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）
Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts. Existing approaches usually mine linear interactions of student exercising process by manual-designed function (e.g., logistic function), which is not sufficient for capturing complex relations between students and exercises. In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework, which incorporates neural networks to learn the complex exercising interactions, for getting both accurate and interpretable diagnosis results. Specifically, we project students and exercises to factor vectors and leverage multi neural layers for modeling their interactions, where the monotonicity assumption is applied to ensure the interpretability of both factors. Furthermore, we propose two implementations of NeuralCD by specializing the required concepts of each exercise, i.e., the NeuralCDM with traditional Q-matrix and the improved NeuralCDM+ exploring the rich text content. Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability.
Anhui University（安徽大学）；University of Science and Technology of China（中国科学技术大学）
Cognitive diagnosis is a fundamental yet critical research task in the field of intelligent education, which aims to discover the proficiency level of different students on specific knowledge concepts. Despite the effectiveness of existing efforts, previous methods always considered the mastery level on the whole students, so they still suffer from the Long Tail Effect. A large number of students who have sparse data are performed poorly in the model. To relieve the situation, we proposed a Self-supervised Cognitive Diagnosis (SCD) framework which leverages the self-supervised manner to assist…
Center for Advanced Technology and Education（先进技术与教育中心）；Florida International University（佛罗里达国际大学）
Multiple modalities of biomarkers have been proved to be very sensitive in assessing the progression of Alzheimer's disease (AD), and using these modalities and machine learning algorithms, several approaches have been proposed to assist in the early diagnosis of AD. Among the recent investigated state-of-the-art approaches, Gaussian discriminant analysis (GDA)-based approaches have been demonstrated to be more effective and accurate in the classification of AD, especially for delineating its prodromal stage of mild cognitive impairment (MCI). Moreover, among those binary classification invest…
Unified Uncertainty Estimation for Cognitive Diagnosis Models
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）；State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China（中国科学技术大学安徽省大数据分析与应用重点实验室）；The University of Tennessee（田纳西大学）；iFLYTEK Research（讯飞研究院）；iFLYTEK AI Research (Central China)（讯飞人工智能研究（华中））
Prompt Transfer for Dual-Aspect Cross Domain Cognitive Diagnosis
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China)（大数据知识工程教育部重点实验室）；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）；School of Big Data And Artificial Intelligence, Hefei University（合肥学院大数据与人工智能学院）
University of Science and Technology of China（中国科学技术大学）
Cognitive diagnosis is an essential task in many educational applications. Many solutions have been designed in the literature. The deterministic input, noisy "and" gate (DINA) model is a classical cognitive diagnosis model and can provide interpretable cognitive parameters, e.g., student vectors. However, the assumption of the probabilistic part of DINA is too strong, because it assumes that the slip and guess rates of questions are student-independent. Besides, the question matrix (i.e., Q-matrix) recording the skill distribution of the questions in the cognitive diagnosis domain often requi…
Multilayer Q-Matrix-Embedded Neural Network for Cognitive Diagnosis (M-QCDNet): Structure-Aware Deep Learning Architecture for Psychometric Interpretability
Language Representation Favored Zero-Shot Cross-Domain Cognitive Diagnosis
Shanghai Institute of AI Education（上海人工智能教育研究院）；School of Computer Science and Technology（计算机科学与技术学院）；East China Normal University（华东师范大学）；Department of Educational Psychology（教育心理学系）；Faculty of Education（教育学院）
Concept-Aware Latent and Explicit Knowledge Integration for Enhanced Cognitive Diagnosis
School of Information Science and Engineering, Shandong Normal University（山东师范大学信息科学与工程学院）；School of Journalism and Communication, Shandong Normal University（山东师范大学新闻与传媒学院）
Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
CogTax: A Four-Level Cognitive Taxonomy for Command-Line Computing Education
Universidade de Vigo（维戈大学）；IFCAE-Instituto de Investigación en Física, Computación y Ciencia Aeroespacial, Universidade de Vigo（IFCAE-维戈大学物理、计算与航空航天科学研究所）；Computer Science Department, IFCAE-Instituto de Investigación en Física, Computación y Ciencia Aeroespacial, Universidade de Vigo（维戈大学IFCAE物理、计算与航空航天科学研究所计算机科学系）；Universidade de Vigo, Department of Computer Science, ESEI-Escuela Superior de Ingeniería Informática（维戈大学计算机科学系ESEI高等信息工程学院）
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis
