This article focuses on three “hacks” to the traditional model of music theory instruction:standards-based grading(SBG),just-in-time teaching(JiTT), andthe inverted classroom. In SBG, students receive multiple grades in reference to clearly defined learning objectives rather than a single grade that may mask weaknesses by averaging them with strengths. JiTT assesses students’ understanding before class so that the instructor can adjust the lesson plan according to their needs. In the inverted classroom, students acquire a basic understanding of the material outside of class so that class time may be used for active engagement rather than lecture. These tools and the technologies that support them have the potential to help strengthen curricula, increase the impact that an instructor can have on undergraduate theory students, and in some cases reduce the amount of time an instructor must devote to achieving that impact. Any one of these hacks can be incorporated within an otherwise traditional music theory course and can work together synergistically. Both their modularity and their compatibility give them potential to increase instructor efficiency and effectiveness in a wide range of class settings. After describing the scholarly literature surrounding the three hacks as they have been incorporated in a variety of academic disciplines, the authors present four testimonials describing how the three hacks have been incorporated into four diverse music programs: a traditional school of music within a large state university, a large private institution with a focus on jazz and commercial music, a department of music in a mid-sized university, and a small, private university with music classes that combine theory and aural skills.

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We set out to perform a cluster analysis of harmonic structures (specifically, chord-to-chord transitions) in the McGill Billboard dataset, to determine whether there is evidence of multiple harmonic grammars and practices in the corpus, and if so, what the optimal division of songs, according to those harmonic grammars, is. We define optimal as providing meaningful, specific information about the harmonic practices of songs in the cluster, but being general enough to be used as a guide to songwriting and predictive listening. We test two hypotheses in our cluster analysis — first that 5–9 clusters would be optimal, based on the work of Walter Everett (2004), and second that 15 clusters would be optimal, based on a set of user-generated genre tags reported by Hendrik Schreiber (2015). We subjected the harmonic structures for each song in the corpus to a K-means cluster analysis. We conclude that the optimal clustering solution is likely to be within the 5–8 cluster range. We also propose that a map of cluster types emerging as the number of clusters increases from one to eight constitutes a greater aid to our understanding of how various harmonic practices, styles, and sub-styles comprise the McGill Billboard dataset.

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