Integrating LSTM and BERT for Long-Sequence Data Analysis in Intelligent Tutoring Systems

• URL: http://arxiv.org/abs/2405.05136v1
• Date: Wed, 24 Apr 2024 18:19:44 GMT
• Title: Integrating LSTM and BERT for Long-Sequence Data Analysis in Intelligent Tutoring Systems
• Authors: Zhaoxing Li, Jujie Yang, Jindi Wang, Lei Shi, Sebastian Stein,
• Abstract summary: We propose a LSTM BERT-based Knowledge Tracing model for long sequence data processing, namely LBKT. The results indicate that LBKT is faster, more interpretable, and has a lower memory cost than the traditional deep learning based Knowledge Tracing methods.
• Score: 4.359769884713738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of Knowledge Tracing aims to understand how students learn and master knowledge over time by analyzing their historical behaviour data. To achieve this goal, many researchers have proposed Knowledge Tracing models that use data from Intelligent Tutoring Systems to predict students' subsequent actions. However, with the development of Intelligent Tutoring Systems, large-scale datasets containing long-sequence data began to emerge. Recent deep learning based Knowledge Tracing models face obstacles such as low efficiency, low accuracy, and low interpretability when dealing with large-scale datasets containing long-sequence data. To address these issues and promote the sustainable development of Intelligent Tutoring Systems, we propose a LSTM BERT-based Knowledge Tracing model for long sequence data processing, namely LBKT, which uses a BERT-based architecture with a Rasch model-based embeddings block to deal with different difficulty levels information and an LSTM block to process the sequential characteristic in students' actions. LBKT achieves the best performance on most benchmark datasets on the metrics of ACC and AUC. Additionally, an ablation study is conducted to analyse the impact of each component of LBKT's overall performance. Moreover, we used t-SNE as the visualisation tool to demonstrate the model's embedding strategy. The results indicate that LBKT is faster, more interpretable, and has a lower memory cost than the traditional deep learning based Knowledge Tracing methods.

Related papers

• Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data [54.934578742209716]
  In real-world NLP applications, Large Language Models (LLMs) offer promising solutions due to their extensive training on vast datasets. LLKD is an adaptive sample selection method that incorporates signals from both the teacher and student. Our comprehensive experiments show that LLKD achieves superior performance across various datasets with higher data efficiency.
  arXiv  Detail & Related papers  (2024-11-12T18:57:59Z)
• Knowledge Graph Structure as Prompt: Improving Small Language Models Capabilities for Knowledge-based Causal Discovery [10.573861741540853]
  KG Structure as Prompt is a novel approach for integrating structural information from a knowledge graph, such as common neighbor nodes and metapaths, into prompt-based learning. Experimental results on three types of biomedical and open-domain datasets under few-shot settings demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-07-26T14:07:00Z)
• Enhancing Deep Knowledge Tracing via Diffusion Models for Personalized Adaptive Learning [1.2248793682283963]
  This study aims to tackle data shortage issues in student learning records to enhance DKT performance for personalized adaptive learning (PAL) It employs TabDDPM, a diffusion model, to generate synthetic educational records to augment training data for enhancing DKT. The experimental results demonstrate that the AI-generated data by TabDDPM significantly improves DKT performance.
  arXiv  Detail & Related papers  (2024-04-25T00:23:20Z)
• Pre-Trained Models: Past, Present and Future [126.21572378910746]
  Large-scale pre-trained models (PTMs) have recently achieved great success and become a milestone in the field of artificial intelligence (AI) By storing knowledge into huge parameters and fine-tuning on specific tasks, the rich knowledge implicitly encoded in huge parameters can benefit a variety of downstream tasks. It is now the consensus of the AI community to adopt PTMs as backbone for downstream tasks rather than learning models from scratch.
  arXiv  Detail & Related papers  (2021-06-14T02:40:32Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• On the Interpretability of Deep Learning Based Models for Knowledge Tracing [5.120837730908589]
  Knowledge tracing allows Intelligent Tutoring Systems to infer which topics or skills a student has mastered. Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have achieved significant improvements. However, these deep learning based models are not as interpretable as other models because the decision-making process learned by deep neural networks is not wholly understood.
  arXiv  Detail & Related papers  (2021-01-27T11:55:03Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• 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)
• Multilinear Compressive Learning with Prior Knowledge [106.12874293597754]
  Multilinear Compressive Learning (MCL) framework combines Multilinear Compressive Sensing and Machine Learning into an end-to-end system. Key idea behind MCL is the assumption of the existence of a tensor subspace which can capture the essential features from the signal for the downstream learning task. In this paper, we propose a novel solution to address both of the aforementioned requirements, i.e., How to find those tensor subspaces in which the signals of interest are highly separable?
  arXiv  Detail & Related papers  (2020-02-17T19:06:05Z)

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.