Humanoid robots have been successfully used in artistic research areas, and many works have studied and implemented systems for robotic dance. However, only few works take into account the human evaluation of these artistic outputs. This work makes a step in the direction of addressing the complex task of defining criteria for the evaluation of robotic dance performances. For this aim, in the context of a Master course on Fundamentals of Artificial Intelligence (AI), we have organized a challenge among our students and the winner is decided on the basis of a questionnaire we defined for robotic dance evaluation. In addition, we created a public dataset that maps the features of each choreography to the judgements provided by audience with different backgrounds on several evaluation targets. Then, we tested various Machine Learning models for predicting the audience evaluation, and we propose a choreography features importance analysis to help both human choreographers and AI algorithms to create dance performances with a major impact on the audience. We also suggest new directions for future interdisciplinary research.