Automatic student engagement measurement using machine learning techniques: A literature study of data and methods
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17534-9.pdf
Reference82 articles.
1. Fredricks JA, Blumenfeld PC, Paris AH (2004) School engagement: Potential of the concept, state of the evidence. Rev Educ Res 74:59–109
2. Hiver P, Al-Hoorie AH, Mercer S (eds) (2021) Student Engagement in the Language Classroom. Multilingual Matters. https://doi.org/10.21832/9781788923613
3. Deeva G, De Smedt J, De Weerdt J (2022) Educational Sequence Mining for Dropout Prediction in MOOCs: Model Building, Evaluation, and Benchmarking. IEEE Trans Learn Technol 1–16. https://doi.org/10.1109/TLT.2022.3215598
4. Palacios Hidalgo FJ, Huertas Abril CA, Gómez Parra ME (2020) MOOCs: Origins, Concept and Didactic Applications: A Systematic Review of the Literature (2012–2019). Technol Knowl Learn 25:853–879. https://doi.org/10.1007/s10758-019-09433-6
5. Dyment J, Stone C, Milthorpe N (2020) Beyond busy work: rethinking the measurement of online student engagement. High Educ Res Dev 39:1440–1453. https://doi.org/10.1080/07294360.2020.1732879
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
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