Factors Affecting Pre-service TESOL Teachers’ Attitudes towards Using CD-ROM Dictionary
Bibliographic record
Abstract
Rapid technological advances in communication technologies and computational power are altering the nature ofknowledge, skills, talents and the know-how of individuals. A CD-ROM dictionary is an interesting and effectiveteaching tool, which captures pre-service teachers’ interest and does much more than just translates especially withthe availability of a variety of resources to support language learning and teaching. This research reports on thestudy undertaken to determine whether some factors (gender and year of study) affect pre-service TESOL teachers’attitudes towards using CD-ROM dictionaries. The theoretical framework for the study is drawn from Diffusion ofinnovations’ theory by Rogers (1995) and TAM model by Davis (1986; 1989). A modified questionnaire isemployed to obtain the necessary information on pre-service TESOL teachers’ backgrounds, their attitudes towardsusing CD-ROM dictionaries, and certain factors. A total of (80) pre-service TESOL teacher at a Malaysian highereducational institution was purposively selected for the quantitative data. The findings exhibit that the respondents’gender does not affect their attitudes towards using CD-ROM dictionaries. However, a significant difference isrevealed, through employing one-way analysis of group variance (ANOVA), in the mean scores of pre-serviceTESOL teachers’ attitudes among the first, second and third year. Finally, it is concluded that the CD-ROMdictionary is a way of encoding all relevant information associated with lexical entries in a manner easily accessibleto users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".