Abstract
Listening to music is a common behavior when people study or work. However, effects of music listening on studying are still disputed in previous studies. To explore the associations between music characteristics and learning performance and engagement and to develop a music recommender for studying in naturalistic settings, we conducted a two-month field experiment with 51 undergraduate and graduate students. A mobile application based on the experience sampling method was designed and implemented to ubiquitously collect participants' learning status and music listening traces. Statistical tests and machine learning were adopted respectively for uncovering the associations between music listening on learning and constructing a music recommendation model. Results first indicated that learners' music preferences and several musical features were positively correlated with self-reported learning performance and concentration. Furthermore, machine learning modeling demonstrated promising results for developing a music recommender for studying in naturalistic settings. Findings are expected to contribute to research on learning with background music and learning-oriented music recommendation. Copyright © 2023 IEEE.
Original language | English |
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Title of host publication | Proceedings of 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023 |
Place of Publication | USA |
Publisher | IEEE |
Pages | 169-173 |
ISBN (Electronic) | 9798350300543 |
DOIs | |
Publication status | Published - 2023 |
Citation
Liu, R., Wang, Z., Ba, S., & Hu, X. (2023). Preliminary exploration of the effectiveness of music listening and music recommender for studying in naturalistic settings. In Proceedings of 2023 IEEE International Conference on Advanced Learning Technologies, ICALT 2023 (pp. 169-173). IEEE. https://doi.org/10.1109/ICALT58122.2023.00055Keywords
- Learning
- Background music
- Music recommendation