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“Say What?” Influence of Perceived Self-Confidence in English of Senior High School Students on their Willingness to Communicate in English

Paper · 2024 · Mark Joseph B. Zapanta

Summary

The Philippines ranks high on the EF English Proficiency Index, but being proficient does not guarantee that people will actually use English. This study asks whether Senior High School students' self-perceived confidence in English is linked to their willingness to communicate (WTC) in English during class, and which factors students themselves think shape that willingness. Grade 11 and 12 students at Pateros Catholic School in Metro Manila reported their confidence and their WTC across classroom speaking situations. The relationship was tested with Spearman rho, and the analysis draws on MacIntyre et al.'s layered WTC model. Students rated themselves confident and said they were usually willing to speak, yet the two measures were only moderately and positively correlated. The author traces this gap mainly to fear of making mistakes and worry about classmates' reactions. Students also credited teacher strategy and skill, along with their own motivation for self-improvement and future careers. The findings suggest that classroom climate and teacher training can matter as much as raw proficiency.

Key points

  • Context: the Philippines held EF EPI ranks of 27 (2020), 18 (2021), 22 (2022) and 20 (2023), placing second in Asia after Singapore.
  • Participants came from the Grade 11 and 12 levels of Pateros Catholic School's Senior High School, and data were gathered in the school's ICT rooms.
  • The study used Spearman rho and found a moderate positive correlation between perceived confidence and WTC, even though both were rated high.
  • Reported inhibitors were nervousness after an error, fear of possible mistakes, and anxiety about peers' reactions. Reported enablers were motivation, teaching strategies and activities, and the teacher's communication skills.
  • Recommendations:
    • study WTC in written discourse;
    • use standardized competency tests instead of self-report;
    • train teachers in CLT, Problem-based learning and technology integration;
    • improve English communication skills of teachers across all subjects.

Interesting ideas

  • I1. Two self-rated scales can both score high while being only moderately linked, so a high average says little about whether one variable predicts the other.
  • I2. The author warns that self-assessment may overestimate or underestimate real skill and proposes checking it against standardized tests.
  • I3. Spearman rho suits ordinal, Likert-style data because it compares ranks rather than assuming interval-scale values.
  • I4. The WTC model separates stable, enduring influences from flexible, situation-specific ones.
  • I5. Besides measuring the variables, the study asks participants which factors they themselves consider decisive, which adds an explanatory layer to the correlation.

For Mova

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

  • I1 (learn): Mova's evaluation should measure how strongly parser confidence tracks actual correctness on gold trees, not just check that both look good on average.
  • I2 (learn): self-induced repair rules and silver data agreed by witness parsers should be audited regularly against a held-out gold treebank, so the system's self-judgment stays calibrated.
  • I3 (learn): rank correlation is a cheap way to compare orderings, such as witness-agreement scores against LAS per sentence, without trusting the raw magnitudes.
  • I4 (world): character states in a story could be split into lasting traits and momentary situational states, so explanations of why someone spoke or stayed silent can name which layer applies.
  • I5 (answer): when a student-reader fails a test item, Mova could record which pipeline stage it attributes the failure to, and then compare that self-attribution with the actual error source.

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 2The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 works

🧭 Topics 1Research subjects and languages the work is about.English · 2,379 works

Cite

Cite as Mark Joseph B. Zapanta. 2024. “Say What?” Influence of Perceived Self-Confidence in English of Senior High School Students on their Willingness to Communicate in English. Proceedings of the 38th Pacific Asia Conference on Language, Information and Computation.

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{zapanta2024say,
  title = {{“Say What?” Influence of Perceived Self-Confidence in English of Senior High School Students on their Willingness to Communicate in English}},
  author = {Mark Joseph B. Zapanta},
  booktitle = {Proceedings of the 38th Pacific Asia Conference on Language, Information and Computation},
  year = {2024},
  url = {https://aclanthology.org/2024.paclic-1.113/}
}

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