STEAM and English For Specific Purposes: Online Courses For Brazilian Students In Technology
Bibliographic record
Abstract
Nowadays, we have an increase in informal online courses in Brazil with a variety of subjects, according to the student’s needs and interests. These informal courses could complement the knowledge learned in schools and universities, associating formal and informal learning, as defended by the Education 4.0 model. Additional languages, especially the English language, represent a great part of these courses as our society now understands the importance of English in a digital and technological world. English for Specific Purposes (ESP) is an area of English teaching-learning that takes into consideration the student’s needs in the curriculum design, focusing on a context where learners will use the language in real life. This area is also interdisciplinary because it connects linguistic structures with professional fields. For this reason, STEAM (Science, Technology, Engineering, Arts, and Mathematics) education could be an interesting approach in ESP courses by providing an integrative method. Thus, this paper aims to analyze three informal online English courses designed for Brazilian students/professionals in Technology, considering the ESP and STEAM approaches, and compare them with university learners’ needs. After the analysis, we understand in this paper that informal English courses, particularly ESP ones, should be designed by an interdisciplinary group of professionals, such as language teachers and specialists in the area, in order to show a meaningful learning experience. Besides, it is important to go beyond a list of vocabulary, integrating the four language skills and working with genres connected to the student’s own area of study.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".