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Nye, Benjamin D.; Morrison, Donald M.; Samei, Borhan – International Educational Data Mining Society, 2015
Archived transcripts from tens of millions of online human tutoring sessions potentially contain important knowledge about how online tutors help, or fail to help, students learn. However, without ways of automatically analyzing these large corpora, any knowledge in this data will remain buried. One way to approach this issue is to train an…
Descriptors: Tutoring, Instructional Effectiveness, Tutors, Models
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Zhang, Lishan; VanLehn, Kurt – Interactive Learning Environments, 2017
The paper describes a biology tutoring system with adaptive question selection. Questions were selected for presentation to the student based on their utilities, which were estimated from the chance that the student's competence would increase if the questions were asked. Competence was represented by the probability of mastery of a set of biology…
Descriptors: Biology, Science Instruction, Intelligent Tutoring Systems, Probability
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Trausan-Matu, Stefan; Dascalu, Mihai; Rebedea, Traian – International Journal of Computer-Supported Collaborative Learning, 2014
Chat conversations and other types of online communication environments are widely used within CSCL educational scenarios. However, there is a lack of theoretical and methodological background for the analysis of collaboration. Manual assessing of non-moderated chat discussions is difficult and time-consuming, having as a consequence that learning…
Descriptors: Computer Mediated Communication, Feedback (Response), Cooperative Learning, Computer Assisted Instruction
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Chukharev-Hudilainen, Evgeny; Saricaoglu, Aysel – Computer Assisted Language Learning, 2016
Expressing causal relations plays a central role in academic writing. While it is important that writing instructors assess and provide feedback on learners' causal discourse, it could be a very time-consuming task. In this respect, automated writing evaluation (AWE) tools may be helpful. However, to date, there have been no AWE tools capable of…
Descriptors: Discourse Analysis, Feedback (Response), Undergraduate Students, Accuracy
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Liu, Ming; Calvo, R. A.; Aditomo, A.; Pizzato, L. A. – IEEE Transactions on Learning Technologies, 2012
In this paper, we present a novel approach for semiautomatic question generation to support academic writing. Our system first extracts key phrases from students' literature review papers. Each key phrase is matched with a Wikipedia article and classified into one of five abstract concept categories: Research Field, Technology, System, Term, and…
Descriptors: Foreign Countries, Computer Assisted Instruction, Web 2.0 Technologies, Automation