Engaging ESP Students with Brain-Based Learning for Improved Listening Skills, Vocabulary Retention and Motivation
Bibliographic record
Abstract
The concept of teaching and learning has changed drastically over the past few years by the virtue of both research results carried out in the fields of second/Foreign language learning and acquisition. Of all these researches, findings related to the brain structure and functions in cooperation with cognitive aspects of the education process, including the study of learning styles and intelligence have struck the language learning domain. A due understanding of learners’ learning styles, emotional preferences as well as their memory functions help teachers to build their teaching practices to optimize students’ learning. Brain Based Learning Approach (BBLA) is a natural, motivating, and a positive way that supports and maximizes learning and teaching. The current study implements Brain Based Learning Approach to improve listening skills of Business students, retention and establish positive attitudes with regards to their brain dominance and learning styles. To achieve these aims, listening skills test, vocabulary retention test, adapted form of Robert Gardner Motivation Scale, were developed and used. The sample of the study consists of thirty six Business majors. Findings show that Brain Based Learning Is an effective approach for developing listening skills, consolidate vocabulary recalling and retention. It also helps maximize motivation towards learning language 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".