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題名 Forecasting reading anxiety to promote reading performance based on annotation behavior
作者 Lu, T.-Y.;Lin, M.;Chen, Chen C.-M.;Wu, J.-H.
陳志銘
Chen, Chihming
Wu, Jhihhao
貢獻者 圖檔所
關鍵詞 C4.5 decision trees; Cooperative/collaborative learning; Experimental groups; Intelligent tutoring system; Interactive learning environment; Learning performance; Reading performance; Teaching/learning strategy; Computer aided instruction; Data mining; Decision trees; Forecasting; Learning systems
日期 2013
上傳時間 6-Sep-2018 17:41:04 (UTC+8)
摘要 To reduce effectively the reading anxiety of learners while reading English articles, a C4.5 decision tree, a widely used data mining technique, was used to develop a personalized reading anxiety prediction model (PRAPM) based on individual learners` reading annotation behavior in a collaborative digital reading annotation system. In addition to forecasting immediately the reading anxiety levels of learners, the proposed PRAPM can be used to identify the key factors that cause reading anxiety based on the fired prediction rules determined by the developed decision tree. By understanding these key factors that cause reading anxiety, instructors can apply reading strategies to reduce reading anxiety, thus promoting English-language reading performance. To assess whether the proposed PRAPM can assist instructors in reducing the reading anxiety of learners, this study applies the quasi-experimental method to compare the learning performance of three learning groups, which are supported by a collaborative digital reading annotation system with different learning mechanisms to reduce reading anxiety. The control group, experimental group A and experimental group B conducted the same English reading activity. However, each group was given a collaborative digital reading annotation system with individual annotations, cooperative annotations, and cooperative annotation with the instructor`s support to reduce reading anxiety by proposed PRAPM. Experimental results indicate that the average correct prediction rate of the proposed PRAPM in identifying the reading anxiety levels of learners was as high as 70%. The online instructor who applied reading assistive strategies based on the mining factors that affect reading anxiety from the proposed PRAPM can significantly reduce the reading anxiety of male learners in the experimental group B, showing that gender difference existed, and the online instructor`s interaction with the male learners of the experimental group B indeed helped reduce the reading anxiety. © 2013 IEEE.
關聯 Proceedings - International Computer Software and Applications Conference, 2013, 論文編號 06605828, Pages 427-432
2013 IEEE 37th Annual Computer Software and Applications Conference Workshops, COMPSACW 2013; Kyoto; Japan; 22 July 2013 到 26 July 2013; 類別編號E4987; 代碼 100165
資料類型 conference
DOI http://dx.doi.org/10.1109/COMPSACW.2013.132
dc.contributor 圖檔所
dc.creator (作者) Lu, T.-Y.;Lin, M.;Chen, Chen C.-M.;Wu, J.-H.en_US
dc.creator (作者) 陳志銘zh_TW
dc.creator (作者) Chen, Chihmingen_US
dc.creator (作者) Wu, Jhihhaoen_US
dc.date (日期) 2013
dc.date.accessioned 6-Sep-2018 17:41:04 (UTC+8)-
dc.date.available 6-Sep-2018 17:41:04 (UTC+8)-
dc.date.issued (上傳時間) 6-Sep-2018 17:41:04 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/120032-
dc.description.abstract (摘要) To reduce effectively the reading anxiety of learners while reading English articles, a C4.5 decision tree, a widely used data mining technique, was used to develop a personalized reading anxiety prediction model (PRAPM) based on individual learners` reading annotation behavior in a collaborative digital reading annotation system. In addition to forecasting immediately the reading anxiety levels of learners, the proposed PRAPM can be used to identify the key factors that cause reading anxiety based on the fired prediction rules determined by the developed decision tree. By understanding these key factors that cause reading anxiety, instructors can apply reading strategies to reduce reading anxiety, thus promoting English-language reading performance. To assess whether the proposed PRAPM can assist instructors in reducing the reading anxiety of learners, this study applies the quasi-experimental method to compare the learning performance of three learning groups, which are supported by a collaborative digital reading annotation system with different learning mechanisms to reduce reading anxiety. The control group, experimental group A and experimental group B conducted the same English reading activity. However, each group was given a collaborative digital reading annotation system with individual annotations, cooperative annotations, and cooperative annotation with the instructor`s support to reduce reading anxiety by proposed PRAPM. Experimental results indicate that the average correct prediction rate of the proposed PRAPM in identifying the reading anxiety levels of learners was as high as 70%. The online instructor who applied reading assistive strategies based on the mining factors that affect reading anxiety from the proposed PRAPM can significantly reduce the reading anxiety of male learners in the experimental group B, showing that gender difference existed, and the online instructor`s interaction with the male learners of the experimental group B indeed helped reduce the reading anxiety. © 2013 IEEE.en_US
dc.format.extent 209 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceedings - International Computer Software and Applications Conference, 2013, 論文編號 06605828, Pages 427-432
dc.relation (關聯) 2013 IEEE 37th Annual Computer Software and Applications Conference Workshops, COMPSACW 2013; Kyoto; Japan; 22 July 2013 到 26 July 2013; 類別編號E4987; 代碼 100165
dc.subject (關鍵詞) C4.5 decision trees; Cooperative/collaborative learning; Experimental groups; Intelligent tutoring system; Interactive learning environment; Learning performance; Reading performance; Teaching/learning strategy; Computer aided instruction; Data mining; Decision trees; Forecasting; Learning systemsen_US
dc.title (題名) Forecasting reading anxiety to promote reading performance based on annotation behavioren_US
dc.type (資料類型) conference
dc.identifier.doi (DOI) 10.1109/COMPSACW.2013.132
dc.doi.uri (DOI) http://dx.doi.org/10.1109/COMPSACW.2013.132