Advancing Personalized Learning Analysis via an Innovative Domain Knowledge Informed Attention-based Knowledge Tracing Method

• URL: http://arxiv.org/abs/2501.05605v1
• Date: Thu, 09 Jan 2025 22:41:50 GMT
• Title: Advancing Personalized Learning Analysis via an Innovative Domain Knowledge Informed Attention-based Knowledge Tracing Method
• Authors: Shubham Kose, Jin Wei-Kocsis,
• Abstract summary: We propose an innovative attention-based method by effectively incorporating the domain knowledge of knowledge concept routes in the given curriculum. We leverage XES3G5M dataset to evaluate and compare the performance of our proposed method to the seven State-of-the-art deep learning models.
• Score: 0.0
• License:
• Abstract: Emerging Knowledge Tracing (KT) models, particularly deep learning and attention-based Knowledge Tracing, have shown great potential in realizing personalized learning analysis via prediction of students' future performance based on their past interactions. The existing methods mainly focus on immediate past interactions or individual concepts without accounting for dependencies between knowledge concept, referred as knowledge concept routes, that can be critical to advance the understanding the students' learning outcomes. To address this, in this paper, we propose an innovative attention-based method by effectively incorporating the domain knowledge of knowledge concept routes in the given curriculum. Additionally, we leverage XES3G5M dataset, a benchmark dataset with rich auxiliary information for knowledge concept routes, to evaluate and compare the performance of our proposed method to the seven State-of-the-art (SOTA) deep learning models.

Related papers

• How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training [92.88889953768455]
  Large Language Models (LLMs) face a critical gap in understanding how they internalize new knowledge. We identify computational subgraphs that facilitate knowledge storage and processing.
  arXiv  Detail & Related papers  (2025-02-16T16:55:43Z)
• Using Large Multimodal Models to Extract Knowledge Components for Knowledge Tracing from Multimedia Question Information [5.777167013394619]
  We propose a method for automatically extracting knowledge components from educational content using instruction-tuned large multimodal models. Our results indicate that the automatically extracted knowledge components can effectively replace human-tagged labels.
  arXiv  Detail & Related papers  (2024-09-30T10:26:29Z)
• Personalized Forgetting Mechanism with Concept-Driven Knowledge Tracing [16.354428270912138]
  We propose a Concept-driven Personalized Forgetting knowledge tracing model (CPF) CPF integrates hierarchical relationships between knowledge concepts and incorporates students' personalized cognitive abilities. Our CPF outperforms current forgetting curve theory based methods in predicting student performance.
  arXiv  Detail & Related papers  (2024-04-18T12:28:50Z)
• Towards Automated Knowledge Integration From Human-Interpretable Representations [55.2480439325792]
  We introduce and motivate theoretically the principles of informed meta-learning enabling automated and controllable inductive bias selection. We empirically demonstrate the potential benefits and limitations of informed meta-learning in improving data efficiency and generalisation.
  arXiv  Detail & Related papers  (2024-02-25T15:08:37Z)
• Exploring Federated Unlearning: Analysis, Comparison, and Insights [101.64910079905566]
  federated unlearning enables the selective removal of data from models trained in federated systems. This paper examines existing federated unlearning approaches, examining their algorithmic efficiency, impact on model accuracy, and effectiveness in preserving privacy. We propose the OpenFederatedUnlearning framework, a unified benchmark for evaluating federated unlearning methods.
  arXiv  Detail & Related papers  (2023-10-30T01:34:33Z)
• Learning from AI: An Interactive Learning Method Using a DNN Model Incorporating Expert Knowledge as a Teacher [7.964052580720558]
  Visual explanation is an approach for visualizing the grounds of judgment by deep learning. A method that incorporates expert human knowledge in the model via an attention map is proposed. The results of an evaluation experiment with subjects show that learning using the proposed method is more efficient than the conventional method.
  arXiv  Detail & Related papers  (2023-06-04T04:22:55Z)
• A Closer Look at Knowledge Distillation with Features, Logits, and Gradients [81.39206923719455]
  Knowledge distillation (KD) is a substantial strategy for transferring learned knowledge from one neural network model to another. This work provides a new perspective to motivate a set of knowledge distillation strategies by approximating the classical KL-divergence criteria with different knowledge sources. Our analysis indicates that logits are generally a more efficient knowledge source and suggests that having sufficient feature dimensions is crucial for the model design.
  arXiv  Detail & Related papers  (2022-03-18T21:26:55Z)
• Learning Data Teaching Strategies Via Knowledge Tracing [5.648636668261282]
  We propose a novel method, called Knowledge Augmented Data Teaching (KADT), to optimize a data teaching strategy for a student model. The KADT method incorporates a knowledge tracing model to dynamically capture the knowledge progress of a student model in terms of latent learning concepts. We have evaluated the performance of the KADT method on four different machine learning tasks including knowledge tracing, sentiment analysis, movie recommendation, and image classification.
  arXiv  Detail & Related papers  (2021-11-13T10:10:48Z)
• A Competence-aware Curriculum for Visual Concepts Learning via Question Answering [95.35905804211698]
  We propose a competence-aware curriculum for visual concept learning in a question-answering manner. We design a neural-symbolic concept learner for learning the visual concepts and a multi-dimensional Item Response Theory (mIRT) model for guiding the learning process. Experimental results on CLEVR show that with a competence-aware curriculum, the proposed method achieves state-of-the-art performances.
  arXiv  Detail & Related papers  (2020-07-03T05:08:09Z)
• Dynamic Knowledge embedding and tracing [18.717482292051788]
  We propose a novel approach to knowledge tracing that combines techniques from matrix factorization with recent progress in recurrent neural networks (RNNs) The proposed emphDynEmb framework enables the tracking of student knowledge even without the concept/skill tag information.
  arXiv  Detail & Related papers  (2020-05-18T21:56:42Z)
• A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning [60.335974351919816]
  Object perception is a fundamental sub-field of Computer Vision. Recent works seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects.
  arXiv  Detail & Related papers  (2019-12-26T13:26:49Z)

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.