Author: perezparedes

Extending corpus linguistics methods to education research

University of Exeter

Language & Education Network Research Seminar, 22 February 2021.

Abstract

Corpora have been widely used in applied linguistics research and, to a lesser extent, in other fields such as political science or sociology. However, corpus research methods are rarely taught in education faculties. I will discuss different approaches to using CL methods in education research and examine the underlying assumptions that may justify distinguishing between corpus linguistics (CL) as a methodology and as a set of methods. This talk seeks to contribute to the advancement of the debate about how CL can position itself within the wide spectrum of current educational research methods.

References

Bednarek, M., Pinto, M. V., & Werner, V. (2021). Corpus approaches to telecinematic language. International Journal of Corpus Linguistics, 26(1), 1-9.


Cameron, D. & Panović, I. (2014). Corpus-based discourse analysis. In Working with written discourse (pp. 81-96). Sage.


Cohen, L., Manion, L. & Morrison, K. (2018) Research methods in education. Routledge.


Creswell, J. W. & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.

Durrant, P., Brenchley, M., & McCallum, L. (2021). Understanding development and proficiency in writing: quantitative corpus linguistic approaches. Cambridge University Press.


Fest, J. (2015). Corpora in the Social Sciences-How corpus-based appraches can support qualitative interview analyses. LFE. Revista de Lenguas para Fines Específicos, 21,2, 48-69.


Gianfreda, S. (2019). Using a mixed-method approach to examine party positioning on immigration and the european union in parliamentary proceedings.In SAGE Research Methods Cases.


Leech, G. (2000). Grammars of spoken English: New outcomes of corpus‐oriented research. Language learning, 50(4), 675-724.


Leavy, P. (2017). Research design: Quantitative, qualitative, mixed methods, arts-based, and community-based participatory research approaches. The Guildford Press.

Pérez-Paredes, P. (2020). Corpus Linguistics for Education: A Guide for Research. Routledge.


Seale, C. & Charteris-Black, J. (2010). Keyword analysis: a new tool for qualitative research. In The SAGE handbook of qualitative methods in health research (pp. 536-556). Sage.


Sealey, A., & Thompson, P. (2004). ‘What do you call the dull words?’Primary school children using corpus-based approaches to learn about language. English in Education, 38(1), 80-91.


Wright, D. (2017). Using word n-grams to identify authors and idiolects: A corpus approach to a forensic linguistic problem. International Journal of Corpus Linguistics, 22(2), 212-241.


Vessey, R. (2013). Challenges in cross-linguistic corpus-assisted discourse studies. Corpora, 8(1), 1-26.


Vessey, R. (2017). Representations of language education in Canadian newspapers. Canadian Modern Language Review, 73(2), 158-182.

An equitable CALL / SLA interface

faceless schoolchildren watching video on cellphone during break in classroom
Photo by Katerina Holmes on Pexels.com

From Ortega, L. (2017) New CALL-SLA Research Interfaces for the 21st Century: Towards Equitable Multilingualism. Calico Journal, 34.3, 285–316.

The majority of the world is multilingual, but inequitably multilingual, and much of the world is also technologized, but inequitably so. Thus, researchers in the fields of computer-assisted language learning (CALL) and second language acquisition (SLA) would profit from considering multilingualism and social justice when envisioning new CALL-SLA interfaces for the future. 

I remain convinced that “in the ultimate analysis, it is not the methods or the epistemologies [or the theories] that justify the legitimacy and quality of human research, but the moral-political purposes that guide sustained research efforts” (Ortega, 2005, p. 438). The need to incorporate ethics and axiology in the study of language learning seems all the more acute in our present world, where human solidarity and respect for human diversity, including linguistic diversity, is under siege, creating serious vulnerabilities for the goal of multilingualism and the lives of many multilinguals. Echoingbut also widening Chun’s (2016) call for an ecological CALL in the post-2000s era, the overarching question that I have submitted to orient CALL–SLA research interfaces for the 21st century is: What technologies, teaching paradigms, views of language, and principal uses of computers can nurture multilingualism and digital literacies for all, not just for the privileged?

Aprendizaje de lenguas mediante dispositivos móviles: alcance, praxis y teoría

Conferencia plenaria, 25 de noviembre 2020,; XXI Congreso SEDLL Multimodalidad y nuevos entornos de aprendizaje en la enseñanza de las lenguas y las literaturas.

3 case studies

Pérez-Paredes, P., Ordoñana Guillamón, C., Van de Vyver, J., Meurice, A., Aguado Jiménez, P., Conole, G., & Sánchez Hernández, P. (2019). Mobile data-driven language learning: Affordances and learners’ perception. System, 84, 145–159.

Zhang, D., & Pérez-Paredes, P. (2019). Chinese postgraduate EFL learners’ self-directed use of mobile English learning resources. Computer Assisted Language Learning.  

