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Titlebook: Artificial Intelligence in Education; 21st International C Ig Ibert Bittencourt,Mutlu Cukurova,Eva Millán Conference proceedings 2020 Sprin

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11#
發(fā)表于 2025-3-23 12:37:57 | 只看該作者
Exploring Automatic Short Answer Grading as a Tool to Assist in Human Rating and in turn, have reduced the amount of human annotation necessary for producing a high-quality classification model. After training BERT on expert ratings of constructed responses, we use subsequent automated grading to calculate Cohen’s Kappa as a measure of inter-rater reliability between the au
12#
發(fā)表于 2025-3-23 14:35:56 | 只看該作者
Multi-document Cohesion Network Analysis: Visualizing Intratextual and Intertextual Linksce levels displays links both within and between documents, thus ensuring ease of visualization for links spanning across multiple documents. Two use cases are provided to evaluate key functionalities and insights for each type of visualization.
13#
發(fā)表于 2025-3-23 22:01:54 | 只看該作者
14#
發(fā)表于 2025-3-24 00:01:37 | 只看該作者
Interactive Pedagogical Agents for Learning Sequence Diagramsiagrams. Providing manual timely feedback, though effective, cannot scale for large classes. Our pedagogical agent combining data dependencies and quality metrics with rule-based techniques capturing consistency constraints allowed generation of immediate and holistic feedback. The scaffolding appro
15#
發(fā)表于 2025-3-24 04:16:23 | 只看該作者
A Socratic Tutor for Source Code Comprehension learning computer programming. The result shows there are significant differences between the two groups where students who used Socratic Tutor ITS improved their knowledge by 45% in term of learning gain, developed a better understanding of concepts such as nested if-else and for loop, and improve
16#
發(fā)表于 2025-3-24 09:32:50 | 只看該作者
Scientific Modeling Using Large Scale Knowledge of Life. Learners can use VERA to construct conceptual models of ecological phenomena, run them as simulations, and review their predictions. A study on the use of VERA by college-level students indicates that providing access to large scale but contextualized knowledge helped students build more c
17#
發(fā)表于 2025-3-24 12:44:56 | 只看該作者
18#
發(fā)表于 2025-3-24 17:33:03 | 只看該作者
19#
發(fā)表于 2025-3-24 22:52:52 | 只看該作者
The Potential for the Use of Deep Neural Networks in e-Learning Student Evaluation with New Data Augsonalized learning path through the e-learning system. Assessing student progress at each stage of learning in an individualized process is extremely tedious and arduous. The only solution is to automate assessment using Deep Learning methods. The obstacle is the relatively small amount of data, in
20#
發(fā)表于 2025-3-24 23:47:36 | 只看該作者
Investigating Transformers for Automatic Short Answer Gradinghe Transformer, for increasingly complex natural language processing tasks. Combined with novel unsupervised pre-training tasks such as masked language modeling, sentence ordering or next sentence prediction, those natural language processing models became even more accurate. In this work, we experi
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