Toward Automating Oral Presentation Scoring During Principal Certification Program Using Audio-Video Low-Level Behavior Profiles

Effective leadership bears strong relationship to attributes of emotion contagion, positive mood, and social intelligence. In fact, leadership quality has been shown to be manifested in the exhibited communicative behaviors, especially in settings of public speaking. While studies on the theories of leadership has received much attention, little has progressed in terms of the computational development in its measurements. In this work, we present a behavioral signal processing (BSP) research to assess the qualities of oral presentations in the domain of education, in specific, we propose a multimodal framework toward automating the scoring process of pre-service school principals' oral presentations given at the yearly certification program. We utilize a dense unit-level audio-video feature extraction approach with session-level behavior profile representation techniques based on bag-of-word and Fisher-vector encoding. Furthermore, we design a scoring framework, inspired by the psychological evidences of human's decision-making mechanism, to use confidence measures outputted from support vector machine classifier trained on the distinctive set of data samples as the regressed scores. Our proposed approach achieves an absolute improvement of 0.049 (9.8 percent relative) on average over support vector regression. We further demonstrate that the framework is reliable and consistent compared to human experts.