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Identity or Competency? Exploring the Impact of Demographic and Professional Factors on English Faculty Competencies

Paper · 2024 · Bernadette Bagalay

Summary

The paper asks whether an English teacher's personal attributes (age, sex) or professional background (degree, specialization, experience, training) relate to how competent that teacher is judged to be. The study was set in university English programs at Isabela State University: the BSEd major in English and the BA in English. It used a descriptive design with 250 respondents, made up of department heads, English faculty and students. Faculty rated themselves, while heads and students rated the faculty. All used a pilot-tested two-part questionnaire: a profile section followed by a four-point competency checklist covering six dimensions. ANOVA tested multi-level profile variables, and a t-test compared the sexes. Age, educational attainment and specialization showed no significant effects. Female faculty scored higher in instruction, TESL/TEFL theory knowledge and assessment. Years of experience affected instruction only, and training volume over the past ten years affected theory knowledge. The author recommends targeted professional development, mentoring and regular competency evaluation. The reported degrees of freedom imply a faculty group of only about 36, so the findings should be read with caution.

Key points

  • The six competency dimensions are: instruction; knowledge of TESL/TEFL theories, approaches, methods and strategies; assessment; classroom management; guidance skills; and personality and professional qualities.
  • Ratings used a 1–4 scale. Mean scores were mapped to bands: 3.5–4.0 Outstanding, 2.5–3.4 Very Satisfactory, 1.5–2.4 Satisfactory, 1–1.4 Unsatisfactory.
  • Age showed no significant effect in any dimension. The ANOVA used F(3,32), and p-values ranged from .188 to .953.
  • Sex differences, from t-tests with t(34), favored female faculty in three dimensions:
    • instruction: 3.60 vs 3.33, p = .029
    • theory knowledge: 3.56 vs 3.18, p = .006
    • assessment: 3.61 vs 3.31, p = .024
  • Sex differences were not significant for classroom management, guidance or personality. Experience mattered only for instruction, and number of trainings mainly for TEFL theory knowledge.

Interesting ideas

  • I1. The study gathers ratings of the same people from three viewpoints: self, supervisor and student. This exposes rater bias instead of trusting a single judge.
  • I2. Effects were tested separately per competency dimension and appeared in only three of six, so a single overall score would have hidden them.
  • I3. Every comparison states its null hypothesis and an explicit accept/reject decision at p < 0.05, and non-results are reported as carefully as positive ones.
  • I4. Input attributes are treated as grouping factors, and the study tests which ones actually shift the outcome.
  • I5. Averaged numeric ratings are translated into a small set of named bands, which makes results readable for non-specialists.

For Mova

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

  • I1 (learn): keep each witness parser's verdict as its own score in silver data and report agreement per witness, rather than storing only the merged label.
  • I2 (answer): score readers separately by question type (who-did-what, counting, temporal order, inference) so a loss in one skill is not masked by the average.
  • I3 (learn): when a new repair rule or teacher tier is evaluated, run a per-metric significance test and log the nulls, separating real gains from noise.
  • I4 (tree): slice LAS by sentence length, genre or source treebank and test the group differences to find which conditions really hurt the parser.
  • I5 (answer): present the confidence of an explanation or answer as a few labeled bands instead of raw scores.

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 Bernadette Bagalay. 2024. Identity or Competency? Exploring the Impact of Demographic and Professional Factors on English Faculty Competencies. 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{bagalay2024identity,
  title = {{Identity or Competency? Exploring the Impact of Demographic and Professional Factors on English Faculty Competencies}},
  author = {Bernadette Bagalay},
  booktitle = {Proceedings of the 38th Pacific Asia Conference on Language, Information and Computation},
  year = {2024},
  url = {https://aclanthology.org/2024.paclic-1.104/}
}

References

18 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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