A MOOC on Approaches to Machine Translation
ARTICLE
Marta Ruiz Costa-juss, Instituto Politcnico Nacional ; Lluis Formiga, Verbio ; Oriol Torrillas, Jordi Petit, Jos Rodrguez Fonollosa, Universitat Politcnica de Catalunya
IRRODL Volume 16, Number 6, ISSN 1492-3831 Publisher: Athabasca University Press
Abstract
This paper describes the design, development and analysis of a MOOC entitled “Approaches to Machine Translation: rule-based, statistical and hybrid” providing lessons learnt on conclusions to be take into account in the future. The course was developed within a Canvas platform, used by recognized European universities. The course contains video-lectures, quizzes and laboratory assignments. Evaluation is done across on-line quizzes, programming assignments (PAs) evaluated by means of a specific code evaluation and peer-to-peer strategies. This MOOC allows to introduce people from various areas to the Machine Translation theory and practice. It also allows to internationally publisize different tools developed at the Universitat Polit`ecnica de Catalunya.
Citation
Ruiz Costa-juss, M., Formiga, L., Torrillas, O., Petit, J. & Rodrguez Fonollosa, J. (2015). A MOOC on Approaches to Machine Translation. The International Review of Research in Open and Distributed Learning, 16(6),. Athabasca University Press. Retrieved March 28, 2024 from https://www.learntechlib.org/p/171115/.
Keywords
References
View References & Citations Map- Abeer, W., & Miri, B. (2014). Students’ preferences and views about learning in a MOOC. Procedia-Social and Behavioral Sciences, 152, 318–323.
- Costa-jussà, M.R. (2013). Morphological, syntactical and semantic knowledge in statistical machine translation. In NAACL HLT 2013 Tutorial Abstracts (pp. 16–18). Atlanta, USA:
- Harms, R. (2015). Self-regulated learning, team learning and project performance in
- Kolp, M., & Wautelet, Y. (2014). Computers in human behavior human organizational patterns applied to collaborative learning software systems. Computers in Human Behavior, 1–10.
- Yang, Z., Liu, Z., Liu, S., Min, L., & Meng, W. (2014). Adaptive multi-view selection for semisupervised emotion recognition of posts in online student community. Neurocomputing, 144, 138–150.
These references have been extracted automatically and may have some errors. Signed in users can suggest corrections to these mistakes.
Suggest Corrections to References