Cognitive Systems in Education Sector
Cognitive systems that understand learners and adapt teaching, feedback and assessment to them.
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Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features
CSK Dash, A Kumar Behera, S Dehuri, SB Cho. Cited by 21
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Co-designing artificial intelligence curriculum for secondary schools: A grounded theory of teachers' experience
KW Yau, CS Chai, TKF Chiu, H Meng, I King, SWH Wong, Y Yam. Cited by 29
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Using online food delivery applications during the COVID-19 lockdown period: What drives University Students’ satisfaction and loyalty?
D Pal, S Funilkul, W Eamsinvattana, S Siyal. Cited by 144
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Evaluating the intention for the adoption of artificial intelligence-based robots in the university to educate the students
R Roy, MD Babakerkhell, S Mukherjee, D Pal, S Funilkul. Cited by 146
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Using machine learning as a surrogate model for agent-based simulations
C Angione, E Silverman, E Yaneske. Cited by 157
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Cognitive Systems in Education Sector
Cognitive systems that understand learners and adapt teaching, feedback and assessment to them.
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Reshaping Human Factors Education in Times of Big Data: Practitioner Perspectives
BY Zhang, M Chignell. Cited by 1
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Student Perspectives on Changing Requirements for Human Factors Engineering Education
BY Zhang, EM Rantanen, M Chignell. Cited by 1
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Handwritten Pashto Characters Dataset for Optical Character Recognition
W Mudaser, JH Chan. Cited by 2
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DeepCURATER: Deep Learning for CoURse And Teaching Evaluation and Review
Z Hu, B Thumu, Y Qin, ZS Tao, Y Lu, O Kan, Y Li, I King. Cited by 2
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Hand Foot and Mouth Rash Detection Using Deep Convolution Neural Network
N Vakili, JH Chan, W Krathu, N Phattarakijtham, K Hirata. Cited by 7
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Learning by distillation: a self-supervised learning framework for optical flow estimation
P Liu, MR Lyu, I King, J Xu. Cited by 12