Abstract:

There has been an increased interest in the use of Open Educational Resources (OERs) in language teaching and learning in the past decade (Comas-Quinn, Beaven, & Sawhill, 2019; Comas-Quinn & Borthwick, 2015), with a variety of practices that range from integrating open pedagogies to using open technologies, as the present study shows. The open source software G-Rubric makes use of the latest developments in Data Driven Learning (DDL) and Natural Language Processing (Paredes et al, 2018) to provide automated and personalised feedback to students’ written production in English for specific purposes. G-Rubric is a corpus-based automatic system which makes use of semantic technologies to assess students’ written production, comparing students’ submissions with a corpus-based model answer and rubric that have been embedded withing the system (hence the name, G-Rubric), and provides them with guidelines for improvement of their writing, focusing on the specialised linguistic content, since G-Rubric is a tool based on semantic technologies, as previously stated (Lancho et al., 2018).

This presentation focuses on the piloting of the tool, which compared the assessment provided by the teachers to the written compositions submitted by students completing the university course “English for Professional Purposes”, in the first year of the Degree in Tourism at the Spanish University for Distance Education (UNED), and the feedback elicited by the G-Rubric tool. Results show that G-Rubric offers more specific, systematic feedback, identifying the linguistic patterns and lexis which should be used in English for Professional Purposes, whereas teachers’ comments are more holistic and impressionistic, and do not necessarily concentrate on the particular characteristics languages for specific purposes (LSPs).

These findings have pedagogical implications for LSP instruction, since they open the door to new teaching practices in assessment of student learning: the use of G-Rubric may be beneficial not only for students, who get personalised and automatic feedback for their written production in English, but also for teachers, who will make their assessment more systematic and focused on the specificities of LSP if they use G-Rubric when marking students’ compositions. One of the strengths of DDL is how it identifies patterns of language at all levels; teachers should make use of these advancements in corpus linguistics, which will aid them in noticing the semantic nuances of LSP.

References:
[1] Comas-Quinn, A., Beaven, A., & Sawhill, B. (Eds.). (2019). New case studies of openness in and beyond the language classroom. Research-publishing.net.
[2] Comas-Quinn, A., & Borthwick, K. (2015). Sharing: Open Educational Resources for Language Teachers. In R. Hampel & U. Stickler, Developing Online Language Teaching (pp. 96–112). New York: Palgrave Macmillan. https://doi.org/10.1057/9781137412263_7
[3] Lancho, M. S., Hernández, M., Paniagua, Á. S. E., Encabo, J. M. L., & De Jorge-Botana, G. (2018). Using semantic technologies for formative assessment and scoring in large courses and MOOCs. Journal of Interactive Media in Education, 2018(1), 1–10. https://doi.org/10.5334/jime.468
[4] Pérez-Paredes, P., Ordoñana Guillamón, C., & Aguado Jiménez, P. (2018). Language teachers’ perceptions on the use of OER language processing technologies in MALL. Computer Assisted Language Learning, 31(5–6), 522–545. https://doi.org/10.1080/09588221.2017.1418754

Keywords:

Open Technologies, LSP, G-Rubric, OER.