A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents

• URL: http://arxiv.org/abs/2411.11520v1
• Date: Mon, 18 Nov 2024 12:29:06 GMT
• Title: A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents
• Authors: Jean Vassoyan, Anan Schütt, Jill-Jênn Vie, Arun-Balajiee Lekshmi-Narayanan, Elisabeth André, Nicolas Vayatis,
• Abstract summary: Massive Open Online Courses (MOOCs) have greatly contributed to making education more accessible. Many MOOCs maintain a rigid, one-size-fits-all structure that fails to address the diverse needs and backgrounds of individual learners. This study introduces a novel data-efficient framework for learning path personalization that operates without expert annotation.
• Score: 8.986349423301863
• License:
• Abstract: Massive Open Online Courses (MOOCs) have greatly contributed to making education more accessible.However, many MOOCs maintain a rigid, one-size-fits-all structure that fails to address the diverse needs and backgrounds of individual learners.Learning path personalization aims to address this limitation, by tailoring sequences of educational content to optimize individual student learning outcomes.Existing approaches, however, often require either massive student interaction data or extensive expert annotation, limiting their broad application.In this study, we introduce a novel data-efficient framework for learning path personalization that operates without expert annotation.Our method employs a flexible recommender system pre-trained with reinforcement learning on a dataset of raw course materials.Through experiments on semi-synthetic data, we show that this pre-training stage substantially improves data-efficiency in a range of adaptive learning scenarios featuring new educational materials.This opens up new perspectives for the design of foundation models for adaptive learning.

Related papers

• Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond [61.18736646013446]
  In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate model of a trained neural network. Across three case studies, we illustrate how it can be applied to derive new empirical insights on a diverse range of prominent phenomena.
  arXiv  Detail & Related papers  (2024-10-31T22:54:34Z)
• PAD: Personalized Alignment of LLMs at Decoding-Time [10.347782385286582]
  This paper presents a novel framework designed to align LLM outputs with diverse personalized preferences during the inference phase. The Personalized Alignment at Decoding-time (PAD) framework decouples the text generation process from personalized preferences. PAD not only outperforms existing training-based alignment methods in terms of aligning with diverse preferences but also shows significant generalizability to preferences unseen during training.
  arXiv  Detail & Related papers  (2024-10-05T08:00:55Z)
• Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving [52.808273563372126]
  This paper proposes a novel hierarchical BEV perception paradigm, aiming to provide a library of fundamental perception modules and user-friendly graphical interface. We conduct the Pretrain-Finetune strategy to effectively utilize large scale public datasets and streamline development processes. We also present a Multi-Module Learning (MML) approach, enhancing performance through synergistic and iterative training of multiple models.
  arXiv  Detail & Related papers  (2024-07-17T11:17:20Z)
• Self-Regulated Data-Free Knowledge Amalgamation for Text Classification [9.169836450935724]
  We develop a lightweight student network that can learn from multiple teacher models without accessing their original training data. To accomplish this, we propose STRATANET, a modeling framework that produces text data tailored to each teacher. We evaluate our method on three benchmark text classification datasets with varying labels or domains.
  arXiv  Detail & Related papers  (2024-06-16T21:13:30Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning [95.64041188351393]
  This paper endeavors to solve both the challenges of limited resources and personalization. We propose a method named ZOOPFL that uses Zeroth-Order Optimization for Personalized Federated Learning. To reduce the computation costs and enhance personalization, we propose input surgery to incorporate an auto-encoder with low-dimensional and client-specific embeddings.
  arXiv  Detail & Related papers  (2023-10-08T12:26:13Z)
• Towards Scalable Adaptive Learning with Graph Neural Networks and Reinforcement Learning [0.0]
  We introduce a flexible and scalable approach towards the problem of learning path personalization. Our model is a sequential recommender system based on a graph neural network. Our results demonstrate that it can learn to make good recommendations in the small-data regime.
  arXiv  Detail & Related papers  (2023-05-10T18:16:04Z)
• Towards a General Pre-training Framework for Adaptive Learning in MOOCs [37.570119583573955]
  We propose a unified framework based on data observation and learning style analysis, properly leveraging heterogeneous learning elements. We find that course structures, text, and knowledge are helpful for modeling and inherently coherent to student non-sequential learning behaviors.
  arXiv  Detail & Related papers  (2022-07-18T13:18:39Z)
• Learning by Distillation: A Self-Supervised Learning Framework for Optical Flow Estimation [71.76008290101214]
  DistillFlow is a knowledge distillation approach to learning optical flow. It achieves state-of-the-art unsupervised learning performance on both KITTI and Sintel datasets. Our models ranked 1st among all monocular methods on the KITTI 2015 benchmark, and outperform all published methods on the Sintel Final benchmark.
  arXiv  Detail & Related papers  (2021-06-08T09:13:34Z)
• Predicting Engagement in Video Lectures [24.415345855402624]
  We introduce a novel, large dataset of video lectures for predicting context-agnostic engagement. We propose both cross-modal and modality-specific feature sets to achieve this task. We demonstrate the use of our approach in the case of data scarcity.
  arXiv  Detail & Related papers  (2020-05-31T19:28:16Z)
• Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification [106.08067870620218]
  We propose a self-paced knowledge distillation framework, termed Learning From Multiple Experts (LFME) We refer to these models as 'Experts', and the proposed LFME framework aggregates the knowledge from multiple 'Experts' to learn a unified student model. We conduct extensive experiments and demonstrate that our method is able to achieve superior performances compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-06T12:57:36Z)

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.