Improving student outcomes in high‐enrollment statistics classes with the structured writing approach

Batova, Tatiana; Ruediger, Stefan

doi:10.1111/test.12194

Cited by 3 publications

(1 citation statement)

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“…Yet another study explored the importance of critical reading in the teaching of statistics [19]. Additionally, the field has turned to learning outcomes and assessment such as Batova and Ruediger's study [20] which assessed outcomes from incorporating writing to learn and structured writing with Bloom's taxonomy on exam schools in high enrollment statistics classes.…”

Section: Writing and Stem—curricular Interventionsmentioning

confidence: 99%

Writing goals in U.S. undergraduate data science course outlines: A textual analysis

Gooding

Lyford

Giaimo

2022

Teaching Statistics

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Instructors at postsecondary institutions have designed a myriad of data science classes to keep up with the rise of big data. Businesses and companies have become increasingly interested in hiring people with strong data acquisition, management, and communication skills. Since data science as a field of study is relatively new, though it has deep connections to statistical studies, there are few comprehensive analyses of data science classes, majors, programs, and curricular goals. Through this research, we analyze how writing and communication are taught in undergraduate data science classes in the United States. We analyze the presence of writing and communication learning goals from course descriptions and course syllabi. These results show that most data science courses emphasize technical, computing skills over writing, and communication skills. We conclude with a set of actionable heuristics that emphasize integrating writing and communication into data science courses so that students are prepared to use these skills as responsible citizens.

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Section: Writing and Stem—curricular Interventionsmentioning

confidence: 99%