Abstract:

Greek Sign Language (GSL), as well as most researched Sign Languages (SLs), possess a unique typological class of classifiers with distinct syntactic and semantic properties. GSL classifiers mark a predicate for specific geometrical properties as well as for position, movement and number, and they make obligatory use of the three-dimensional (3D) signing space. Their distribution across different linguistic levels in GSL is high and they constitute a critical subject in GSL learning as a second language (L2). On the contrary, spoken language classifiers consist of linear sequences of words and do not carry a role as central as that of SL classifiers, while there is no evidence for any classifier structures in Indo-european spoken languages or in spoken Greek. Moreover, applied linguistics and teaching methodologies on 3D GSL phenomena such as classifiers require use of state-of-the-art technological applications on dynamic SL representation. However, most GSL online curricula have yet not included classifiers in teaching and learning and still focus on lexeme sequences similar to those of spoken languages with established written forms, not taking advantage of the full potential of SL technologies. As a result, GSL classifiers are a challenging area for L2 learners to acquire and produce, while they are also a challenge for an online curriculum of GSL as an L2.

In the present paper we describe the SL-ReDu model and related educational content for teaching classifiers. SL-ReDu is an innovative interdisciplinary project exploiting technological advances in automatic SL recognition and language education methodologies for GSL teaching using L2 principles. SL-ReDu incorporates SL technologies of video transmission as well as video recognition and automatic translation into a platform for teaching areas of GSL structure unique to SLs such as classifiers and addresses the needs of the students attending the GSL foundation course at ‘true beginner’s’ level (A0-A1) at the University of Thessaly, Greece. In this manner any number of students can use their personal computer and web camera to produce classifiers and receive automatic feedback on their output, without the need of any additional equipment or software. Thus, the SL-ReDu platform is fully compliant with online L2 learning and compatible with 3D classifier presentation, comprehension, production and testing activities irrespective of the number of students attending, number of tutors involved or hours demanded for study.

Keywords:

Sign language learning, second language learning, self assessment, online testing, sign language recognition, classifiers.