A corpus-driven error analysis of the oral and written production of Leaving Certificate Spanish learners

The Leaving Certificate (LC) is the national, standardised state examination in Ireland necessary for entry to third level education – this presents a massive, raw corpus of data with the potential to yield invaluable insight into the phenomena of learner interlanguage. With samples of official LC Spanish examination data, this project has compiled a digitised corpus of learner Spanish comprised of the written and oral production of 100 candidates. This corpus was then analysed using a specific investigative corpus technique, Computer-aided Error Analysis (CEA, Dagneaux et al, 1998). CEA is a powerful apparatus in that it greatly facilitates the quantification and analysis of a large learner corpus in digital format. The corpus was both compiled and analysed with the use of UAM Corpus Tool (O’Donnell 2013). This Tool allows for the recording of candidate-specific variables such as grade, examination level, task type and gender, therefore allowing for critical analysis of the corpus as one unit, as separate written and oral sub corpora and also of performance per task, level and gender. This is an interdisciplinary work combining aspects of Applied Linguistics, Learner Corpus Research and Foreign Language (FL) Learning. Beginning with a review of the context of FL learning in Ireland and Europe, I go on to discuss the disciplinary context and theoretical framework for this work and outline the methodology applied. I then perform detailed quantitative and qualitative analyses before going on to combine all research findings outlining principal conclusions. This investigation does not make a priori assumptions about the data set, the LC Spanish examination, the context of FLs or of any aspect of learner competence. It undertakes to provide the linguistic research community and the domain of Spanish language learning and pedagogy in Ireland with an empirical, descriptive profile of real learner performance, characterising learner difficulty.

A programmer's environment for music analysis

A model for representing music scores in a form suitable for general processing by a music-analyst-programmer is proposed and implemented. Typical input to the model consists of one or more pieces of music which are encoded in a file-based score representation. File-based representations are in a form unsuited for general processing, as they do not provide a suitable level of abstraction for a programmer-analyst. Instead, a representation is created giving a programmer's view of the score. This frees the analyst-programmer from implementation details, that otherwise would form a substantial barrier to progress. The score representation uses an object-oriented approach to create a natural and robust software environment for the musicologist. The system is used to explore ways in which it could benefit musicologists. Methodologies for analysing music corpora are presented in a series of analytic examples which illustrate some of the potential of this model. Proving hypotheses or performing analysis on corpora involves the construction of algorithms. Some unique aspects of using this score model for corpus-based musicology are: - Algorithms impose a discipline which arises from the necessity for formalism. - Automatic analysis enables musicologists to complete tasks that otherwise would be infeasible because of limitations of their energy, attentiveness, accuracy and time.