The influence of second language learning motivation on students' understandability of textbooks
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the relationship between the understandability of an accounting textbooks written in English and the language learning motivation of international students. Previous research assumed that native speakers of a language and second-language speakers would understand a given accounting text similarly and little attempt has been made to ascertain any individual differences in users’ capacity to read and understand a foreign language. Design/methodology/approach The 107 participants in this study comprised of full-time English as a Second Language postgraduate commerce students studying at a major Australian university. The authors used two-part questionnaire to examine the motivation of participants and the understandability of an accounting textbook using the Cloze test. Findings The results suggest that most international students have difficulty in understanding the textbook narratives used in this study. Furthermore, the results show that students’ motivation to learn a foreign language impacts on the understandability of an accounting textbook. Practical implications This study will help the educators, textbook publishers and students to understand the needs of ESL students. It is expected to provide guidance for authors and instructors to enhance the effectiveness of the accounting courses. Originality/value The accounting literature shows that there have been efforts by accounting researchers to measure the understandability of accounting texts or narratives. This research provided valuable insights of the learning challenges of international students and valuable recommendations to educators and publishers to enhance the delivery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".