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<p>Traditional music education struggles to keep pace with the wide variation in learning speed and prior skill that individual students bring to a class, and this mismatch is most visible where auditory perception, rhythmic timing, and motor coordination all diverge from one learner to the next. We propose a deep learning-based adaptive recommendation framework that delivers personalised music instruction through data-driven content selection, and we design each component so that its contribution to actual learning outcomes can be defended empirically. Three parts form the core of the design. A multi-modal feature fusion network reconciles heterogeneous learner data—interaction behaviours, performance metrics, and skill assessments—by letting attention reweight each modality according to context; we benchmark it against modern transformer- and graph-based recommenders rather than only against shallow baselines. A reinforcement learning policy frames recommendation as a sequential decision problem, optimising a longer-horizon learning signal instead of immediate engagement. A multi-task objective jointly predicts recommendation relevance and difficulty alignment so that challenge stays calibrated to what the learner can currently do. Evaluation draws on observational platform data from 8,476 learners; because the platform only supplies bi-weekly assessment scores and three-month continuation rates, we treat both as proxy indicators rather than externally adjudicated measures of musical proficiency. On ranking metrics the proposed model achieves 11.0% higher Precision@5 and 10.4% greater NDCG@10 than the strongest deep-learning baseline (BERT4Rec), with wider margins against shallower models such as DeepFM (22.5% in Precision@5) and NCF (23.9% in NDCG@10); paired t-tests and 95% bootstrap confidence intervals confirm statistical significance across three random seeds. A propensity-score-adjusted longitudinal cohort analysis is consistent with, but does not prove, higher platform-assessment gains and higher three-month continuation rates for learners exposed to the adaptive policy. Because cohorts self-selected and our E-value analysis indicates that motivation alone could plausibly clear the sensitivity threshold, we deliberately withhold point estimates from this abstract and treat the observational figures as an association rather than causal evidence; the specific magnitudes, together with their caveats, are stated only in Sect. 4.3. We close by discussing what a single-platform observational study can and cannot say, and what a controlled trial would have to add before causal claims become tenable.</p>

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Deep learning-based adaptive recommendation algorithm for personalized music teaching

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  • 出版模式：

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  • 发表日期：

    2026年7月25日

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  • Rongji Su

摘要

Traditional music education struggles to keep pace with the wide variation in learning speed and prior skill that individual students bring to a class, and this mismatch is most visible where auditory perception, rhythmic timing, and motor coordination all diverge from one learner to the next. We propose a deep learning-based adaptive recommendation framework that delivers personalised music instruction through data-driven content selection, and we design each component so that its contribution to actual learning outcomes can be defended empirically. Three parts form the core of the design. A multi-modal feature fusion network reconciles heterogeneous learner data—interaction behaviours, performance metrics, and skill assessments—by letting attention reweight each modality according to context; we benchmark it against modern transformer- and graph-based recommenders rather than only against shallow baselines. A reinforcement learning policy frames recommendation as a sequential decision problem, optimising a longer-horizon learning signal instead of immediate engagement. A multi-task objective jointly predicts recommendation relevance and difficulty alignment so that challenge stays calibrated to what the learner can currently do. Evaluation draws on observational platform data from 8,476 learners; because the platform only supplies bi-weekly assessment scores and three-month continuation rates, we treat both as proxy indicators rather than externally adjudicated measures of musical proficiency. On ranking metrics the proposed model achieves 11.0% higher Precision@5 and 10.4% greater NDCG@10 than the strongest deep-learning baseline (BERT4Rec), with wider margins against shallower models such as DeepFM (22.5% in Precision@5) and NCF (23.9% in NDCG@10); paired t-tests and 95% bootstrap confidence intervals confirm statistical significance across three random seeds. A propensity-score-adjusted longitudinal cohort analysis is consistent with, but does not prove, higher platform-assessment gains and higher three-month continuation rates for learners exposed to the adaptive policy. Because cohorts self-selected and our E-value analysis indicates that motivation alone could plausibly clear the sensitivity threshold, we deliberately withhold point estimates from this abstract and treat the observational figures as an association rather than causal evidence; the specific magnitudes, together with their caveats, are stated only in Sect. 4.3. We close by discussing what a single-platform observational study can and cannot say, and what a controlled trial would have to add before causal claims become tenable.