To Read or Not to Read: Predicting Student Engagement in Interactive Reading
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64299-9_15
Reference27 articles.
1. Bates, D., Mächler, M., Bolker, B., Walker, S.: Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48 (2015)
2. Beigman Klebanov, B., Loukina, A.: Exploiting structured error to improve automated scoring of oral reading fluency. In: Proceedings of AIED, pp. 76–81 (2021)
3. Biedermann, D., et al.: Detecting the disengaged reader-using scrolling data to predict disengagement during reading. In: LAK23: 13th International Learning Analytics and Knowledge Conference, pp. 585–591 (2023)
4. Van den Broek, P., Lorch, R., Linderholm, T., Gustafson, M.: The effects of readers’ goals on inference generation and memory for texts. Memory Cogn. 29(8), 1081–1087 (2001)
5. Brysbaert, M.: How many words do we read per minute? A review and meta-analysis of reading rate. J. Memory Language 109(104047) (2019)
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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