Proceedings of the Fifth Annual ACM Conference on Learning at Scale最新文献_第7页

Classifying and visualizing students' cognitive engagement in course readings 分类和可视化学生在课程阅读中的认知参与

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231648

Eran Yogev, Y. Gal, David R Karger, M. Facciotti, Michele Igo

引用次数: 11

QG-net: a data-driven question generation model for educational content QG-net:一个数据驱动的教育内容问题生成模型

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231654

Zichao Wang, Andrew S. Lan, Weili Nie, Andrew E. Waters, Phillip J. Grimaldi, Richard Baraniuk

引用次数: 57

Contemporary online course design recommendations to support women's cognitive development 当代在线课程设计建议，支持女性认知发展

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-26 DOI: 10.1145/3231644.3231688

Virginia L. Byrne

引用次数: 2

Addressing two problems in deep knowledge tracing via prediction-consistent regularization 通过预测一致正则化解决深度知识跟踪中的两个问题

Proceedings of the Fifth Annual ACM Conference on Learning at Scale Pub Date : 2018-06-06 DOI: 10.1145/3231644.3231647

Chun-Kit Yeung, D. Yeung

{"title":"Addressing two problems in deep knowledge tracing via prediction-consistent regularization","authors":"Chun-Kit Yeung, D. Yeung","doi":"10.1145/3231644.3231647","DOIUrl":"https://doi.org/10.1145/3231644.3231647","url":null,"abstract":"Knowledge tracing is one of the key research areas for empowering personalized education. It is a task to model students' mastery level of a knowledge component (KC) based on their historical learning trajectories. In recent years, a recurrent neural network model called deep knowledge tracing (DKT) has been proposed to handle the knowledge tracing task and literature has shown that DKT generally outperforms traditional methods. However, through our extensive experimentation, we have noticed two major problems in the DKT model. The first problem is that the model fails to reconstruct the observed input. As a result, even when a student performs well on a KC, the prediction of that KC's mastery level decreases instead, and vice versa. Second, the predicted performance for KCs across time-steps is not consistent. This is undesirable and unreasonable because student's performance is expected to transit gradually over time. To address these problems, we introduce regularization terms that correspond to reconstruction and waviness to the loss function of the original DKT model to enhance the consistency in prediction. Experiments show that the regularized loss function effectively alleviates the two problems without degrading the original task of DKT.1","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91002235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 146