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Anxiety Reduction and RALL Implementation in English Grammar Acquisition among EFL Learners

Paper · 2022 · Minoo Alemi, Nayereh Bakhtyarifard and Atefeh Rezanejad

Summary

The paper asks whether bringing a humanoid social robot into English grammar lessons lowers the classroom anxiety of young Iranian EFL learners. It is one part of a larger project on Robot-Assisted Language Learning (RALL). Forty-five boys aged 11–15 at a private Tehran institute were split into three groups of 15. One group was taught with a NAO robot renamed "Nima". A second group played the same language games without the robot (Game-based Language Learning, GBLL). The control group had conventional teacher-led drills. The teacher, textbook and materials were the same across groups. Anxiety was measured once, in the final session, with Horwitz et al.'s FLCAS questionnaire and analysed with descriptive statistics and ANOVA. The RALL group reported the lowest anxiety. The clearest gaps were on items about fear of mistakes, being called on, and volunteering. The authors credit specific robot behaviours for these gaps. The study matters as rare evidence about how RALL affects learners' feelings rather than their skills. Its single-gender, single-site sample limits how far the results generalise.

Key points

  • Sample: N=45 male learners (mean age 13), of equal proficiency by the Oxford placement test, none of whom had met a robot before. The abstract calls the groups intact classes, while the method section says students were randomly divided.
  • Robot: NAO H21 (Aldebaran Robotics), programmed in its visual Choreograph environment and piloted in Sharif University's CEDRA lab before every session.
  • Games used in both RALL and GBLL: board game, hot potato, shopping game and verb ball toss, all taken from ESLgamesplus.
  • Item results, RALL vs. other groups:
    • Item 17 (worry about teacher's corrections): M=1.60 vs. 2.40.
    • Item 18 (nervous when called on): M=1.80 vs. 2.40.
    • Item 28 (self-consciousness when speaking): M=2.60 vs. 3.26.
    • Item 26 (worry about mistakes): M=1.87, the lowest of the three groups.
  • About a third of control-group students sometimes wanted to skip class, compared with roughly 20% in the GBLL group.

Interesting ideas

  • I1. The robot was scripted to make errors on purpose, so mistakes became ordinary and visible rather than shameful; planted errors are a cheap way to make error-spotting part of the task.
  • I2. A middle arm (games without the robot) separates the robot's effect from the effect of simply adding games, which keeps a novelty factor from being credited to the wrong cause.
  • I3. Instead of reporting only an overall score, the authors link particular design features to particular questionnaire items, such as calling students by name and the item about being called on.
  • I4. Each robot session was rehearsed in the lab beforehand to catch problems before facing real users.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Evaluation sets could include deliberately corrupted inputs or wrong candidate answers as negative controls, to check that a reader actually rejects them instead of accepting everything.
  • I2 (learn): When testing a new repair rule or teacher tier, add a matched arm that changes only the surrounding setup, so any gain can be attributed to the component itself.
  • I3 (answer): School-test failures could be broken down per question type and traced to the pipeline stage responsible, which makes the explanation as fine-grained as the architecture.
  • I4 (answer): A dry run of each new reader on a small held-out batch before full benchmark runs would catch pipeline breakages early and cheaply.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksInformation Systems · 1,730 worksPsychology · 1,649 worksSocial Psychology · 330 works

🧭 Topics 2Research subjects and languages the work is about.English · 2,379 worksGrammatical error correction & learner language · 162 works

Cite

Cite as Minoo Alemi, Nayereh Bakhtyarifard and Atefeh Rezanejad. 2022. Anxiety Reduction and RALL Implementation in English Grammar Acquisition among EFL Learners. Iranian Journal of Applied Language Studies 14.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.22111/ijals.2022.7222

@article{alemi2022anxiety,
  title = {{Anxiety Reduction and RALL Implementation in English Grammar Acquisition among EFL Learners}},
  author = {Minoo Alemi, Nayereh Bakhtyarifard and Atefeh Rezanejad},
  journal = {Iranian Journal of Applied Language Studies 14},
  year = {2022},
  doi = {10.22111/ijals.2022.7222},
  url = {https://doi.org/10.22111/ijals.2022.7222}
}

References

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