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According to NASEM (2018), data science has foundations in computing, mathematics, and statistics. However, at the K-12 level, these foundations are usually taught as standalone courses that are unconnected with each other. Students may struggle to see their connections. We proposed a framework unifying those foundations using mathematical logic. A core concept in mathematical logic is function. A general function has one or more possibly non-number inputs and an output. Data science motivates a comprehensive understanding of functions and provides extensive culturally relevant, real-world, and data-rich problems and applications for students to practice their understanding. It is interesting to know how well students understand functions. We developed a six-lesson online module with more than 100 in-lesson questions. Initial analysis of the students’ answers to the questions shows that students can understand the basics of the general functions but have more difficulties in involved applications of functions. 

Data science is revolutionizing academia and industry, creating a high demand for a workforce fluent in this field. While the availability of data science courses has increased recently, few curricula rigorously build on mathematical logic. The LogicDS Project addresses this gap by engaging high school students from rural communities in an online data science course integrating mathematics, statistics, and programming into a unified framework based on logic and reasoning. A one-week course, consisting of six lessons, was developed and 110 participants were recruited. Pre- and post-intervention data, along with students' LMS activity logs, were collected to analyze engagement. Results indicate that the Logic-Based framework effectively engages students from diverse backgrounds, with participants finding the course valuable for learning data science skills. Notably, entropy analysis of student activity logs correlated with other mixed methods analyses, providing insights into engaging K-12 students in data science education. 

The increasing use of machine learning and Large Language Models (LLMs) opens up opportunities to use these artificially intelligent algorithms in novel ways. This article proposes a methodology using LLMs to support traditional deductive coding in qualitative research. We began our analysis with three different sample texts taken from existing interviews. Next, we created a codebook and inputted the sample text and codebook into an LLM. We asked the LLM to determine if the codes were present in a sample text provided and requested evidence to support the coding. The sample texts were inputted 160 times to record changes between iterations of the LLM response. Each iteration was analogous to a new coder deductively analyzing the text with the codebook information. In our results, we present the outputs for these recursive analyses, along with a comparison of the LLM coding to evaluations made by human coders using traditional coding methods. We argue that LLM analysis can aid qualitative researchers by deductively coding transcripts, providing a systematic and reliable platform for code identification, and offering a means of avoiding analysis misalignment. Implications of using LLM in research praxis are discussed, along with current limitations.