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Role of Socio-cultural Differences in Labeling Students' Affective States., , , , and . AIED (1), volume 10947 of Lecture Notes in Computer Science, page 367-380. Springer, (2018)Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings., , and . LREC, European Language Resources Association (ELRA), (2016)Investigating the Impact of a Real-time, Multimodal Student Engagement Analytics Technology in Authentic Classrooms., , , , , , and . CHI, page 304. ACM, (2019)Semi-supervised model personalization for improved detection of learner's emotional engagement., , , , , , , and . ICMI, page 100-107. ACM, (2016)An unobtrusive and multimodal approach for behavioral engagement detection of students., , , , , and . MIE@ICMI, page 26-32. ACM, (2017)Towards an Emotional Engagement Model: Can Affective States of a Learner be Automatically Detected in a 1: 1 Learning Scenario?, , , , , , , , and . UMAP (Extended Proceedings), volume 1618 of CEUR Workshop Proceedings, CEUR-WS.org, (2016)Human Expert Labeling Process: Valence-Arousal Labeling for Students' Affective States., , , , and . MIS4TEL, volume 804 of Advances in Intelligent Systems and Computing, page 53-61. Springer, (2018)Leveraging Topics and Audio Features with Multimodal Attention for Audio Visual Scene-Aware Dialog., , , , and . CoRR, (2019)Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings., , and . CoRR, (2018)Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students., , , , , and . CoRR, (2019)