Learnersourcing in Humanities and Social Sciences

This paper presents the results of learnersourcing in a multidisciplinary setting, where Linguistics students (Humanities) and students of Information Sciences (Social Sciences) engaged in a sentiment annotation task. The student evaluations of a task, the learnersourcing platform and student motivation are also presented. The learnersourcing task was connected to learning goals of three academic courses (Language Engineering, Translator and the Computer and Corpus Linguistics). A total of 62 students between the age of 22 to 24 participated in a learnersourcing task marking Croatian movie review sentences (~5000 sentences per student) with five categories of coarse sentiment. They also participated in a machine translation (MT) evaluation task consisting of a comprehension test and rating of MT output, as well as in an exercise involving error quantification and classification in sample texts written in several languages and translated by machine into Croatian.