Uso de podcasts para la mejora de la producción oral de alumnos de secundaria en asignaturas STEM/AICLE
Bibliographic record
Abstract
In today’s world technological and language skills appear to be essential in order to success in the labour market. Foreign language learning has been around since ancient times and pedagogical methodologies have been many and varied. In modern history, Canadian immersion programmes were the starting point of Content and Language Integrated Learning (CLIL) methodologies in Europe in the mid-1990s. The EU has encouraged the adoption of programmes and methodologies that foster proficiency in, at least, two additional EU languages and, thus, CLIL have become a very popular methodology in the EU. CLIL has another noteworthy characteristic, that is, it emphasises the learning of content through the use of a FL. STEM professions, that is, technological, scientific and engineering careers require high skilled professionals and the final products are of high added value. Unfortunately, the number of students in STEM classes or degrees has decreased in the last few decades. STEM subjects are regarded as uninterested and difficult due to the oftentimes abstract theory and their quality has been found somewhat lacking. In order to attract a higher number of students, steps to make STEM subjects more attractive have been taken, such as real-life application of their content. Podcasts are downloadable audio recordings that have been mainly used in higher education as supplemental materials. They have also been used as a tool to teach FL, paying special attention to oral productive and comprehensive skills. There is a dearth of studies on the use of podcasts as a STEM, CLIL learning tool, particularly student-generated ones. In the few studies available, students have expressed their higher motivation, satisfaction and engagement when learning content. Therefore, a combination of all three, that is, STEM, CLIL student-generated podcasts should provide a positive influence on students’ content- and language-knowledge acquisition. Resumen: Para poder triunfar en el mercado laboral actual, los conocimientos tecnológicos y lingüísticos resultan imprescindibles. El aprendizaje de idiomas es un fenómeno conocido desde la antigüedad y las metodologías empleadas han sido variadas. Más recientemente, los programas canadienses de immersión fueron el origen de la metodología AICLE (Aprendizaje Integrado de Contenidos y Lenguas Extranjeras) en Europa a mediados de los 90. La UE ha abanderado la adopción de programas y metodologías que promuevan dominar, al menos, dos idiomas de la UE, además de la lengua materna, por lo que AICLE es muy popular. Además, la AICLE permite el aprendizaje de contenido a través de un idioma extranjero. Las profesiones tecnológicas, científicas e ingenieriles (STEM) requieren profesionales altamente cualificados y producen productos de alto valor añadido. Desafortunadamente, el número de alumnos en estas carreras se ha reducido en las últimas décadas pues consideran estas asignaturas poco interesantes y complicadas ya que, con frecuencia, son muy abstractas y la calidad de la enseñanza deficiente. Para atraer un mayor número de alumnos, se han tomado los pasos necesarios para hacerlas más atractivas como, por ejemplo, con la aplicación de lo aprendido a la vida real. Los podcasts son archivos de audios descargables que se usan sobre todo en la educación universitaria como material de apoyo, aunque también se usan en la enseñanza de idiomas para mejorar la producción y comprensión oral. Existen pocos estudios en los que se utilicen podcasts, en especial creados por alumnos, como recurso en asignaturas STEM/AICLE. Sin embargo, en ellos los estudiantes han expresado una mayor motivación, satisfacción y participación cuando se usaban para aprender contenido. Por ello, el uso de podcasts creados por alumnos en asignaturas STEM/AICLE podría ejercer una influencia positiva en la adquisición de conocimientos de los contenidos y de una lengua extranjera
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".