Statistics

The goal of the "Quick Twenty" project is to create a quick and fun introduction to STEM songs in each field (biology, math, etc.), making it easy for instructors and administrators to understand how songs can be used for education, and leading to wider adoption of STEM songs for teaching. We think that a "quick twenty list" in each field (not intended to be an objective and authoritative ranking; more like "twenty diverse examples of good songs") will accomplish this.

We posit that a key factor in how effective songs are in students' motivation, engagement, and learning is how interactive the song experience is for students. We articulate and tour a continuum of interactivity, illustrated with examples that are grounded in the context of core learning objectives in mathematics and statistics for students in high school or college, but applicable to virtually any subject matter.

Thousands of STEM songs exist (the singaboutscience.org database alone has over 7000), but they vary widely in how readily a teacher can use them in her class. For some teachers, it may be enough that a song exists on the learning objective at hand, but for (probably, most) teachers, a song is not a self-contained lesson and it makes a huge difference if the song is supported by accompanying resources.

Journals such as the MAA's Convergence reflect the value and interest in using the history of mathematics in the teaching of mathematics. History and song share goals of motivating and humanizing content for students in STEM classes. Illustrated with the specific context of mathematics/statistics class (but applicable to other STEM areas as well), this poster overviews rationale and criteria for use of such songs -- whether instructor-created or student-created -- and includes several examples (e.g., biography songs, key moments in the discipline, etc.) and resources.

Numbers Never Lie will be presented as a music video that explores how statistics can both illuminate and distort issues pertaining to politics and social justice. It is produced by Rock In The Classroom which is a nonprofit organization promoting creative arts in education programs. A draft of the video will be included through the email link on the proposal page. The song examines how statistics can be a powerful tool that can empower historically marginalized populations and bolster arguments on the need to take action on environmental issues.

This presentation focuses on the qualities and quantity of song usage to enhance student learning and depth of understanding. As an AP Statistics teacher, I incorporate over 45 songs into my curriculum to enhance and reinforce everyday learning. This, in turn, has allowed my students to get past basic knowledge and focus on enrichment. I will take you through my song-making process and show you the various ways that my students are able to access the songs. Use of iMovie, YouTube, and Google Sound Cloud will be touched upon.

This presentation focuses on the qualities and quantity of song usage to enhance student learning and depth of understanding. As an AP Statistics teacher, I incorporate over 50 songs into my curriculum to enhance and reinforce everyday learning. This, in turn, has allowed my students to get past basic knowledge and focus on enrichment. I will take you through my song-making process and show you the various ways that my students are able to access the songs. Use of iMovie, YouTube, and Google Sound Cloud will be touched upon.

The presentation will demonstrate the use of music to introduce beginning statistics topics, using an "oldie but goodie" song. The demonstration illustrates how to engage students actively in counting, frequency tables, and graphs - counting words, phrases or musical beats; recording these; and creating graphs to display the generated data. Then, the activity examines using a more recent song appropriate for grade levels 6 – 10 (depending on course set-up) or pre-service teachers to create a frequency table and graph of the data.

We introduce 'voice' package, developed to be a free and easy-to-use set of tools to perform audio analysis in R. Three main approaches are presented: (i) extract_features, (ii) tag and (iii) poetry. Such approaches aim to (i) easily build data frames containing the main features from multiple audio files, (ii) attach summarized columns from audio files to canonical data and (iii) map the best spoken words in the best order. The open datasets (Mozilla) Common Voice and VoxForge are presented to illustrate the main functionalities of (i) and (ii).