  The development of real-time affect detection models often depends upon
obtaining annotated data for supervised learning by employing human experts to
label the student data. One open question in annotating affective data for
affect detection is whether the labelers (i.e., human experts) need to be
socio-culturally similar to the students being labeled, as this impacts the
cost feasibility of obtaining the labels. In this study, we investigate the
following research questions: For affective state annotation, how does the
socio-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 of
affect detection models that are trained using these labels?
