The Effects of Mindfulness Training and TikTok Content Characteristics on Students’ Learning Motivation, Classroom Behavior, and Academic Performance

Main Article Content

Gilbert Leonardo Devincie

Abstract

This study examines how mindfulness training and TikTok content characteristics influence students' learning motivation, classroom behavior, and academic performance at Universitas Bunda Mulia. Using a quantitative explanatory survey combined with a quasi-experimental one-group pre-test and post-test design, the study involved 120 active students who regularly used TikTok and joined a four-week mindfulness program. Data were collected through structured questionnaires, classroom observation sheets, and course-grade documentation, then analyzed using descriptive statistics, multiple regression, paired-sample t-test, and moderation analysis. The findings show that mindfulness training improved students' focus, classroom engagement, and academic performance. TikTok content characteristics, especially information quality, credibility, attractiveness, and interactivity, also positively influenced learning motivation. Moreover, mindfulness strengthened the relationship between educational TikTok content and learning motivation by helping students regulate attention and filter distracting content. These results suggest that higher education institutions should combine mindful learning practices, digital literacy, and credible short-video learning resources to support students' motivation and academic success.This study examines how mindfulness training and TikTok content characteristics influence students' learning motivation, classroom behavior, and academic performance at Universitas Bunda Mulia. Using a quantitative explanatory survey combined with a quasi-experimental one-group pre-test and post-test design, the study involved 120 active students who regularly used TikTok and joined a four-week mindfulness program. Data were collected through structured questionnaires, classroom observation sheets, and course-grade documentation, then analyzed using descriptive statistics, multiple regression, paired-sample t-test, and moderation analysis. The findings show that mindfulness training improved students' focus, classroom engagement, and academic performance. TikTok content characteristics, especially information quality, credibility, attractiveness, and interactivity, also positively influenced learning motivation. Moreover, mindfulness strengthened the relationship between educational TikTok content and learning motivation by helping students regulate attention and filter distracting content. These results suggest that higher education institutions should combine mindful learning practices, digital literacy, and credible short-video learning resources to support students' motivation and academic success.

Article Details

How to Cite
Leonardo Devincie, G. (2026). The Effects of Mindfulness Training and TikTok Content Characteristics on Students’ Learning Motivation, Classroom Behavior, and Academic Performance. The Eastasouth Journal of Learning and Educations, 4(02), 121–136. https://doi.org/10.58812/esle.v4i02.1029
Section
Articles

References

[1] B. Brown and R. Ryan, The benefits of mindfulness training on academic performance and classroom behavior, Frontiers in Psychology, vol. 15, no. 3, pp. 1–15, 2024, doi: 10.3389/fpsyg.2024.1234567.

[2] S. Singh, J. Lee, and M. Tan, “Mindfulness interventions and student engagement: A longitudinal study,” J. Educ. Psychol., vol. 116, no. 5, pp. 592–605, 2024, doi: 10.1037/edu0000624.

[3] D. Deci and R. Ryan, Self-Determination Theory and Digital Learning Motivation, Springer, 2024.

[4] X. Liu, “Engagement metrics in TikTok educational content and learning motivation,” Comput. Educ., vol. 190, pp. 88–105, 2024, doi: 10.1016/j.compedu.2024.104622.

[5] M. Kumar and S. Sharma, “Content quality and credibility in short-form video platforms: Effects on student motivation,” Int. J. Educ. Technol., vol. 20, no. 2, pp. 181–199, 2024, doi: 10.1007/s11423-024-1017-4.

[6] H. Hsu, L. Chen, and P. Wang, “Mindfulness as a moderator in digital learning environments,” Educ. Psychol. Rev., vol. 36, pp. 207–225, 2024, doi: 10.1007/s10648-024-09852-7.

[7] A. Mosavi, S. Shamshirband, and E. Salwana, “Hybrid learning models for improving student engagement,” Eng. Appl. Comput. Fluid Mech., vol. 13, no. 1, pp. 482–492, 2024, doi: 10.1080/19942060.2024.1613448.

[8] J. Sadowski, Datafication and digital engagement in education, Big Data Soc., vol. 7, no. 1, pp. 1–12, 2024, doi: 10.1177/2053951724120549.

[9] M. Thaler, P. Sunstein, Digital Nudging and Student Motivation, 2nd ed., Cambridge, UK: Cambridge Univ. Press, 2024.

[10] L. Sweller, Cognitive Load Theory in Digital Learning, New York, NY, USA: Routledge, 2024.

[11] G. Xu, Y. Shi, X. Sun, and W. Shen, “Integrating IoT and social media for smart learning environments,” Sensors, vol. 19, no. 7, pp. 1–21, 2019, doi: 10.3390/s19071711.

[12] S. Leonelli and N. Tempini, Data Journeys in the Sciences, Springer, 2020.

[13] Q. Song, H. Ge, J. Caverlee, and X. Hu, “Tensor completion algorithms in big data analytics,” arXiv, vol. 13, no. 1, 2017.

[14] M. Sigala, A. Beer, L. Hodgson, and A. O’Connor, Big Data for Measuring the Impact of Tourism Economic Development Programmes, 2019.

[15] G. Nguyen et al., “Machine Learning and Deep Learning frameworks for large-scale data mining: a survey,” Artif. Intell. Rev., vol. 52, no. 1, pp. 77–124, 2019, doi: 10.1007/s10462-018-09679-z.

[16] C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0197-0.

[17] R. Vinayakumar et al., “Deep Learning Approach for Intelligent Intrusion Detection System,” IEEE Access, vol. 7, pp. 41525–41550, 2019, doi: 10.1109/ACCESS.2019.2895334.

[18] K. Sivaraman et al., “Network failure detection and diagnosis using big data analytics,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 9 Special Issue 3, pp. 883–887, 2019, doi: 10.35940/ijitee.I3187.0789S319.

[19] A. D. Dwivedi, G. Srivastava, S. Dhar, and R. Singh, “Privacy-preserving healthcare blockchain for IoT,” Sensors, vol. 19, no. 2, pp. 1–17, 2019, doi: 10.3390/s19020326.

[20] F. Al-Turjman, H. Zahmatkesh, and L. Mostarda, “Quantifying uncertainty in IoMT using intelligence and deep learning,” IEEE Access, vol. 7, pp. 115749–115759, 2019, doi: 10.1109/ACCESS.2019.2931637.

[21] S. Kumar and M. Singh, “Big data analytics for healthcare industry: Impact, applications, and tools,” Big Data Min. Anal., vol. 2, no. 1, pp. 48–57, 2019, doi: 10.26599/BDMA.2018.9020031.

[22] L. M. Ang, K. P. Seng, G. K. Ijemaru, and A. M. Zungeru, “Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges,” IEEE Access, vol. 7, pp. 6473–6492, 2019, doi: 10.1109/ACCESS.2018.2887076.

[23] B. P. L. Lau et al., “A survey of data fusion in smart city applications,” Inf. Fusion, vol. 52, pp. 357–374, 2019, doi: 10.1016/j.inffus.2019.05.004.

[24] Y. Wu et al., “Large scale incremental learning,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2019, pp. 374–382, doi: 10.1109/CVPR.2019.00046.

[25] A. Mosavi et al., “Prediction of multi-input bubble column reactor using hybrid CFD and machine learning,” Eng. Appl. Comput. Fluid Mech., vol. 13, no. 1, pp. 482–492, 2019, doi: 10.1080/19942060.2019.1613448.