Utilizing crowdsourcing and machine learning in education: Literature review
Author:
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Education
Link
http://link.springer.com/content/pdf/10.1007/s10639-020-10102-w.pdf
Reference39 articles.
1. Alghamdi, E. A., Aljohani, N. R., Alsaleh, A. N., Bedewi, W., & Basheri, M. (2015). CrowdyQ: A virtual crowdsourcing platform for question items development in higher education. Proceedings of the 17th International Conference on Information Integration and Web-Based Applications &Services - IiWAS ‘15, 1–4. https://doi.org/10.1145/2837185.2843852.
2. Barbosa, C. E., Epelbaum, V. J., Antelio, M., Oliveira, J., & Moreira de Souza, J. (2013). Crowdsourcing environments in E-learning scenario: A classification based on educational and collaboration criteria. 2013 IEEE International Conference on Systems, Man, and Cybernetics, 687–692. https://doi.org/10.1109/SMC.2013.122.
3. Choi, J.-R., Kim, S., & Lim, S.-B. (2017). The feedback block model for an adaptive E-book. Adjunct Publication of the 30th Annual ACM Symposium on User Interface Software and Technology - UIST ‘17, 127–128. https://doi.org/10.1145/3131785.3131834.
4. Corrigan-Gibbs, H., Gupta, N., Northcutt, C., Cutrell, E., & Thies, W. (2015). Deterring cheating in online environments. ACM Transactions on Computer-Human Interaction, 22(6), 1–23. https://doi.org/10.1145/2810239.
5. Crooks, T. J. (1988). The impact of classroom evaluation practices on students. Review of Educational Research, 58(4), 438–481. https://doi.org/10.3102/00346543058004438.
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