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首页> 外文期刊>CBE Life Sciences Education >What Are They Thinking? Automated Analysis of Student Writing about Acid–Base Chemistry in Introductory Biology
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What Are They Thinking? Automated Analysis of Student Writing about Acid–Base Chemistry in Introductory Biology

机译：他们在想什么？自动分析入门生物学中有关酸碱化学的学生论文

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Students’ writing can provide better insight into their thinking than can multiple-choice questions. However, resource constraints often prevent faculty from using writing assessments in large undergraduate science courses. We investigated the use of computer software to analyze student writing and to uncover student ideas about chemistry in an introductory biology course. Students were asked to predict acid–base behavior of biological functional groups and to explain their answers. Student explanations were rated by two independent raters. Responses were also analyzed using SPSS Text Analysis for Surveys and a custom library of science-related terms and lexical categories relevant to the assessment item. These analyses revealed conceptual connections made by students, student difficulties explaining these topics, and the heterogeneity of student ideas. We validated the lexical analysis by correlating student interviews with the lexical analysis. We used discriminant analysis to create classification functions that identified seven key lexical categories that predict expert scoring (interrater reliability with experts = 0.899). This study suggests that computerized lexical analysis may be useful for automatically categorizing large numbers of student open-ended responses. Lexical analysis provides instructors unique insights into student thinking and a whole-class perspective that are difficult to obtain from multiple-choice questions or reading individual responses.
机译：与多项选择题相比，学生的写作可以更好地理解他们的思想。但是，资源限制通常会阻止教师在大型本科科学课程中使用写作评估。我们调查了计算机软件的使用，以分析学生的写作并在生物学入门课程中揭示学生有关化学的观点。要求学生预测生物官能团的酸碱行为并解释他们的答案。学生的解释由两个独立的评估者评估。还使用SPSS问卷调查文本分析和与评估项目相关的自定义科学术语和词汇类别库对答复进行了分析。这些分析揭示了学生之间的概念联系，学生在解释这些主题时遇到的困难以及学生观点的异质性。我们通过将学生访谈与词法分析相关联来验证词法分析。我们使用判别分析来创建分类函数，该分类函数确定了七个预测专家评分的关键词汇类别（专家的互信者可靠性= 0.899）。这项研究表明，计算机词法分析对于自动分类大量学生开放式答案可能很有用。词汇分析为教师提供了对学生思维和全班观点的独特见解，难以从多项选择题或阅读单个答案中获得。

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