Despite the growing interest in sign language acquisition, learner corpora and datasets remain scarce, with many studies focusing primarily on isolated signs. Expanding the scope from single signs to sentences requires continuous sign language data, which introduces additional complexity due to the need to analyse both manual and non-manual components of the language. This chapter presents the compilation process and exploitation of a longitudinal learner (L2) corpus for Swiss German Sign Language (DSGS), one of three sign languages used in Switzerland. The L2 data are complemented by a parallel control subcorpus of native signers (L1). Both subcorpora are designed to support research in sign language linguistics, language assessment, and the training of computer-vision-based sign language recognition systems. The implications of this corpus extend beyond linguistic research, offering practical applications for developing automatic feedback tools to support L2 learners while learning DSGS. This chapter outlines the data collection process, from participant recruitment to data processing and annotation, and presents preliminary results from exploratory analyses of non-manual components using L1 data.