Using Student Writing and Lexical Analysis to Reveal Student Thinking about the Role of Stop Codons in the Central Dogma

Prevost, Luanna B.; Smith, Michelle K.; Knight, Jennifer K.

doi:10.1187/cbe.15-12-0267

Cited by 36 publications

(55 citation statements)

References 29 publications

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“…Lexical analysis has shown promise for evaluating constructed-response items through computer scoring. 18,[47][48][49][50][51] Over 100 items are available in the Automated Analysis of Constructed Response (AACR) library; 52 most items in the library, though, target biology concepts. 18 We remedied the burden of assessing student understanding of the Lewis acid-base model by capitalizing on the AACR project's lexical analysis methodology; we developed a predictive model for scoring written explanations of an acid-base proton-transfer reaction with 86% accuracy for "use" or "non-use" of the Lewis acid-base model in the response.…”

Section: Lexical Analysis Of Constructed-response Items As a Formativmentioning

confidence: 99%

Development and evaluation of a Lewis acid–base tutorial for use in postsecondary organic chemistry courses

et al. 2019

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A well-developed understanding of the Lewis acid–base model is highly important for the understanding of organic chemistry. As such, students should receive instruction and be assessed on use of the model. Online tutorials and constructed-response items provide a means for confirming that students have a well-developed conceptualization of the Lewis acid–base model. In a prior study, a predictive logistic regression model was presented that can be used with constructed-response assessment items to determine use of a Lewis acid–base model in written responses. In this study, we use that predictive model to evaluate the effectiveness of a tutorial designed to promote meaningful understanding of the Lewis acid–base model in three different instructional contexts: first-semester organic chemistry students before summative assessment, first-semester organic chemistry students after summative assessment, and second-semester organic chemistry students. Additionally, we evaluated the learning gains of one set of first-semester students after a 3-week time delay. McNemar’s test results suggest that the tutorial had a net positive impact in all three instructional contexts, with the most significant impact observed with the second-semester students. This work has implications for further development of literature-based tutorials to promote meaningful understanding of organic chemistry reaction mechanisms assessed by constructed-response items.

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Section: Lexical Analysis Of Constructed-response Items As a Formativmentioning

confidence: 99%

Development and evaluation of a Lewis acid–base tutorial for use in postsecondary organic chemistry courses

et al. 2019

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“…This was necessary to alleviate extraneous variables, such as instructor, in the study. Prior research has shown that 350 or more student responses are necessary to build statistically significant predictive models (11,25). This study was done at the beginning of our data collection, and we will continue to collect data to build statistical models that predict human scoring of these two questions.…”

Section: Discussionmentioning

confidence: 99%

Question order and student understanding of structure and function

Carter

Prevost

2018

Advances in Physiology Education

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The relationship between structure and function is a core concept in physiology education. Written formative assessments can provide insight into student learning of the structure and function relationship, which can then inform pedagogy. However, question order may influence student explanations. We explored how the order of questions from different cognitive levels affects student explanations. A junior level General Physiology class was randomly split in half. One-half of the students answered, “Define the principle: form reflects function,” followed by “Give an example of the principle: form reflects function” (format DX), whereas the other half answered, “Give an example of the principle: form reflects function,” followed by “Define the principle: form reflects function” (format XD). Human grading and computerized lexical analysis were used to evaluate student responses. Two percent of students in the format DX group related structure and function in their definition, whereas 48% of students related structure and function in their examples. In the format XD group, 17% related structure and function in their definition, and 26% related structure and function in their example of the principle. Overall, students performed better on the last question in the sequence, which may be evidence for conceptual priming. Computerized lexical analysis revealed that students draw on only a few levels of organization and may be used by instructors to quickly assess the levels of organization students use in their responses. Written assessment coupled with lexical analysis has the potential to reveal student understanding of core concepts in anatomy and physiology education.

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“…Similarly, Weerts and Cabrera [84] documented factors that influenced the type of civic engagement college students engaged in (superengager, apolitical-engager, social-cultural engager, and nonengager). Finally, Prevost and colleagues [85] used multinomial regression to analyze student understanding of biology concepts by moving beyond simply categorizing student responses as right or wrong and allowing for different types of incomplete understanding.…”

Section: E Multinomial Regression 1 When To Use Multinomial Regressionmentioning

confidence: 99%

Beyond linear regression: A reference for analyzing common data types in discipline based education research

Theobald

Aikens

Eddy

et al. 2019

Phys. Rev. Phys. Educ. Res.

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This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] A common goal in discipline-based education research (DBER) is to determine how to improve student outcomes. Linear regression is a common technique used to test hypotheses about the effects of interventions on continuous outcomes (such as exam score) as well as control for student nonequivalence in quasirandom experimental designs. (In quasirandom designs, subjects are not randomly assigned to treatments. For example, when treatment is assigned by classroom, and observations are made on students, the design is quasirandom because treatment is assigned to classroom, not subject (students).) However, many types of outcome data cannot be appropriately analyzed with linear regression. In these instances, researchers must move beyond linear regression and implement alternative regression techniques. For example, student outcomes can be measured on binary scales (e.g., pass or fail), tightly bound scales (e.g., strongly agree to strongly disagree), or nominal scales (i.e., different discrete choices for example multiple tracks within a physics major), each necessitating alternative regression techniques. Here, we review extensions of linear modeling-generalized linear models (glms)-and specifically compare five glms that are useful for analyzing DBER data: logistic, binomial, proportional odds (also called ordinal; including censored regression), multinomial, and Poisson (including negative binomial, hurdle, and zero-inflated) regression. We introduce a diagnostic tool to facilitate a researcher's identification of the most appropriate glm for their own data. For each model type, we explain when, why, and how to implement the regression approach. When: we provide examples of the types of research questions and outcome data that would motivate this regression approach, including citations to articles in the DBER literature. Why: we name which linear regression assumption is violated by the data type. How: we detail implementation and interpretation of this modeling approach in R, including R syntax and code, and how to discuss the regression output in research papers. Code accompanying each analysis can be found in the online github repository that is associated with this paper (https://github.com/ejtheobald/BeyondLinearRegression). This paper is not an exhaustive review of regression techniques, nor does it review nonregression-based analyses. Rather, it aims to compile and summarize regression techniques useful for the most common types of DBER data and provide examples, citations, and heavily annotated R code so that researchers can easily implement the technique in their work.

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Using Student Writing and Lexical Analysis to Reveal Student Thinking about the Role of Stop Codons in the Central Dogma

Cited by 36 publications

References 29 publications

Development and evaluation of a Lewis acid–base tutorial for use in postsecondary organic chemistry courses

Development and evaluation of a Lewis acid–base tutorial for use in postsecondary organic chemistry courses

Question order and student understanding of structure and function

Beyond linear regression: A reference for analyzing common data types in discipline based education research

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