Defining, Measuring, and Utilizing Student’s Learning in a Course
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-16178-0_2
Reference15 articles.
1. Ahuja, R.; Jha, A.; Maurya, R.; and Srivastava, R. 2019. Analysis of educational data mining. In Harmony Search and Nature Inspired Optimization Algorithms, 897–907. Springer.
2. de Barba, P. G.; Kennedy, G. E.; and Ainley, M. 2016. The role of students’ motivation and participation in predicting performance in a MOOC. Journal of Computer Assisted Learning 32(3): 218–231.
3. Doan, T.-N.; and Sahebi, S. 2019. Rank-based tensor factorization for student performance prediction. In 12th International Conference on Educational Data Mining (EDM).
4. Elbadrawy, A.; Studham, R. S.; and Karypis, G. 2015. Collaborative multi-regression models for predicting students’ performance in course activities. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge, 103–107.
5. Hellas, A.; Ihantola, P.; Petersen, A.; Ajanovski, V. V.; Gutica, M.; Hynninen, T.; Knutas, A.; Leinonen, J.; Messom, C.; and Liao, S. N. 2018. Predicting academic performance: a systematic literature review. In Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, 175–199.
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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