ion of verb agreement by hearing learners of a sign language. During a 2-year period, 14 novel learners of Sign Language of the Netherlands (NGT) with a spoken language background performed an elicitation task 15 times. Seven deaf native signers and NGT teachers performed the same task to serve as a benchmark group. The results obtained show that for some learners, the verb agreement system of NGT was difficult to master, despite numerous examples in the input. As compared to the benchmark group, learners tended to omit agreement markers on verbs that could be modified, did not always correctly use established locations associated with discourse referents, and made characteristic errors with respect to properties that are important in the expression of agreement (movement and orientation). The outcomes of the study are of value to practitioners in the field, as they are informative with regard to the nature of the learning process during the first stages of learning a sign language.
The aim of this dissertation is to examine how adult learners with a spoken language background who are acquiring a signed language, learn how to use the space in front of the body to express grammatical and topographical relations. Moreover, it aims at investigating the effectiveness of different types of instruction, in particular instruction that focuses the learner's attention on the agreement verb paradigm. To that end, existing data from a learner corpus (Boers-Visker, Hammer, Deijn, Kielstra & Van den Bogaerde, 2016) were analyzed, and two novel experimental studies were designed and carried out. These studies are described in detail in Chapters 3–6. Each chapter has been submitted to a scientific journal, and accordingly, can be read independently.1 Yet, the order of the chapters follows the chronological order in which the studies were carried out, and the reader will notice that each study served as a basis to inform the next study. As such, some overlap in the sections describing the theoretical background of each study was unavoidable.
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Over the past few years, there has been an explosion of data science as a profession and an academic field. The increasing impact and societal relevance of data science is accompanied by important questions that reflect this development: how can data science become more responsible and accountable while also responding to key challenges such as bias, fairness, and transparency in a rigorous and systematic manner? This Patterns special collection has brought together research and perspective from academia, the public and the private sector, showcasing original research articles and perspectives pertaining to responsible and accountable data science.
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