Technology‐Enhanced SLA Research

Smith, Bryan

doi:10.1002/9781118914069.ch29

Cited by 8 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Digital platforms provide multiple data points that can be mined to understand learner characteristics at both the individual and group levels. Tracking user behavior helps language educators conduct in-depth analyses of how learners engage with web-based language learning programs or interact with other learners in collaborative online contexts (Smith, 2017). Collecting and analyzing such behavioral data allows for documenting the multiple routes and particularities of language learning processes and provides guidance on how to best design web environments for enhanced participation and optimal language learning (Chun, 2013).…”

Section: Introductionmentioning

confidence: 99%

Recent Contributions of Data Mining to Language Learning Research

Warschauer

Yim

Lee

et al. 2019

Ann Rev Appl Linguist

View full text Add to dashboard Cite

This paper will review the role of data mining in research on second language learning. Following a general introduction to the topic, three areas of data mining research will be summarized—clustering techniques, text-mining, and social network analysis—with examples from both the broader field and studies conducted by the authors. The application of data mining in second language learning research is relatively new, and more theoretical and empirical support is needed in the appropriate collection, use, and interpretation of data for specific research and pedagogical objectives. The three examples that we introduce illustrate how new data sources accessible in online environments can be analyzed to better understand the optimal instructional context for corpus-based vocabulary learning (clustering technique), characteristics and patterns of collaborative written interaction using Google Docs (text mining and visualizations), and issues of access and community in computer-mediated discussion (social network analysis). Implications of these new techniques for L2 research will be discussed.

show abstract

Section: Introductionmentioning

confidence: 99%

Recent Contributions of Data Mining to Language Learning Research

Warschauer

Yim

Lee

et al. 2019

Ann Rev Appl Linguist

View full text Add to dashboard Cite

show abstract

Toward Langua‐technocultural Competence

Sauro¹,

Chapelle²

2017

The Handbook of Technology and Second Language Teaching and Learning

View full text Add to dashboard Cite

Past the Anthropocentric: Sociocognitive Perspectives for Tech-Mediated Language Learning

Finol

2019

Ann Rev Appl Linguist

View full text Add to dashboard Cite

Researchers in second language acquisition (SLA) have long debated the nature of human cognition and how it affects second language learning (L2L). On the one hand, and largely dominating the field of SLA, is the cognitive approach, which focuses on the brain as the unit for cognitive analysis. On the other hand, sociocultural theory holds that human cognition is mediated by cultural artifacts. These perspectives present significantly different views on interaction, understanding it as either auxiliary or instrumental in the learning process, or “fundamentally constitutive” of learning (Mondada & Pekarek Doehler, 2004, p. 502). Increasingly concerned with the role of technology in language learning, current computer-assisted language learning (CALL) research demands theoretical frameworks that account for the ecological relationship between learners, the tools they use, and the environments they inhabit. This article raises the issue of how anthropocentric perspectives on technology have permeated the field of CALL until now, inhibiting its development. I suggest the need for CALL to adopt a less instrumental, more determinist (Carr, 2010) view of the role that technology plays in language learning, by engaging in sociocognitive research (Atkinson, 2010) that embraces the transformative power that technology has over cognition.

show abstract

Technology‐Enhanced SLA Research

Cited by 8 publications

References 37 publications

Recent Contributions of Data Mining to Language Learning Research

Recent Contributions of Data Mining to Language Learning Research

Toward Langua‐technocultural Competence

Past the Anthropocentric: Sociocognitive Perspectives for Tech-Mediated Language Learning

Contact Info

Product

Resources

About