Titlebook: Artificial Intelligence in Education; 22nd International C Ido Roll,Danielle McNamara,Vania Dimitrova Conference proceedings 2021 Springer

PHONE

Vida Česnuitytė,Detlev Lück,Eric D. Widmerere representative of student performance from the training dataset, while the GAN trained on all features was not able to capture characteristics from the dataset. Based on the results, the synthetic dataset can provide an alternative unrestricted source of data without compromising student privacy.

Tailor

衰老

critique

Flawless

BLAZE

Generation of Automatic Data-Driven Feedback to Students Using Explainable Machine Learningautomatically provides data-driven recommendations for action. The underlying predictive model effectiveness of the proposed approach is evaluated, with the results demonstrating 90 per cent accuracy.

物种起源

Protecting Student Privacy with Synthetic Data from Generative Adversarial Networksere representative of student performance from the training dataset, while the GAN trained on all features was not able to capture characteristics from the dataset. Based on the results, the synthetic dataset can provide an alternative unrestricted source of data without compromising student privacy.

Venules

VALID

itinerary

Conference proceedings 2021tion, AIED 2021, held in Utrecht, The Netherlands, in June 2021.*.The 40 full papers presented together with 76 short papers, 2 panels papers, 4 industry papers, 4 doctoral consortium, and 6 workshop papers were carefully reviewed and selected from 209 submissions. The conference provides opportunit