Enhancing Cognitive Diagnosis by Modeling Learner Cognitive Structure State
- URL: http://arxiv.org/abs/2412.19759v1
- Date: Fri, 27 Dec 2024 17:41:39 GMT
- Title: Enhancing Cognitive Diagnosis by Modeling Learner Cognitive Structure State
- Authors: Zhifu Chen, Hengnian Gu, Jin Peng Zhou, Dongdai Zhou,
- Abstract summary: Theoretically, an individual's cognitive state is essentially equivalent to their cognitive structure state.
A learner's cognitive structure is essential for promoting meaningful learning and shaping academic performance.
- Score: 3.0103051895985256
- License:
- Abstract: Cognitive diagnosis represents a fundamental research area within intelligent education, with the objective of measuring the cognitive status of individuals. Theoretically, an individual's cognitive state is essentially equivalent to their cognitive structure state. Cognitive structure state comprises two key components: knowledge state (KS) and knowledge structure state (KUS). The knowledge state reflects the learner's mastery of individual concepts, a widely studied focus within cognitive diagnosis. In contrast, the knowledge structure state-representing the learner's understanding of the relationships between concepts-remains inadequately modeled. A learner's cognitive structure is essential for promoting meaningful learning and shaping academic performance. Although various methods have been proposed, most focus on assessing KS and fail to assess KUS. To bridge this gap, we propose an innovative and effective framework-CSCD (Cognitive Structure State-based Cognitive Diagnosis)-which introduces a novel framework to modeling learners' cognitive structures in diagnostic assessments, thereby offering new insights into cognitive structure modeling. Specifically, we employ an edge-feature-based graph attention network to represent the learner's cognitive structure state, effectively integrating KS and KUS. Extensive experiments conducted on real datasets demonstrate the superior performance of this framework in terms of diagnostic accuracy and interpretability.
Related papers
- Concept-Aware Latent and Explicit Knowledge Integration for Enhanced Cognitive Diagnosis [13.781755345440914]
We propose a Concept-aware Latent and Explicit Knowledge Integration model for cognitive diagnosis (CLEKI-CD)
Specifically, a multidimensional vector is constructed according to the students' mastery and exercise difficulty for each knowledge concept from multiple perspectives.
We employ a combined cognitive diagnosis layer to integrate both latent and explicit knowledge, further enhancing cognitive diagnosis performance.
arXiv Detail & Related papers (2025-02-04T08:37:16Z) - Optimizing Student Ability Assessment: A Hierarchy Constraint-Aware Cognitive Diagnosis Framework for Educational Contexts [26.287186220848763]
We propose the Hierarchy Constraint-Aware Cognitive Diagnosis Framework (HCD)
HCD aims to more accurately represent student ability performance within real educational contexts.
It integrates hierarchy constraint perception features with existing models, improving the representation of both individual and group characteristics.
arXiv Detail & Related papers (2024-11-21T11:37:36Z) - Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation [53.70131202548981]
We present a two-step segmentation framework employing Knowledge-Guided Prompt Learning (KGPL) for brain MRI.
Specifically, we first pre-train segmentation models on large-scale datasets with sub-optimal labels.
The introduction of knowledge-wise prompts captures semantic relationships between anatomical variability and biological processes.
arXiv Detail & Related papers (2024-07-31T04:32:43Z) - A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions [66.40362209055023]
This paper aims to provide a survey of current models for cognitive diagnosis, with more attention on new developments using machine learning-based methods.
By comparing the model structures, parameter estimation algorithms, model evaluation methods and applications, we provide a relatively comprehensive review of the recent trends in cognitive diagnosis models.
arXiv Detail & Related papers (2024-07-07T18:02:00Z) - ReliCD: A Reliable Cognitive Diagnosis Framework with Confidence
Awareness [26.60714613122676]
Existing approaches often suffer from the issue of overconfidence in predicting students' mastery levels.
We propose a novel Reliable Cognitive Diagnosis(ReliCD) framework, which can quantify the confidence of the diagnosis feedback.
arXiv Detail & Related papers (2023-12-29T07:30:58Z) - Exploring the Cognitive Knowledge Structure of Large Language Models: An
Educational Diagnostic Assessment Approach [50.125704610228254]
Large Language Models (LLMs) have not only exhibited exceptional performance across various tasks, but also demonstrated sparks of intelligence.
Recent studies have focused on assessing their capabilities on human exams and revealed their impressive competence in different domains.
We conduct an evaluation using MoocRadar, a meticulously annotated human test dataset based on Bloom taxonomy.
arXiv Detail & Related papers (2023-10-12T09:55:45Z) - Multi-task Collaborative Pre-training and Individual-adaptive-tokens
Fine-tuning: A Unified Framework for Brain Representation Learning [3.1453938549636185]
We propose a unified framework that combines Collaborative pre-training and Individual--Tokens fine-tuning.
The proposed MCIAT achieves state-of-the-art diagnosis performance on the ADHD-200 dataset.
arXiv Detail & Related papers (2023-06-20T08:38:17Z) - UIILD: A Unified Interpretable Intelligent Learning Diagnosis Framework
for Intelligent Tutoring Systems [8.354034992258482]
The proposed unified interpretable intelligent learning diagnosis (UIILD) framework benefits from the powerful representation learning ability of deep learning and the interpretability of psychometrics.
Within the proposed framework, this paper presents a two-channel learning diagnosis mechanism LDM-ID as well as a three-channel learning diagnosis mechanism LDM-HMI.
arXiv Detail & Related papers (2022-07-07T07:04:22Z) - Kernel Based Cognitive Architecture for Autonomous Agents [91.3755431537592]
This paper considers an evolutionary approach to creating a cognitive functionality.
We consider a cognitive architecture which ensures the evolution of the agent on the basis of Symbol Emergence Problem solution.
arXiv Detail & Related papers (2022-07-02T12:41:32Z) - Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization [49.00409552570441]
We study the role of conceptualization in commonsense reasoning, and formulate a framework to replicate human conceptual induction.
We apply the framework to ATOMIC, a large-scale human-annotated CKG, aided by the taxonomy Probase.
arXiv Detail & Related papers (2022-06-03T12:24:49Z) - CogNGen: Constructing the Kernel of a Hyperdimensional Predictive
Processing Cognitive Architecture [79.07468367923619]
We present a new cognitive architecture that combines two neurobiologically plausible, computational models.
We aim to develop a cognitive architecture that has the power of modern machine learning techniques.
arXiv Detail & Related papers (2022-03-31T04:44:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.