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Posted: May 2nd, 2019
Lin, M. (2016). Effects of corpus-aided language learning in the EFL grammar classroom: A case study of students’ learning attitudes and teachers’ perceptions in Taiwan. TESOL Quarterly, 50(4), 871-893.
Introduction
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Numerous studies attempt maximising learners’ English proficiency with the aim of identifying the best practices to increase acquisition and development in second-language learning. One approach that has attracted significant attention in recent years is the corpus-based approach. A corpus is defined by Reppen (2010) as “a large and principled collection of naturally occurring texts (written or spoken) stored electronically” (p. 2). This approach has impacted second-language teaching and acquisition significantly. Strong research suggests that when learning an L2, students have a greater colloquial understanding of a language, or can express their exact meaning more clearly, if they do not learn about grammar rules in isolation, but within the context in which those rules are used. Thus, data-driven learning DDL facilitates learning and teaching either grammar, collocation, vocabulary, or writing and develops autonomy, motivation, and confidence.
Summary
Lin’s study was conducted in Taiwan by applying a blended approach, namely a mix between Data-Driven Learning (DDL) and traditional deductive approach (TDA). The aim was to examine how Taiwanese students and teachers in an ELF classroom learn and teach passives and relative clauses. The research question asked whether DDL, compared with TDA, was considered to be more pedagogically suitable and more effective in promoting learning motivation and self-efficacy.
Lin used a mixed method to collect data. Quantitative method was used to collect data from 52 first-year undergraduate students (aged 18 to 19 years old) who were majoring in the English language. He used three classes; one class applied 100% TDA, the second class applied 40% DDL and 60% TDA, the last class applied 60% DDL and 40% TDA. Qualitative method was used with 14 early-career teachers (ECTs); the ECTs were all postgraduate students, and at the same time, they were teachers of English to undergraduates in Lin’s experiment.
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After using a paired-sample t-test, the quantitative results showed that only one out of three classes, which received 60% DDL instruction, had shown improvement in students’ learning attitudes while the qualitative results from the teachers showed that the applying DDL was interesting and innovative experience and could inspire the students to be more independent about learning grammar. Lin recommends applying DDL more widely in teaching and learning grammar.
Structure
The article has clear subsections that effectively organise the content. The analysis is clear, and Lin compares his study with references to other researchers in this field by demonstrating the findings of other empirical studies. He supports his analysis and findings via tables and figures that clarify his points.
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Structure and Methodology
The article has clear subsections that effectively organise the content. The analysis is clear; and he supports his analysis and findings via tables and figures that clarify his points.
The methodology and procedure of Lin’s experiment, including the number and level of participants, are relatively similar to other studies that I will compare with. All the studies typically have one or two treatment/focus group(s) and one control group. All supporting studies in this paper have compared traditional inductive/deductive teaching approaches with DDL approach. However, Lin’s study is unique in terms of the duration of both the course for the student participants and the training session for teacher participants on how to apply DDL in the classroom (see Table 1). The longer durations affect the validity of Lin’s study negatively as explained in the validity section below.
Table 1: Differences in the duration of the DDL courses between Lin and other researchers.
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Study |
Course Duration |
Lin (2016) |
Students: 3 weeks (9 hours in total) Is AI Involved in Writing?Nope—all human, all the time. Our writers are pros with real degrees, crafting unique papers with expertise AI can’t replicate, checked for originality. Teachers: 2 weeks (6 hours) |
Akinci and Yildiz (2017) |
Students: 5 weeks (15 hours) |
Barabadi and Khajavi (2017) Why Are You the Best for Research?Our writers are degree-holding pros who tackle any topic with skill. We ensure quality with top tools and offer revisions—perfect papers, even under pressure. |
Students: 7 weeks |
Huang (2012) |
Students: 12 weeks |
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Students: 20 weeks (30 hours) |
Farr (2008) |
Teachers: 24 weeks (84 hours) |
Mukherjee (2004) Will My Paper Be Unique?Guaranteed—100%! We write every piece from scratch—no AI, no copying—just fresh, well-researched work with proper citations, crafted by real experts. You can grab a plagiarism report to see it’s 95%+ original, giving you total peace of mind it’s one-of-a-kind and ready to impress. |
Teachers: 1 year |
Data collection
Lin used mixed method research (MMR) to collect data. According to Dorney (2007), MMR helps develop a full understanding of a complex issue by “looking at it from different angles” (p. 164). Lin’s first method was the quantitative method of 16-question questionnaire through using a 5-point Likert scale. Undergraduates completed entry and exit questionnaires. The second method was an open-ended (semi-structured) interview. This qualitative method was directed at the teachers, the postgraduate.
