The Impact of English as a Global Language on Filipino Language Practices
- Orathai Chureson Adventist International Institute of Advanced Studies
A country’s national language is a national treasure that should be preserved and passed on from generation to generation. In some multilingual contexts, as in the Philippines, the national language is evolving, being mixed with English (Francisco, 2012) possibly as a consequence of globalization. In an effort to create an awareness of this trend and foster the preservation of a country’s national language, this research explores the impact of the emergence of English as a global language on the Filipino language. It further examines the perceptions of Filipinos towards the language modification as impacted by English. It operates under a qualitative critical research paradigm that utilizes semi-structured interviews, spot observations, and observations of Filipino media, especially those manned by the educated populace who are proficient in both Filipino and English. Findings indicate language modifications resulting in Taglish, some loss of proficiency in Filipino, and a lack of purity in the Filipino and English languages.
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PhD Candidate, Education Adventist International Institute of Advanced Studies Cavite, Philippines
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Effects of Using Filipino Language during English Classes on Performance of Grade 11 HUMSS Strand Students in Bestlink College of the Philippines
- Sandra Villanueva
- Janella Joaquin Bawing
- Ariella Garcia
- Roberto Paez
- Abegail Meyor
- Mirasol Midel
Although the Philippines uses the Filipino language, English is used as the primary medium of instruction, and its presence ensures sufficient exposure to provide proficiency. However, recent studies show that some factors in English classes, such as untranslatable complex words, would lead to the misinterpretation of the lessons, thereby resulting in the poor performance of students. Hence, Filipino words are suggested as a translation tool to provide words that would fit the meaning of the English word, but its use may affect the teachers and especially students, which can result in additional misunderstandings. The design of the study is descriptive research. The survey is used to gather data from the selected 80 Grade 11 Humanities and Social Sciences Strand students by using questionnaires, which collect data from respondents through a set of questions. The team used a random sampling technique where each individual is randomly chosen. To interpret the data gathered, the researchers use a 5-point Likert scale. The information gathered from the questionnaires will serve as the primary data that will be used to answer the statement of the problem. The interpretation of the gathered data shows the following results of using Filipino during English classes. With a total average weighted mean of 3.88, which is interpreted as agreed, students agree that it would affect their academic performance on these subjects. It has positive effects on major exams, which was interpreted as agreed with a total average weighted mean of 3.97. However, with a total average weighted mean of 3.23, which is interpreted as undecided, most of the students are undecided on its effect on their grades. The data gathered also show a total average weighted mean of 4.38, which is interpreted as agreed. The students believe that it has positive effect on them as students. As for the teachers, with a total average weighted mean of 3.66, students agree that it would have a positive effect on the teachers as well. The researchers conclude that utilizing the Filipino language in English classes would be beneficial to students and teachers. The use of the Filipino language creates a link between teachers and students in communicating ideas and thoughts. Good communication improves understanding between participants. As such, the researchers suggest that teachers should consider using Filipino in their teaching method to help students in their studies and improve their grades.
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Research finds improving AI large language models helps better align with human brain activity
With generative artificial intelligence (GenAI) transforming the social interaction landscape in recent years, large language models (LLMs), which use deep-learning algorithms to train GenAI platforms to process language, have been put in the spotlight. A recent study by The Hong Kong Polytechnic University (PolyU) found that LLMs perform more like the human brain when being trained in more similar ways as humans process language, which has brought important insights to brain studies and the development of AI models.
Current large language models (LLMs) mostly rely on a single type of pretraining -- contextual word prediction. This simple learning strategy has achieved surprising success when combined with massive training data and model parameters, as shown by popular LLMs such as ChatGPT. Recent studies also suggest that word prediction in LLMs can serve as a plausible model for how humans process language. However, humans do not simply predict the next word but also integrate high-level information in natural language comprehension.
A research team led by Prof. LI Ping, Dean of the Faculty of Humanities and Sin Wai Kin Foundation Professor in Humanities and Technology at PolyU, has investigated the next sentence prediction (NSP) task, which simulates one central process of discourse-level comprehension in the human brain to evaluate if a pair of sentences is coherent, into model pretraining and examined the correlation between the model's data and brain activation. The study has been recently published in the academic journal Sciences Advances .
