Developing Communicative Competence of Tertiary Level Engineering Students through De Bono’s Lateral Thinking Tool Design
Bibliographic record
Abstract
Lateral thinking is a unique form of thinking proposed by Edward De Bono. He believes that innovation is the ability to see the changes as an opportunity not as a threat. It results in the generation of new ideas and breaking out of the concept prisons of old ideas. This type of thinking decides the outcome of one's speaking and the job of lateral thinking is to enable the brain to find multiple possibilities or perceptions. The attitude of the present set of tertiary learners, lack of confidence and positive approach, their habits hinder their brain to intake, create and present the information in the demanding context. The major reasons for their inability are the cultural and psychological background of the learners. Developing lateral thinking skills of the tertiary level learners may pave the way to keep a constructive mind to acquire new language competencies. 'Think out of the box' is the crucial motto of the language teachers these days. De Bono's concept of Lateral thinking would enable the language teachers to break a new ground in inculcating speaking skills for the learners of English as a second language. The present study proposes Design , one of the lateral thinking techniques of Bono and its effectiveness as a tool that can be adapted by the language teachers to enrich the communicative competence of the tertiary level learners of English as a second language. The criteria for speaking activity are assessed based on stage III of Canadian Language Benchmark for assessing speaking skills.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".