Table 2: Comparison of Lin’s and other researchers’ studies.
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Researcher(s) |
Pre-/post- test Quantitative |
Interview (semi-structure) Can I Change My Order Details?For sure—you’re not locked in! Chat with your writer anytime through our handy system to update instructions, tweak the focus, or toss in new specifics, and they’ll adjust on the fly, even if they’re mid-draft. It’s all about keeping your paper exactly how you want it, hassle-free. Qualitative |
Entry/exit Questionnaire Likert Scales Quantitative How Do I Order a Paper?It’s a breeze—submit your order online with a few clicks, then track progress with drafts as your writer brings it to life. Once it’s ready, download it from your account, review it, and release payment only when you’re totally satisfied—easy, affordable help whenever you need it. Plus, you can reach out to support 24/7 if you’ve got questions along the way! |
Lin (2016) |
MA-Teachers |
UG-Students |
|
Akinci and Yildiz (2017) How Quick Can You Write?Need it fast? We can whip up a top-quality paper in 24 hours—fully researched and polished, no corners cut. Just pick your deadline when you order, and we’ll hustle to make it happen, even for those nail-biting, last-minute turnarounds you didn’t see coming. |
UG-Students |
UG-Students |
UG-Students |
Barabadi and Khajavi (2017) |
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||
Huang (2012) |
UG-Students |
UG-Students |
|
Oghigian and Chujo (2010) |
UG-Students How Do You Match Professor Expectations?We follow your rubric to a T—structure, evidence, tone. Editors refine it, ensuring it’s polished and ready to impress your prof. |
||
Farr (2008) |
MA-Teachers |
||
*Mukherjee (2004) |
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*Mukherjee used multiple answers and multiple choice in the questionnaire.
Other researchers have either employed different data collection methods or applied the same methods to their participants in different ways comparing with with Lin (see Table 2). The above table shows the following:
Similar to Lin, as shown in Table 2, Akinci and Yildiz (2017) and Huang (2012) conducted an exit questionnaire on their students using the quantitative method. In contrast, Mukherjee asked the teachers to complete entry and exit questionnaires with different scales (with multiple-answer and multiple-choice items). Using the qualitative method, Akinci and Yildiz used a semi-structured interview (open-ended questions). However, while Akinci and Yildiz interviewed the students, Lin interviewed the teachers.
Data analysis
Lin analysed his data applying a paired-sample t-test to the questionnaire data. First, he used multivariate analysis of variance (MANOVA) for the entry questionnaire. Then he used multivariate analysis of covariance (MANCOVA) for the exit questionnaire, including the scores of the entry questionnaire as covariance.
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In contrast, Akinci and Yildiz and Barabadi and Khajavi (2017) used an ANOVA one-way independent paired-sample t-test for each pre/post-test to have separate results. Huang used (ANOVA) two-way t-test. She concentrated on the independent variable within one group (pre-test and post-test) and between groups (focused and controlled group). Farr and Huang used percentages to present the results of their questionnaire; Oghigian and Chujo (2010) applied a percentage to their pre-test and post-test to present their results.
Findings
The findings of the other researchers aligned with Lin’s findings. DDL as a learning tool had a positive effect compared with traditional teaching approaches. The DDL treatment groups outperformed controlled groups taught traditionally with textbooks and dictionaries.
Validity
Lin conducted a pilot study with 68 participants after revising the questionnaire for validity and reliability. Cohen et al. (2017, p. 496) stressed the importance of applying a pilot study “to increase the reliability, validity, and practicality of the questionnaire”. However, some validity issues remain in Lin’s study compared with the other studies.
Conclusion
Lin identified some of the limitations of his study. Although he notes that his sample was small, the samples in the other studies are only slightly bigger or are even smaller than Lin’s. More notably, he notes that his questionnaire failed to ask students their learning preferences (either DDL, TDA, or both), and his interviews failed to ask teachers if they would use DDL to teach complex or simple grammar rules. The study also produced no evidence as to whether DDL helps to increase grammar acquisition. Lin advises that future research use a pedagogical corpus, which is easier to use (concerning finding suitable concordance lines for students) and more appropriate for teaching and learning purposes than regular corpora.
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Number of the words: 1570
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