The research team trained two models, one with NSP enhancement and the other without, both also learned word prediction. Functional magnetic resonance imaging (fMRI) data were collected from people reading connected sentences or disconnected sentences. The research team examined how closely the patterns from each model matched up with the brain patterns from the fMRI brain data.
It was clear that training with NSP provided benefits. The model with NSP matched human brain activity in multiple areas much better than the model trained only on word prediction. Its mechanism also nicely maps onto established neural models of human discourse comprehension. The results gave new insights into how our brains process full discourse such as conversations. For example, parts of the right side of the brain, not just the left, helped understand longer discourse. The model trained with NSP could also better predict how fast someone read -- showing that simulating discourse comprehension through NSP helped AI understand humans better.
Recent LLMs, including ChatGPT, have relied on vastly increasing the training data and model size to achieve better performance. Prof. Li Ping said, "There are limitations in just relying on such scaling. Advances should also be aimed at making the models more efficient, relying on less rather than more data. Our findings suggest that diverse learning tasks such as NSP can improve LLMs to be more human-like and potentially closer to human intelligence."
He added, "More importantly, the findings show how neurocognitive researchers can leverage LLMs to study higher-level language mechanisms of our brain. They also promote interaction and collaboration between researchers in the fields of AI and neurocognition, which will lead to future studies on AI-informed brain studies as well as brain-inspired AI."
- Brain-Computer Interfaces
- Learning Disorders
- Language Acquisition
- Intelligence
- Computer Modeling
- Neural Interfaces
- Mathematical Modeling
- Mathematics
- Psycholinguistics
- Social cognition
- Great Ape language
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- Evolutionary psychology
- Asperger syndrome
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Materials provided by The Hong Kong Polytechnic University . Note: Content may be edited for style and length.
Journal Reference :
- Shaoyun Yu, Chanyuan Gu, Kexin Huang, Ping Li. Predicting the next sentence (not word) in large language models: What model-brain alignment tells us about discourse comprehension . Science Advances , 2024; 10 (21) DOI: 10.1126/sciadv.adn7744
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Reviews in Gastroenterology 2023
Electrogastrography Measurement Systems and Analysis Methods Used in Clinical Practice and Research: Comprehensive Review Provisionally Accepted
- 1 VSB-Technical University of Ostrava, Czechia
The final, formatted version of the article will be published soon.
Electrogastrography (EGG) is a non-invasive method with high diagnostic potential for the prevention of gastroenterological pathologies in clinical practice. In this paper, a review of the measurement systems, procedures, and methods of analysis used in electrogastrography is presented. A critical review of historical and current literature is conducted, focusing on electrode placement, measurement apparatus, measurement procedures, and time-frequency domain methods of filtration and analysis of the non-invasively measured electrical activity of the stomach.As a result a total of 129 relevant articles with primary aim on experimental diet were reviewed in this study. Scopus, PubMed and Web of Science databases were used to search for articles in English language, according to the specific query and using PRISMA method. The research topic of electrogastrography has been continuously growing in popularity since the first measurement by professor Alvarez 100 years ago and there are many researchers and companies interested in EGG nowadays. Measurement apparatus and procedures are still being developed in both commercial and research settings. There are plenty variable electrode layouts, ranging from minimal numbers of electrodes for ambulatory measurements to very high numbers of electrodes for spatial measurements. Most authors used in their research anatomically approximated layout with 2 active electrodes in bipolar connection and commercial electrogastrograph with sampling rate of 2 or 4 Hz. Test subjects were usually healthy adults and diet was controlled. However, evaluation methods are being developed at a slower pace and usually the signals are classified only based on dominant frequency. The main review contributions include the overview of spectrum of measurement systems and procedures for electrogastrography developed by many authors, but a firm medical standard has not yet been defined. Therefore, it is not possible to use this method in clinical practice for objective diagnosis.
Keywords: electrogastrography, non-invasive method, Measurement systems, Electrode placement, Measurement apparatus, Signal processing
Received: 19 Jan 2024; Accepted: 03 Jun 2024.
Copyright: © 2024 Oczka, Augustynek, Penhaker and Kubicek. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Jan Kubicek, VSB-Technical University of Ostrava, Ostrava, 708 33, Moravian-Silesian Region, Czechia
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