Learning business English in virtual worlds : effectiveness and acceptance in a Malaysian context : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management Information Systems at Massey University, Palmerston North
Bibliographic record
Abstract
Motivated by the need to provide better opportunities for Malays in Malaysia to improve their oral business English communication skills, the research focused on the use of multiuser virtual environment (MUVE) for learning English and pursued the following research questions: (1) Is MUVE based learning effective in facilitating situated scenario-based learning of oral business English communication skills by Malay learners? (2) Which factors influence the acceptance of MUVE based learning of oral business English communication skills by Malay learners? To address the first research question, a controlled experiment was conducted to compare the learning gains in traditional classroom and in MUVE environment. To address the second research question, an acceptance model based on the social cognitive theory and the technology acceptance model was tested by fitting it to the data obtained by using a questionnaire. The experiment involved 152 Malay tertiary learners, who also filled in the questionnaire. Findings indicated that MUVE was effective in facilitating scenario-based learning of business English by Malay learners. Learners‘ oral skills showed statistically significant improvement following learning in MUVE. However, the difference between the improvement in the classroom environment and in MUVE was not statistically significant. As for MUVE acceptance, Video Games Affect, English Class Anxiety, and Perceived Usefulness affected the learners' Intention to Use MUVE for e-Learning, although the effect size for Video Games Affect and English Class Anxiety was small. The results of the study suggest that MUVE based learning is an effective environment for learning oral business English communication skills. MUVE is particularly suitable for distance learning, when traditional classroom learning is not available. The study confirmed the claims in the literature that MUVE is particularly suitable for anxious learners and for learners who like to play video games. The study involved Malay university students as participants, and the results are not necessarily generalizable to other types of learners.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".