arXiv subjects

Nicholas Jon Horton

Publications and source records attributed to Nicholas Jon Horton.

5 recordsLinked to original sources

arXiv2026

Pivoting the paradigm: the role of spreadsheets in K-12 data science

Spreadsheet tools are widely accessible to and commonly used by K-12 students and teachers. While spreadsheets are not ideal for many types of statistical analysis, they have an important role in data collection and organization. From a pedagogical standpoint, spreadsheets make data visible and easy to interact with, facilitating student engagement in data exploration, analysis, and computation. Though not suitable for all tasks, spreadsheets can facilitate learning and practicing data and computing skills for K-12 students. This paper 1) demonstrates the potential utility of spreadsheets in K-12; 2) reviews prior frameworks and standards that are relevant for K-12 data tools; and 3) proposes data-driven data skills to help develop data acumen and computational fluency. We provide some example activities, identify challenges and barriers to adoption, suggest pedagogical approaches to ease the learning curve for instructors and students, and discuss the need for professional development to facilitate deeper use of spreadsheets for data science and STEM disciplines.

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arXiv2018

Data scraping, ingestation, and modeling: bringing data from cars.com into the intro stats class

New tools have made it much easier for students to develop skills to work with interesting data sets as they begin to extract meaning from data. To fully appreciate the statistical analysis cycle, students benefit from repeated experiences collecting, ingesting, wrangling, analyzing data and communicating results. How can we bring such opportunities into the classroom? We describe a classroom activity, originally developed by Danny Kaplan (Macalester College), in which students can expand upon statistical problem solving by hand-scraping data from cars.com, ingesting these data into R, then carrying out analyses of the relationships between price, mileage, and model year for a selected type of car.

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arXiv2017

Data Visualization on Day One: Bringing Big Ideas into Intro Stats Early and Often

In a world awash with data, the ability to think and compute with data has become an important skill for students in many fields. For that reason, inclusion of some level of statistical computing in many introductory-level courses has grown more common in recent years. Existing literature has documented multiple success stories of teaching statistics with R, bolstered by the capabilities of R Markdown. In this article, we present an in-class data visualization activity intended to expose students to R and R Markdown during the first week of an introductory statistics class. The activity begins with a brief lecture on exploratory data analysis in R. Students are then placed in small groups tasked with exploring a new dataset to produce three visualizations that describe particular insights that are not immediately obvious from the data. Upon completion, students will have produced a series of univariate and multivariate visualizations on a real dataset and practiced describing them.

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arXiv2015

Challenges and opportunities for statistics and statistical education: looking back, looking forward

The 175th anniversary of the ASA provides an opportunity to look back into the past and peer into the future. What led our forebears to found the association? What commonalities do we still see? What insights might we glean from their experiences and observations? I will use the anniversary as a chance to reflect on where we are now and where we are headed in terms of statistical education amidst the growth of data science. Statistics is the science of learning from data. By fostering more multivariable thinking, building data-related skills, and developing simulation-based problem solving, we can help to ensure that statisticians are fully engaged in data science and the analysis of the abundance of data now available to us.

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arXiv2013

I hear, I forget. I do, I understand: a modified Moore-method mathematical statistics course

Moore introduced a method for graduate mathematics instruction that consisted primarily of individual student work on challenging proofs (Jones, 1977). Cohen (1982) described an adaptation with less explicit competition suitable for undergraduate students at a liberal arts college. This paper details an adaptation of this modified Moore-method to teach mathematical statistics, and describes ways that such an approach helps engage students and foster the teaching of statistics. Groups of students worked a set of 3 difficult problems (some theoretical, some applied) every two weeks. Class time was devoted to coaching sessions with the instructor, group meeting time, and class presentations. R was used to estimate solutions empirically where analytic results were intractable, as well as to provide an environment to undertake simulation studies with the aim of deepening understanding and complementing analytic solutions. Each group presented comprehensive solutions to complement oral presentations. Development of parallel techniques for empirical and analytic problem solving was an explicit goal of the course, which also attempted to communicate ways that statistics can be used to tackle interesting problems. The group problem solving component and use of technology allowed students to attempt much more challenging questions than they could otherwise solve.

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