Proceedings of the 17th Annual Conference of the European Association for Machine Translation

Proceedings of the 17th Annual Conference of the European Association for Machine Translation (EAMT

Learning English as a Foreign Language in a Blended Mode of Face-to-face and Online Discussions: A Case Study in a University in Taiwan

Learning English as a Foreign Language in a Blended Mode of Face-to-face and Online Discussions: A Case Study in a University in Taiwan Previous studies have documented many beneficial results arising from integrating online discussion with face-to-face instruction for language learning, yet the interactive process of students within both formal and informal contexts remains to be explored. This research examined the dynamics of student learning in blended face-to-face and online discussions in and after class in the context of learning English as a foreign language (EFL) in a university in Taiwan. An embedded case study was applied with a mixed-methods approach to investigate how students jointly accomplished tasks, and how this blended approach had contributed to their English learning. The data collected include the qualitative data of observations on three groups of 14 participants, three focus groups with 11 participants, 72 online discussion logs of the three groups and the quantitative data of 45 questionnaire responses. The findings revealed that students learned primarily through mediation of L1 and L2, through collaborative interaction, through co-construction of meaning, and from teacher and peer scaffolds. Students tended to provide information and suggestions in face-to-face discussions by using L1, but they expressed thoughts, gave comments and probed questions in online discussions by using L2. Students changed their interactive patterns from passive to active by mutually assisting each other in accomplishing tasks. Data also showed that students recognised that blended discussions had contributed to their cognitive, language, interactional and affective gains. Blended discussions were perceived as learner-centred undertakings that increased participation, collaboration and engagement. Four key factors were observed to have affected learning in this blended instruction. The research concludes that blended discussions changed the conventional EFL classroom culture and had a positive influence on student learning in terms of interaction, processes of meaning construction and perceptions. Keywords Online Discussion, Computer-Mediated Communication, Computer-Assisted Language Learning, Blended Learning, Collaborative interaction, Co-construction of Meanin

A machine learning taxonomic classifier for science publications

Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas