Assessing Student Engagement Levels Using Speech Emotion Recognition

Author:

Shrivastava SoumyaORCID,Shukla RaviORCID,Abhi ShinuORCID,Agarwal RashmiORCID

Publisher

Springer Nature Singapore

Reference22 articles.

1. UNICEF. Early for students at risk of dropping out systems warning policy and practice pointers for enrolling all children and adolescents in school and preventing dropout. In: UNICEF series on education participation and dropout prevention-volume 2. Accessed 2023/08/05

2. Valderama AMC, Garcia MB, Tuazon JBD (2022) Promoting student thinking and engagement through question-based and gamified learning. In: 2022 IEEE 14th international conference on humanoid, nanotechnology, information technology, communication and control, environment, and management, HNICEM 2022. https://doi.org/10.1109/HNICEM57413.2022.10109470

3. Subramainan L, Mahmoud MA, Ahmad MS, Yusoff MZM (2017) A conceptual emotion-based model to improve students engagement in a classroom using agent-based social simulation. In: Proceedings—2016 4th international conference on user science and engineering, i-USEr 2016, pp 149–154. https://doi.org/10.1109/IUSER.2016.7857951

4. Wei Y, Wang J, Yang H et al (2019) An investigation of academic self-efficacy, intrinsic motivation and connected classroom climate on college students’ engagement in blended learning. In: Proceedings—2019 international symposium on educational technology, ISET 2019, pp 160–164. https://doi.org/10.1109/ISET.2019.0004

5. Ren Y, Huang S, Zhou Y (2021) Deep learning and integrated learning for predicting student’s withdrawal behavior in MOOC. In: Proceedings—2021 2nd international conference on education, knowledge and information management, ICEKIM 2021, pp 81–84. https://doi.org/10.1109/ICEKIM52309.2021.00026

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