Conference Agenda

As data-driven decision making becomes increasingly vital across industries, skills in online data visualization and analysis are in high demand. While many colleges now offer coursework in data analytics and visualization, providing earlier access to these concepts can better prepare motivated high school students for advanced study and early internship opportunities in a competitive job market.

This study presents the design and implementation of a four-week summer curriculum in data visualization and analysis tailored for high school students. The program meets four days per week for six hours per day, combining three hours of lecture with three hours of hands-on lab work. The curriculum focuses on three-tier architecture for data visualization design, development, and analysis. No prior programming experience is required; however, students are expected to have completed Algebra and to demonstrate strong interest in computing.

The learning outcomes of the curriculum are for students to:

• understand the purpose and impact of data visualization and analysis;

• develop online data visualization and analysis applications;

• create interactive visualizations that support user engagement; and

• apply storytelling techniques to communicate insights and patterns.

Students learn the fundamentals of Structured Query Language (SQL) to query and aggregate data within a MySQL database; PHP and MySQL functions for application-layer development; and JavaScript, event handling, and the Google Charts Library for presentation-layer design. Instruction covers core chart types—including line, pie, bar, histogram, and scatter plots—and guides students through retrieving data from a database and visualizing it effectively. Analytical components introduce students to interpreting distributions, identifying correlations and outliers, and validating findings with AI-assisted tools such as ChatGPT.

Overall, this accelerated curriculum provides high-achieving high school students with foundational skills in computer science, data science, and AI-related fields, positioning them for early success in college-level coursework, internships, and future career pathways.

As part of a NSF S-STEM grant, Elms College has piloted requiring students who are STEM scholars to take two cohort-based interdisciplinary Data Science courses to strengthen student skills and student engagement across STEM disciplines. First-year STEM scholars across the majors of Biology, Biotechnology, Chemistry, Computer Science, Comp. Info. Tech. and Security, and Data Science & AI, enroll as a cohort in a newly developed course, Computational Statistics, which introduces statistics, data analysis, and programming using R with example data across the sciences and meets a math core requirement. And transfer STEM scholars and scholars in their junior year are required to take a Data Analytics & Visualization course in Python in Computer Science which includes a final project where students choose to work with real-world data in their disciplines. These cohort-based courses prepare students for advanced research in STEM fields that require students to apply data analysis tools to real-world scientific questions. Embedding data science across disciplines has helped students develop transferable skills in quantitative reasoning, computational skills, and data analysis. The shared learning experiences promote peer support and sustained engagement, particularly for students entering with diverse academic backgrounds. Since 2022, 47 STEM scholars have participated in these cohort-based data science courses, with 26 currently active students, providing multiple cohorts for observing patterns of engagement and skill development. In addition to positive student feedback and increased participation in research, internships, or advanced courses, the initiative has contributed to growth in the institution’s Data Analytics minor, Computer Science, and Data Science and AI major. This work highlights how interdisciplinary, cohort-based data science instruction can serve as a powerful mechanism for enhancing student learning and engagement across STEM programs.

While teaching discrete mathematics using open education materials, I observed that many students needed additional practice with immediate, targeted feedback. Standard non-interactive practice problem sets suffer from the limitation that they must either be graded by the instructor or distributed with solutions so that the student can check their own work. Instructor-graded problems necessarily limit the time that a student can usefully spend practicing. Problem sets distributed with solutions present a temptation to the student to read the answers rather than attempt the problems independently and limit the amount of feedback a student can receive per problem, since they are typically not able to detect a mistake at any step without viewing the complete solution.

I developed a web application that provides scaffolded practice in constructing truth tables for arbitrary logical statements, and optionally for using the resulting truth table to answer questions on equivalence and argument-form validity. I also created a series of procedural question generators, allowing students to encounter a wide range of distinct problems.

When using the application, a student is first asked to indicate all logical operators in a given statement. Once this task is completed, the application highlights each operator in turn, asking the student to indicate all symbols (whether logical operators or statement variables) belonging to sub-statements of the indicated operator. After successfully processing all operators, the student is asked to choose an order in which to evaluate the sub-statements when constructing the truth table. If a valid order is specified, the student is provided with a blank truth table to fill in, with the columns in their chosen order. For logical equivalence and argument-form validity questions, the student is further asked to identify the columns and, for argument questions, rows needed to answer the final question. This structure allows feedback to be immediately provided at each intermediate stage of reasoning, rather than only on final answers.

For each student, the application asks a series of questions of escalating complexity before switching to a free-practice mode once the student has successfully completed their question series. Because questions are generated procedurally, each student will continue to receive fresh questions during free-practice, though since questions are of finite size duplicates may be encountered as well during free-practice.

In this poster, I present my experiences designing the software and using it during the Fall 2025 semester and the first part of the Spring 2026 semester, including observations about how the application supported more flexible and sustained student engagement with discrete mathematics practice.