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An Analytical System for Determining Disciplinary Vocabulary for Data-Driven Learning: An Example from Civil Engineering

机译:确定数据驱动学习学科词汇量的分析系统 - 以土木工程为例

摘要

Data-driven learning (DDL), an inductive teaching approach in which students learn through corpus interaction, has gained recent traction as way to teach specialized vocabulary in English for Specific Purposes (ESP) classes. There is little research, however, that addresses how to choose specialized vocabulary for teaching with DDL.This study addressed this gap in research by exploring the potential of a three-part analytical, corpus-based system for determining vocabulary to teach with DDL for a specific context of language use. This system included (1) identifying words that were significantly more frequent in a specialized expert corpus than in a corpus of general English, (2) narrowing to words that showed patterned differences in use between the specialized corpus and a student corpus, and (3) narrowing further to words with salient enough patterns of usage to teach with DDL. This three-part system was applied to the context of civil engineering in order to find vocabulary words to teach civil engineering students with low-proficiency writing skills at Portland State University.For the first step in my analytical system, I found 201 words that occurred significantly more frequently in civil engineering practitioner writing than in the Corpus of Contemporary American English and that met requirements for frequency, distribution, and other criteria. I tested the second and third steps on 45 of these words and identified 14 words that showed evidence of needing to be taught and being well suited to DDL.After reflecting on my process, I found that the analytical system was successful in meeting my goals for finding civil engineering vocabulary for data-driven activities. I also made several observations that may be useful for ESP teachers who are interested in applying this methodology for their classes, the most notable of which were:1. The system was especially useful for connecting words that are not explicitly civil engineering themed (e.g., encountered or using) to important writing functions that civil engineers perform.2. Although it provided a systematic basis for vocabulary teaching decisions, the process was generally time-consuming and required complex judgments, which indicated that it may only be worth performing if teachers plan to regularly incorporate DDL vocabulary instruction into their course.
机译:数据驱动学习(DDL)是一种归纳式教学方法,通过该方法,学生可以通过语料库交互进行学习,这种方法最近已受到人们的欢迎,这是一种以专用英语(ESP)班级教授专业词汇的方法。然而,很少有研究涉及如何选择DDL教学专用词汇。本研究通过探索基于语料库的三部分分析系统来确定DDL教学词汇的潜力,从而解决了研究中的这一差距。语言使用的特定上下文。该系统包括(1)识别在专门专家语料库中比在通用英语语料库中更频繁出现的单词;(2)缩小到显示出专门语料库和学生语料库之间使用模式差异的单词,以及(3 )进一步缩小到具有足够明显的用法模式的单词,以使用DDL进行教学。这个三部分的系统被应用到土木工程的环境中,以寻找词汇来教授波特兰州立大学低技能写作技能的土木工程学生。在我的分析系统的第一步中,我发现了201个单词在土木工程从业人员的写作中,相比于满足频率,分布和其他条件要求的《当代美国英语语料库》的使用频率要高得多。我测试了其中的45个单词的第二步和第三步,确定了14个单词,这些单词表明需要教书并且非常适合DDL。反思了我的过程之后,我发现分析系统成功实现了我的目标查找用于数据驱动活动的土木工程词汇。我还提出了一些意见,这可能对有兴趣在课堂上采用这种方法的ESP教师有用,其中最值得注意的是:1.。该系统对于将未明确以土木工程为主题(例如,遇到或使用)的词与土木工程师执行的重要写作功能联系在一起特别有用2。尽管它为词汇教学决策提供了系统的基础,但该过程通常很耗时并且需要复杂的判断,这表明只有在教师计划将DDL词汇教学定期纳入其课程时,才值得执行。

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    Otto Philippa Jean;

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