作者
12#
發(fā)表于 2025-3-23 17:19:11 | 只看該作者
Cranial, Craniofacial and Skull Base Surgeryization and design of a variety of learning tools, with particular interest given to digital games. Several studies are investigating their effectiveness in learning CT, however more research is needed on the specific features of these tools, such as scaffolding features. This study evaluates a scaf
13#
發(fā)表于 2025-3-23 19:14:59 | 只看該作者
Cranio-Spinal Surgery with the Ronjair?onses. Most deep learning-based KT models have suffered from attributions of KT datasets such as the data sparsity, changeability of the knowledge state, and educational domain. Recently, most KT models use attention mechanisms to solve these problems. However, few studies tried to redesign the atte
14#
發(fā)表于 2025-3-24 02:02:25 | 只看該作者
15#
發(fā)表于 2025-3-24 03:24:38 | 只看該作者
https://doi.org/10.1007/978-1-4612-2466-2iate qualitative measures is proposed to extract behavioral sequences that are representative of learning success. Applied on an online programming platform, obtained results allowed to highlight important self-regulation behaviors during the planning and engagement phases. It e.g. appears that succ
16#
發(fā)表于 2025-3-24 07:01:33 | 只看該作者
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發(fā)表于 2025-3-24 14:37:44 | 只看該作者
18#
發(fā)表于 2025-3-24 18:38:17 | 只看該作者
Karin De La Fuente,Kevin E. Bright To achieve this goal, many researchers have proposed KT models that use data from Intelligent Tutoring Systems (ITS) to predict students’ subsequent actions. However, with the development of ITS, large-scale datasets containing long-sequence data began to emerge. Recent deep learning based KT model
19#
發(fā)表于 2025-3-24 22:46:29 | 只看該作者
20#
發(fā)表于 2025-3-25 01:34:59 | 只看該作者
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