A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments

• URL: http://arxiv.org/abs/2410.15054v1
• Date: Sat, 19 Oct 2024 10:12:02 GMT
• Title: A Dual-Fusion Cognitive Diagnosis Framework for Open Student Learning Environments
• Authors: Yuanhao Liu, Shuo Liu, Yimeng Liu, Jingwen Yang, Hong Qian,
• Abstract summary: This paper proposes a dual-fusion cognitive diagnosis framework (DFCD) to address the challenge of aligning two different modalities. Experiments show that DFCD achieves superior performance by integrating different modalities and strong adaptability in open student learning environments.
• Score: 10.066184572184627
• License:
• Abstract: Cognitive diagnosis model (CDM) is a fundamental and upstream component in intelligent education. It aims to infer students' mastery levels based on historical response logs. However, existing CDMs usually follow the ID-based embedding paradigm, which could often diminish the effectiveness of CDMs in open student learning environments. This is mainly because they can hardly directly infer new students' mastery levels or utilize new exercises or knowledge without retraining. Textual semantic information, due to its unified feature space and easy accessibility, can help alleviate this issue. Unfortunately, directly incorporating semantic information may not benefit CDMs, since it does not capture response-relevant features and thus discards the individual characteristics of each student. To this end, this paper proposes a dual-fusion cognitive diagnosis framework (DFCD) to address the challenge of aligning two different modalities, i.e., textual semantic features and response-relevant features. Specifically, in DFCD, we first propose the exercise-refiner and concept-refiner to make the exercises and knowledge concepts more coherent and reasonable via large language models. Then, DFCD encodes the refined features using text embedding models to obtain the semantic information. For response-related features, we propose a novel response matrix to fully incorporate the information within the response logs. Finally, DFCD designs a dual-fusion module to merge the two modal features. The ultimate representations possess the capability of inference in open student learning environments and can be also plugged in existing CDMs. Extensive experiments across real-world datasets show that DFCD achieves superior performance by integrating different modalities and strong adaptability in open student learning environments.

Related papers

• End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial [11.670969577565774]
  This paper proposes an End-to-end Graph Neural Networks-based Cognitive Diagnosis (EGNN-CD) model. EGNN-CD consists of three main parts: knowledge concept network (KCN), graph neural networks-based feature extraction (GNNFE), and cognitive ability prediction (CAP)
  arXiv  Detail & Related papers  (2024-10-30T06:18:47Z)
• Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification [49.41632476658246]
  We discuss the extension of DFKD to Vision-Language Foundation Models without access to the billion-level image-text datasets. The objective is to customize a student model for distribution-agnostic downstream tasks with given category concepts. We propose three novel Prompt Diversification methods to encourage image synthesis with diverse styles.
  arXiv  Detail & Related papers  (2024-07-21T13:26:30Z)
• ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education Systems [14.453681018572977]
  Cognitive diagnosis models (CDMs) are designed to learn students' mastery levels using their response logs. Despite the success achieved by existing CDMs, we find that they suffer from a thorny issue that the learned students' mastery levels are too similar. This paper proposes an oversmoothing-resistant cognitive diagnosis framework (ORCDF) to enhance existing CDMs.
  arXiv  Detail & Related papers  (2024-06-28T16:42:53Z)
• MindFormer: Semantic Alignment of Multi-Subject fMRI for Brain Decoding [50.55024115943266]
  We introduce a novel semantic alignment method of multi-subject fMRI signals using so-called MindFormer. This model is specifically designed to generate fMRI-conditioned feature vectors that can be used for conditioning Stable Diffusion model for fMRI- to-image generation or large language model (LLM) for fMRI-to-text generation. Our experimental results demonstrate that MindFormer generates semantically consistent images and text across different subjects.
  arXiv  Detail & Related papers  (2024-05-28T00:36:25Z)
• Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Online Intelligent Education Systems [9.907875032214996]
  This paper proposes an inductive cognitive diagnosis model (ICDM) for fast new students' mastery levels inference in WOIESs. To obtain this representation, ICDM consists of a construction-aggregation-generation-transformation process. Experiments across real-world datasets show that, compared with the existing cognitive diagnosis methods that are always transductive, ICDM is much more faster.
  arXiv  Detail & Related papers  (2024-04-17T11:55:43Z)
• Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning [55.715623885418815]
  Cross-Domain Few-Shot Learning methods require access to source domain data to train a model in the pre-training phase. Due to increasing concerns about data privacy and the desire to reduce data transmission and training costs, it is necessary to develop a CDFSL solution without accessing source data. This paper proposes an Enhanced Information Maximization with Distance-Aware Contrastive Learning method to address these challenges.
  arXiv  Detail & Related papers  (2024-03-04T12:10:24Z)
• Improving Cognitive Diagnosis Models with Adaptive Relational Graph Neural Networks [33.76551090755183]
  Cognitive Diagnosis (CD) algorithms assist students by inferring their abilities on various knowledge concepts. Recently, researchers have found that building and incorporating a student-exercise bipartite graph is beneficial for enhancing diagnostic performance. We propose Adaptive Semantic-aware Graph-based Cognitive Diagnosis model (ASG-CD), which introduces a novel and effective way to leverage bipartite graph information in CD.
  arXiv  Detail & Related papers  (2024-02-15T14:12:38Z)
• Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems [11.068126651925425]
  This paper proposes a symbolic cognitive diagnosis(SCD) framework to simultaneously enhance generalization and interpretability. The SCD framework incorporates the symbolic tree to explicably represent the complicated student-exercise interaction function. It alternately learns the symbolic tree by derivative-free genetic programming and learns the student and exercise parameters via gradient-based Adam.
  arXiv  Detail & Related papers  (2023-12-30T09:40:10Z)
• Dual Semantic Knowledge Composed Multimodal Dialog Systems [114.52730430047589]
  We propose a novel multimodal task-oriented dialog system named MDS-S2. It acquires the context related attribute and relation knowledge from the knowledge base. We also devise a set of latent query variables to distill the semantic information from the composed response representation.
  arXiv  Detail & Related papers  (2023-05-17T06:33:26Z)
• elBERto: Self-supervised Commonsense Learning for Question Answering [131.51059870970616]
  We propose a Self-supervised Bidirectional Representation Learning of Commonsense framework, which is compatible with off-the-shelf QA model architectures. The framework comprises five self-supervised tasks to force the model to fully exploit the additional training signals from contexts containing rich commonsense. elBERto achieves substantial improvements on out-of-paragraph and no-effect questions where simple lexical similarity comparison does not help.
  arXiv  Detail & Related papers  (2022-03-17T16:23:45Z)
• PIN: A Novel Parallel Interactive Network for Spoken Language Understanding [68.53121591998483]
  In the existing RNN-based approaches, ID and SF tasks are often jointly modeled to utilize the correlation information between them. The experiments on two benchmark datasets, i.e., SNIPS and ATIS, demonstrate the effectiveness of our approach. More encouragingly, by using the feature embedding of the utterance generated by the pre-trained language model BERT, our method achieves the state-of-the-art among all comparison approaches.
  arXiv  Detail & Related papers  (2020-09-28T15:59:31Z)

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.