A cognitive model of human learning provides information about skills a learner must acquire to perform accurately in a task domain. Cognitive models of learning are not only of scientific interest, but are also valuable in adaptive online tutoring systems. A more accurate model yields more effective tutoring through better instructional decisions. Prior methods of automated cognitive model discovery have typically focused on well-structured domains, relied on student performance data or involved substantial human knowledge engineering. In this paper, we propose Cognitive Representation Learner (CogRL), a novel framework to learn accurate cognitive models in ill-structured domains with no data and little to no human knowledge engineering. Our contribution is two-fold: firstly, we show that representations learnt using CogRL can be used for accurate automatic cognitive model discovery without using any student performance data in several ill-structured domains: Rumble Blocks, Chinese Character, and Article Selection. This is especially effective and useful in domains where an accurate human-authored cognitive model is unavailable or authoring a cognitive model is difficult. Secondly, for domains where a cognitive model is available, we show that representations learned through CogRL can be used to get accurate estimates of skill difficulty and learning rate parameters without using any student performance data. These estimates are shown to highly correlate with estimates using student performance data on an Article Selection dataset.
Latent Variable Sequence Identification for Cognitive Models with Neural Network Estimators
Department of Psychology, University of California, Berkeley,Helen Wills Neuroscience Institute, University of California, Berkeley（心理学系，加州大学伯克利分校，Helen Wills神经科学研究所，加州大学伯克利分校）
Neural Cognitive Diagnosis for Intelligent Education Systems
Anhui Province Key Lab. of Big Data Analysis and Application（安徽省大数据分析与应用重点实验室）；School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；iFLYTEK Research（科大讯飞研究院）
Multilayer Q-Matrix-Embedded Neural Network for Cognitive Diagnosis (M-QCDNet): Structure-Aware Deep Learning Architecture for Psychometric Interpretability
Improving Prediction of Cognitive Performance using Deep Neural Networks in Sparse Data
Columbia University（哥伦比亚大学）；Columbia University Medical Center（哥伦比亚大学医学中心）
中年的认知能力是与年龄相关的智力衰退的重要预测因素，预测认知能力的统计模型可以用于预测衰退。然而，现有的模型很难捕捉影响认知的物理，社会人口，心理和精神健康因素之间的复杂关系。使用来自一项观察性队列研究--美国中年（MIDUS）的数据，我们对大量变量进行建模，以预测执行功能和情景记忆指标。我们使用了不同稀疏度或缺失数据量的横截面和纵向结果。深度神经网络（DNN）模型在所有认知表现预测任务中始终排名最高，如对样本外数据的均方根误差（RMSE）所评估的那样。DNN和其他模型类型之间的RMSE差异具有统计学显著性（T（8）= -3.70; p < 0.05）。模型类型和稀疏性之间的相互作用效应是显著的（F（9）=59.20; p < 0.01），表明DNN的成功部分归因于它们的鲁棒性和对健康相关因素之间的层次关系建模的能力。我们的研究结果强调了神经网络对临床数据集建模的潜力，并允许更好地理解导致认知能力下降的因素。
CogGNN: Cognitive Graph Neural Networks in Generative Connectomics
National Engineering School of Sousse, University of Sousse, LATIS- Laboratory of Advanced Technology and Intelligent Systems（突尼斯苏塞国立工程学校，苏塞大学，先进技术与智能系统实验室）；BASIRA Lab, Imperial-X（BASIRA实验室，Imperial-X）；Department of Computing, Imperial College London, UK（计算系，伦敦帝国学院，英国）
Transforming Traditional Neural Networks into Neuromorphic Quantum-Cognitive Models: A Tutorial with Applications
Artificial Intelligence and Cyber Futures Institute, Charles Sturt University（查尔斯斯特大学人工智能与网络未来学院）；Cyber Futures Institute, Charles Sturt University（查尔斯斯特大学网络未来学院）
Bridging LSTM Architecture and the Neural Dynamics during Reading
Shanghai Key Laboratory of Intelligent Information Processing, Fudan University（复旦大学上海市智能信息处理重点实验室）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）
Predicting cognitive load in immersive driving scenarios with a hybrid CNN-RNN model
Institute for Intelligent Systems Research and Innovation（智能系统研究与创新研究所）；Faculty of Computing and Information Technology (FoCIT)（计算与信息科技学院）；Swinburne University of Technology（斯威丁大学）
