Enriching teachers’ assessments of rhythmic Forró dance skills by modelling motion sensor data

Abstract

Rhythmic skills are fundamental in social partner dance. Yet, large class sizes and time constraints often make it challenging for teachers to assess learners’ rhythmic skills to provide constructive feedback. As motion sensors are becoming widely available and embedded in most smartphones, it is also becoming feasible to create models of anatomical movements of the human body that could be used to support such teachers’ assessments. Current solutions aimed at modelling motor skills using motion sensors and artificial intelligence (AI) have enabled the detection of some dimensions of rhythm, but they are either technically too complex to be used ‘in-the-wild’ or have not been designed based on dance teachers’ assessment needs. This paper presents an approach for modelling rhythmic movements, via a single smartphone, while learners learn how to dance Forró. The purpose is to enrich teachers’ rhythmic assessments with data. Following a user-centred approach, we first elicited from teachers the dance elements that they commonly focus their rhythm assessments on (i.e., tempo, pause, step size and weight transfer). Then, selected features were extracted from raw motion sensor data related to the rhythmic patterns of learners dancing, their synchronisation with the beat of the music, and particular characteristics of the song being played. Finally, machine learning (ML) algorithms were used to create predictive computational models using these features. The modelling approach was validated through two studies: (1) a quantitative comparison between the ML outputs and dance teachers assessments of learners’ dance performance; and (2) a qualitative analysis of the potential pedagogical uses of the outputs of the ML models envisioned by dance teachers.