Faculty of Electrical and Computer Engineering, National Technical University of Athens（雅典国立技术大学电气与计算机工程学院）；Center for Biomedical Image Computing and Analytics, University of Pennsylvania（宾夕法尼亚大学生物医学图像计算与分析中心）
Student diversity, like academic background, learning styles, career and life goals, ethnicity, age, social and emotional characteristics, course load and work schedule, offers unique opportunities in education, like learning new skills, peer mentoring and example setting. But student diversity can be challenging too as it adds variability in the way in which students learn and progress over time. A single teaching approach is likely to be ineffective and result in students not meeting their potential. Automated support could address limitations of traditional teaching by continuously assessin…
Quantum Sparse Autoencoders for Q-Matrix Estimation in Cognitive Diagnosis
School of Computer and Cyber Sciences, Augusta University（奥古斯塔大学计算机与网络科学学院）；School of Computing, University of Georgia（佐治亚大学计算学院）；Department of Graduate Psychology, James Madison University（詹姆斯麦迪逊大学研究生心理学系）；Department of Computer Science and Engineering, University of Tennessee at Chattanooga（田纳西大学查塔努加分校计算机科学与工程系）；Department of Radiology, Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院放射科）
DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
Anhui University（安徽大学）；Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education（教育部智能计算与信号处理重点实验室）；Anhui Province Key Laboratory of Intelligent Computing and Applications（安徽省智能计算与应用重点实验室）；Department of Information Materials and Intelligent Sensing Laboratory of Anhui Province（安徽省信息材料与智能感知实验室）
Connected Speech-Based Cognitive Assessment in Chinese and English
Usher Institute, Medical School（医学院厄舍研究所）；School of Health in Social Science（社会科学健康学院）；School of Engineering, University of Edinburgh（爱丁堡大学工程学院）；Carnegie Mellon University（卡内基梅隆大学）；University of Delaware（特拉华大学）；Cardinal Tien Hospital（圣保禄医院）
Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；Institute of Dataspace, Hefei Comprehensive National Science Center（合肥综合性国家科学中心数据空间研究院）
Lifespan tree of brain anatomy: diagnostic values for motor and cognitive neurodegenerative diseases
CNRS（法国国家科学研究中心）；Univ. Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立综合理工学院）；LABRI（计算机科学、通信与图像处理实验室）；ITACA（ITACA机构）；Universitat Politècnica de València（瓦伦西亚理工大学）；ToNIC, Toulouse NeuroImaging Center（图卢兹神经影像中心）；Université de Toulouse（图卢兹大学）；Inserm（法国国家健康与医学研究院）；UPS（图卢兹第三大学）；CHU Bordeaux（波尔多大学医院）；IMNc（认知神经影像与神经疾病研究所）；University of Otago（奥塔哥大学）；New Zealand Brain Research Institute（新西兰脑研究所）；Neurocentre Magendie（马让迪神经中心）；CHU de Bordeaux（波尔多大学医院）
Interpreting Learning Under Competing Models: Joint and Stepwise Approaches for Dynamic Cognitive Diagnosis
School of Mathematical Sciences, Lancaster University（兰卡斯特大学数学科学学院）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Department of Psychology, Lancaster University（兰卡斯特大学心理学系）
The Ohio State University（俄亥俄州立大学）；University of Pennsylvania（宾夕法尼亚大学）；Indiana University（印第安纳大学）
Background:Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer's Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method: We adapt a newly developed lear…
Lehigh University（理海大学）；University of Pittsburgh（匹兹堡大学）；The Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation informati…
The emergence of large scaled sensor networks facilitates the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or un-formulated. In this paper, we have developed an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of in the signal space. Learning in the model space is implemented by fitting a series of models using …
Ludwig-Maximilians-Universität（路德维希-马克西米利安大学）；Indiana University School of Medicine（印第安纳大学医学院）
The current state-of-the-art deep neural networks (DNNs) for Alzheimer's Disease diagnosis use different biomarker combinations to classify patients, but do not allow extracting knowledge about the interactions of biomarkers. However, to improve our understanding of the disease, it is paramount to extract such knowledge from the learned model. In this paper, we propose a Deep Factorization Machine model that combines the ability of DNNs to learn complex relationships and the ease of interpretability of a linear model. The proposed model has three parts: (i) an embedding layer to deal with spar…
Massachusetts General Hospital（麻省总医院）；McCance Center for Brain Health（McCance脑健康中心）
Dementia is a neurodegenerative disorder that causes cognitive decline and affects more than 50 million people worldwide. Dementia is under-diagnosed by healthcare professionals - only one in four people who suffer from dementia are diagnosed. Even when a diagnosis is made, it may not be entered as a structured International Classification of Diseases (ICD) diagnosis code in a patient's charts. Information relevant to cognitive impairment (CI) is often found within electronic health records (EHR), but manual review of clinician notes by experts is both time consuming and often prone to errors.…
