A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913580995051520 |
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| author | Vassoyan, Jean Schütt, Anan Vie, Jill-Jênn Lekshmi-Narayanan, Arun-Balajiee André, Elisabeth Vayatis, Nicolas |
| author_facet | Vassoyan, Jean Schütt, Anan Vie, Jill-Jênn Lekshmi-Narayanan, Arun-Balajiee André, Elisabeth Vayatis, Nicolas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11520 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents Vassoyan, Jean Schütt, Anan Vie, Jill-Jênn Lekshmi-Narayanan, Arun-Balajiee André, Elisabeth Vayatis, Nicolas Artificial Intelligence Computers and Society Machine Learning 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. |
| title | A Pre-Trained Graph-Based Model for Adaptive Sequencing of Educational Documents |
| topic | Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2411.11520 |