Intention-aware exercise recommendation enhanced by deep forgetting modeling for e-learning
Abstract Recommender system is regarded as a crucial strategy for realizing personalized e-learning. To better enhance the learning outcomes for learners, exercise recommendation plays a vital role in this system. However, current modeling methods are insufficient to fully capture the dynamic proces...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Discover Computing |
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| Online Access: | https://doi.org/10.1007/s10791-025-09656-5 |
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| _version_ | 1849238440286093312 |
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| author | Zezheng Wu Jingwei Zhang Minghao Liu Xiaoyang Huang Qing Yang |
| author_facet | Zezheng Wu Jingwei Zhang Minghao Liu Xiaoyang Huang Qing Yang |
| author_sort | Zezheng Wu |
| collection | DOAJ |
| description | Abstract Recommender system is regarded as a crucial strategy for realizing personalized e-learning. To better enhance the learning outcomes for learners, exercise recommendation plays a vital role in this system. However, current modeling methods are insufficient to fully capture the dynamic process of learners’ knowledge concept forgetting. Moreover, recommending learning resources based solely on the learner’s current level, without exploring the learner’s learning intentions, leads to inefficient utilization of learning resources. To address these issues, this paper proposes a novel Intention-aware Exercise Recommendation enhanced by deep Forgetting modeling for e-learning (IERF). Specifically, we integrate the forgetting mechanism into multi-concept sequences, model the contextual dependencies of sequences, and design behavioral balancing factors in multiple dimensions to optimize the forgetting prediction of the learning process. Additionally, we construct a heterogeneous information network (HIN) of the learning process and design multi-attention mechanisms to highlight high-order learning relationships between networks and perceive the learner’s learning intentions. Experimental results on three public real-world datasets show that the proposed model outperforms state-of-the-art baselines, and enhances the interpretability under the exercise recommendation. |
| format | Article |
| id | doaj-art-12fbbcc0019545499a556e8f0ba35e0b |
| institution | Kabale University |
| issn | 2948-2992 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Computing |
| spelling | doaj-art-12fbbcc0019545499a556e8f0ba35e0b2025-08-20T04:01:36ZengSpringerDiscover Computing2948-29922025-07-0128112510.1007/s10791-025-09656-5Intention-aware exercise recommendation enhanced by deep forgetting modeling for e-learningZezheng Wu0Jingwei Zhang1Minghao Liu2Xiaoyang Huang3Qing Yang4Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic TechnologyGuangxi Key Laboratory of Trusted Software, Guilin University of Electronic TechnologyGuangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic TechnologyGuangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic TechnologyGuangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic TechnologyAbstract Recommender system is regarded as a crucial strategy for realizing personalized e-learning. To better enhance the learning outcomes for learners, exercise recommendation plays a vital role in this system. However, current modeling methods are insufficient to fully capture the dynamic process of learners’ knowledge concept forgetting. Moreover, recommending learning resources based solely on the learner’s current level, without exploring the learner’s learning intentions, leads to inefficient utilization of learning resources. To address these issues, this paper proposes a novel Intention-aware Exercise Recommendation enhanced by deep Forgetting modeling for e-learning (IERF). Specifically, we integrate the forgetting mechanism into multi-concept sequences, model the contextual dependencies of sequences, and design behavioral balancing factors in multiple dimensions to optimize the forgetting prediction of the learning process. Additionally, we construct a heterogeneous information network (HIN) of the learning process and design multi-attention mechanisms to highlight high-order learning relationships between networks and perceive the learner’s learning intentions. Experimental results on three public real-world datasets show that the proposed model outperforms state-of-the-art baselines, and enhances the interpretability under the exercise recommendation.https://doi.org/10.1007/s10791-025-09656-5E-learning servicesExercise recommendationForgetting modelingIntention-aware modeling |
| spellingShingle | Zezheng Wu Jingwei Zhang Minghao Liu Xiaoyang Huang Qing Yang Intention-aware exercise recommendation enhanced by deep forgetting modeling for e-learning Discover Computing E-learning services Exercise recommendation Forgetting modeling Intention-aware modeling |
| title | Intention-aware exercise recommendation enhanced by deep forgetting modeling for e-learning |
| title_full | Intention-aware exercise recommendation enhanced by deep forgetting modeling for e-learning |
| title_fullStr | Intention-aware exercise recommendation enhanced by deep forgetting modeling for e-learning |
| title_full_unstemmed | Intention-aware exercise recommendation enhanced by deep forgetting modeling for e-learning |
| title_short | Intention-aware exercise recommendation enhanced by deep forgetting modeling for e-learning |
| title_sort | intention aware exercise recommendation enhanced by deep forgetting modeling for e learning |
| topic | E-learning services Exercise recommendation Forgetting modeling Intention-aware modeling |
| url | https://doi.org/10.1007/s10791-025-09656-5 |
| work_keys_str_mv | AT zezhengwu intentionawareexerciserecommendationenhancedbydeepforgettingmodelingforelearning AT jingweizhang intentionawareexerciserecommendationenhancedbydeepforgettingmodelingforelearning AT minghaoliu intentionawareexerciserecommendationenhancedbydeepforgettingmodelingforelearning AT xiaoyanghuang intentionawareexerciserecommendationenhancedbydeepforgettingmodelingforelearning AT qingyang intentionawareexerciserecommendationenhancedbydeepforgettingmodelingforelearning |