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The development of real-time affect detection models often depends uponobtaining annotated data for supervised learning by employing human experts tolabel the student data. One open question in annotating affective data foraffect detection is whether the labelers (i.e., human experts) need to besocio-culturally similar to the students being labeled, as this impacts thecost feasibility of obtaining the labels. In this study, we investigate thefollowing research questions: For affective state annotation, how does thesocio-cultural background of human expert labelers, compared to the subjects,impact the degree of consensus and distribution of affective states obtained?Secondly, how do differences in labeler background impact the performance ofaffect detection models that are trained using these labels?

The Importance of Socio-Cultural Differences for Annotating and Detecting the Affective States of Students