Zhang, D. & Pérez-Paredes, P. (2020). Exploring Chinese EFL teachers’ perceptions of Augmented Reality (AR) in English language learning. In Miller, L. & Wu, G. (eds) Language Learning with Technology: theories, principles and practices. Springer.

Keynote abstract

Mobile assisted language learning (MALL) has become one the most popular keywords in computer assisted language learning (CALL) research over the last twenty years. While MALL enthusiasts have glossed its many affordances, the use of MALL in instructed classroom settings presents challenges of their own (Kukulska-Hulme & Shield, 2008; Conole & Pérez-Paredes, 2017; Pérez-Paredes, Ordoñana Guillamón, & Aguado Jiménez, 2018) that, I argue, have not been successfully defined in CALL research and classroom settings.

Traxler (2009) has noted that mobile learning is uniquely placed to support learning that is personalized, authentic, and situated. However, some relevant studies have thrown cold water on these expectations (Golonka, E. et al., 2014; Grgurović, Chapelle & Shelley, 2013). In this plenary, I will discuss different conceptualizations of MALL that emphasize areas of language learning that are anchored on different theories of language learning. I will use three case studies that have used different research methodologies, namely survey and mixed methods, across different contexts, countries and types of learning. I will discuss the self-directed uses of MALL (Zhang  & Pérez-Paredes, 2019), the design and use of apps to promote the acquisition of frequency-related declarative knowledge (Pérez-Paredes et al., 2019)  and the impact of new technologies such as Augmented Reality (AR) on language classrooms  (Zhang  & Pérez-Paredes, 2020). Ultimately, I will discuss a conceptual framework that situates MALL more critically in the context of existing and future practices of instructed (Foster, 2019; Kaminski, 2019) and self-directed (Trinder, 2017) language learning. Keywords: MALL, language learning, self-directed language learning, second language learning theory

References

Conole, G. & Pérez-Paredes, P. (2017). Adult language learning in informal settings and the role of mobile learning. Mobile and ubiquitous learning. An international handbook. New York: Springer, pp.45-58.

Foster, I. (2019) The future of language learning. Language, Culture and Curriculum, 32,3, 261-269,

Golonka, E. et al. (2014). Technologies for foreign language learning: a review of technology types and their effectiveness”. Computer Assisted Language Learning, 27.1, 70-105.

Grgurović, M. Chapelle, C.  & Shelley, M.  (2013). A meta-analysis of effectiveness studies on computer technology-supported language learning. ReCALL, 25, pp 165-198.

Kaminski, A. (2019). Young learners’ engagement with multimodal texts. ELT Journal, 73(2), 175–185.

Kukulska-Hulme, A. & Shield, L. (2008). An overview of mobile assisted language learning: From content delivery to supported collaboration and interaction. ReCALL, 20, pp 271-289.

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.

Pérez-Paredes, P., Ordoñana Guillamón, C., Van de Vyver, J., Meurice, A., Aguado Jiménez, P., Conole, G., & Sánchez Hernández, P. (2019). Mobile data-driven language learning: Affordances and learners’ perception. System, 84, 145–159.

Zhang, D., & Pérez-Paredes, P. (2019). Chinese postgraduate EFL learners’ self-directed use of mobile English learning resources. Computer Assisted Language Learning.  

Zhang, D. & Pérez-Paredes, P. (2020). Exploring Chinese EFL teachers’ perceptions of Augmented Reality (AR) in English language learning. In Miller, L. & Wu, G. (eds) Language Learning with Technology: theories, principles and practices. Springer.

TELL-OP products and reports available here.

Traxler, J. (2009). Current state of mobile learning. In Ally, M. (ed.) Mobile learning: Transforming the delivery of education and training. Athabasca University Press, 9-24.

Traxler, J. (2018). Learning with Mobiles in the Digital Age. Pedagogika, Special Monothematic Issue: Education Futures for the Digital Age: Theory and Practice

Traxler, J.; Timothy, R.; Kukulska-Hulme, A. & Barcena, E. (2019). Paradoxical paradigm proposals – Learning languages in mobile societies. Argentinian Journal of Applied Linguistics (AJAL), 7(2) pp. 89–109.

Trinder, R. (2017). Informal and deliberate learning with new technologies. ELT Journal, 71(4), 401–412.

Wegerif, R. (2007). Dialogic education and technology: Expanding the space of learning (Vol. 7). Springer Science & Business Media.

Corpus Data Scraping & Sentiment Analysis

My notes from the November 7th webinar led by Adriana Picoral

This is the github resource

R studio

I´m using R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
MacOS High Sierra 10.13.6

We´re using these packages:

install.packages("tidytext")
install.packages("tidyverse")
install.packages("rvest")