자료유형  

 학위논문

Control Number  

0017161594

International Standard Book Number  

9798382777528

Dewey Decimal Classification Number  

370

Main Entry-Personal Name  

Klugman, Emma Mary.

Publication, Distribution, etc. (Imprint  

[S.l.] : Harvard University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

146 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

General Note  

Advisor: Ho, Andrew.

Dissertation Note  

Thesis (Ph.D.)--Harvard University, 2024.

Summary, Etc.  

요약Statistics & data science are growing, rapidly evolving, and increasingly important for an informed citizenry in a data-saturated world. In this dissertation, I address two central questions: (1) who is taking statistics? and (2) what are statistics courses teaching?I estimate that 920,000 US students take statistics in high school each year, but this population has not yet been well studied. Using a rich set of survey responses describing 15,727 students' demographics, career interests and values, STEM identity, grades, and test scores, my first study compares four groups of high-school course-takers: those who take statistics, calculus, both, and neither. I then employ latent profile analysis to shed light on who these students are, showing that students with different profiles take statistics at surprisingly similar rates: statistics is as an important part of the academic pathway for a wide range of students and serves a demographically diverse population.In my second study, I build upon tools from natural language processing and psychometric measurement to develop a human-in-the-loop methodology for measuring latent constructs in large text corpora, and present a framework for doing so. I construct a lexicon-based instrument to measure the extent to which syllabi from college statistics and data science courses align with a vision for modernizing instruction set forth in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) project and across 145 journal articles spanning almost a century. In so doing, I illustrate an approach that researchers can take in bringing measurement questions to text data, a method that I believe strikes a useful balance between interpretability, communicability, validity, and scalability.My final study applies these instruments to 32,483 syllabi from US statistics and data science courses taught between 2010 and 2018. I find a modest overall increase in modern approaches over this decade. Finally, I explore differences between institution types using multilevel models, finding that private and four-year institutions, as well as those with higher admissions rates and Pell-recipient populations, have more modern syllabi, though two-year institutions and schools serving fewer Pell recipients seem to be gaining ground.

Subject Added Entry-Topical Term  

Education.

Subject Added Entry-Topical Term  

Statistics.

Subject Added Entry-Topical Term  

Educational tests & measurements.

Subject Added Entry-Topical Term  

Science education.

Index Term-Uncontrolled  

Data science

Index Term-Uncontrolled  

Data science education

Index Term-Uncontrolled  

Measurement

Index Term-Uncontrolled  

Psychometrics

Index Term-Uncontrolled  

Statistics education

Index Term-Uncontrolled  

Text analysis

Added Entry-Corporate Name  

Harvard University Education

Host Item Entry  

Dissertations Abstracts International. 85-12B.

Electronic Location and Access  

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Control Number  

joongbu:655094