Forecasting Reading Anxiety to Promote Reading Performance Based on Annotation Behavior

Tingda Lu, Mi Lin, Chih-Ming Chen, Jhih-Hao Wu
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引用次数: 1

Abstract

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.
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基于标注行为的阅读焦虑预测促进阅读成绩
为了有效降低学习者在阅读英语文章时的阅读焦虑,在协作式数字阅读标注系统中,采用C4.5决策树这一广泛应用的数据挖掘技术,基于学习者个人的阅读标注行为,建立了个性化的阅读焦虑预测模型(PRAPM)。本文提出的PRAPM除了可以直接预测学习者的阅读焦虑水平外,还可以根据所开发的决策树确定的预测规则来识别导致阅读焦虑的关键因素。通过了解这些导致阅读焦虑的关键因素,教师可以运用阅读策略来减少阅读焦虑,从而提高英语阅读成绩。为了评估PRAPM是否能够帮助教师减少学习者的阅读焦虑,本研究采用准实验方法,比较了在不同学习机制的协作式数字阅读注释系统支持下的三个学习小组的学习表现。对照组、实验A组和实验B组进行相同的英语阅读活动。然而,每个小组都获得了一个协作式数字阅读注释系统,该系统包括个人注释、合作注释和导师支持的合作注释,以通过提出的PRAPM减少阅读焦虑。实验结果表明,所提出的PRAPM识别学习者阅读焦虑水平的平均正确率高达70%。采用基于PRAPM中影响阅读焦虑因素挖掘的阅读辅助策略的在线教师可以显著降低实验组B男性学习者的阅读焦虑,表明存在性别差异,并且在线教师与实验组B男性学习者的互动确实有助于降低阅读焦虑。
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