- Effect of formulaic sequences on fluency of English learners in standardized speaking tests
-
...Xu, 2018).
Table 3
Examples of Standard FS
Formulaic Language Sequence Mean MICASE Tokens per 10,000 Words
according to 2.04
this is what 1.76
compared to 1.36
says in the 0.78
but then 2.34
...
by Jennifer François, Mohammed Albakry
in Volume 25 Number 2, June 2021
- Review of Multilingual Corpora in Teaching and Research
-
...Xu
Xin of the University of Lancaster, addresses the challenges for multilingual parallel corpus research
posed by non-European and non-Indo-European languages. Many new methods are still needed, and ...
by John M. Lawler
in Volume 05 Number 3, September 2001 Special Issue Using Corpora in Language Teaching and Learning
- Multilevel language tests: Walking into the land of the unexplored
-
...Xu (2017), who value the auto-marker’s scoring accuracy,
concluding that it is very satisfactory and reliable.
Oxford Test of English
Like Linguaskill, the Oxford Test of English is a computer ada...
by Jesus García Laborda, Miguel Fernández Álvarez
in Volume 25 Number 2, June 2021
- Vocabulary learning through a daily task of cooking in the Digital Kitchen
-
...Xu, 2010 for reviews), clearly indicating how
a dual-coded approach using computer-mediated education tools can have a positive influence on
vocabulary acquisition.
However, very little attention h...
by Jaeuk Park
in Volume 26 Number 1, 2022
- Automatic pronunciation assessment vs. automatic speech recognition: A study of conflicting conditions for L2-English
-
...Xu, 2018).
The important point is that while assessing contextualized words/sentences seems possible, the
assessment of isolated words has remained a challenge (Cheng, 2018). However, improvement th...
by Enrique Cámara-Arenas, Cristian Tejedor-García, Cecilia Judith Tomas-Vázquez, David Escudero-Mancebo
in Volume 27 Number 1, 2023
- Computer-assisted pronunciation training for foreign language learning of grammatical features
-
...Xu, Q., Goyal, N., Singh, K., von Platen, P., Saraf, Y.,
Pino, J., Baevski, A., Conneau, A., & Auli, M. (2022). XLS-R: Self-supervised cross-lingual
speech representation learning at scale. Proceedi...
by Elsayed Issa, Gus Hahn-Powell
in Volume 29 Number 1, 2025
- Comparing individual vs. collaborative processing of ChatGPT-generated feedback: Effects on L2 writing task improvement and learning
-
...xus between the ecological and leaner-centered perspectives on feedback and the
practice of AF in L2 pedagogy, where researchers used to believe that such tools did not offer appropriate
communicati...
by Da Yan
in Volume 28 Number 1, 2024
- The accuracy of computer-assisted feedback and students’ responses to it
-
...Xu, 2009). The immediacy of the feedback is what essentially sets apart
computer-assisted feedback from human feedback. Although the topic of immediate feedback has not
been well researched in the L...
by Elizabeth Lavolette, Charlene Polio, Jimin Kahng
in Volume 19 Number 2, June 2015
- Investigating linguistic, literary, and social affordances of L2 collaborative reading
-
...Xu, & Hardaker, 2007). DATs also
allow learners to share annotations, which subsequently means that reading is no longer simply an
individual process but can also be a collaborative one (Novak, Razz...
by Joshua J. Thoms, Frederick Poole
in Volume 21 Number 2, June 2017
- Interactive digital textbooks and engagement: A learning strategies framework
-
...Xu, 2015), increased motivation (Huang, 2013), and, if implemented correctly, hypertextuality
(DeStefano & LeFevre, 2007). Students need sufficient time, however, in learning to take advantage of
th...
by Dawn Bikowski, Elliott Casal
in Volume 22 Number 1, February 2018
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