首页> 外文会议>Data Mining and Big Data >L2 Learners' Proficiency Evaluation Using Statistics Based on Relationship Among CEFR Rating Scales
【24h】

L2 Learners' Proficiency Evaluation Using Statistics Based on Relationship Among CEFR Rating Scales

机译:基于CEFR评级量表之间关系的统计学二语学习者能力评估

获取原文
获取原文并翻译 | 示例

摘要

In this paper, aiming at an objective evaluation of second language (L2) learners' proficiencies, it was tried to predict the learners' language proficiency using 94 statistics. The statistics were extracted automatically and manually from English conversation data collected from groups of Japanese English learners at educational institutions and were classified into 5 subcategories. To estimate the learners' English proficiencies represented as Central European Framework of Reference (CEFR) Global Scale scores, canonical correlation analysis was performed on the statistics and the 5 subcategories, and their correlations to CEFR Global Scale scores were analyzed. As the result of the analysis, 24 statistics were selected for predicting the learners' English proficiencies. The estimation experiment was carried out using a neural network trained by data set of 135 learners and the 24 statistics matrixes in cross-validation. An overall correlation of 0.894 was shown between the predicted proficiency scores and the L2 learners' actual CEFR Global Scale scores. These results confirmed the usefulness of the 24 statistical measures out of the beginning set of 94 measures in the objective evaluation of L2 language proficiency.
机译:本文针对第二语言(L2)学习者的能力的客观评估,尝试使用94个统计量来预测学习者的语言能力。统计数据是从教育机构的日语英语学习者小组收集的英语会话数据中自动和手动提取的,并分为5个子类别。为了估计以中欧参考框架(CEFR)全球量表分数表示的学习者的英语水平,对统计数据和5个子类别进行了规范的相关性分析,并分析了他们与CEFR全球量表分数的相关性。分析的结果是,选择了24个统计数据来预测学习者的英语水平。使用由135个学习者的数据集和交叉验证的24个统计矩阵训练的神经网络进行了估计实验。预测的熟练程度分数与二语学习者的实际CEFR全球量表分数之间显示出0.894的总体相关性。这些结果证实了从开始的94项指标中选择24项统计指标在客观评估L2语言水平方面的有用性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号