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Summary
It is clear that learning English and coding at the same time presented challenges for non-native English speakers when it came to reading educational content, communicating technically, reading and writing software, and other related tasks. They demanded additional images, multimedia, culturally-neutral code patterns, simplified English without culturally unique language, and training materials with built-in dictionaries (Aeiad & Meziane, 2019). Some people were inspired to learn English more effectively through programming, and it also clarified their logical reasoning toward natural languages.
The preponderance of the literature and widely used programming languages are written in English. A person must expend half of their brainpower to understand one English phrase and the other part to acquire the new terminology of computer languages, according to a current study on how communicating with ordinary basic comprehension impacts learning new information (Alaofi, 2020). It might be challenging for non-native English speakers to describe their programming skills in manuals.
The statements about the students native language condition were made as a result of the studys self-reported language competence restriction. Confidence levels varied significantly by gender, with male students expressing much greater confidence levels than female and non-binary pupils. Confidence levels were also strongly influenced by past experience, with more knowledgeable students reporting higher levels of satisfaction than more minor experience pupils. This supports earlier research that asserts academic self-efficacy is linked to prior educational excellence.
Plan
Future research in this area may focus on identifying the phrases that hinder non-native English speakers the most. Programming languages are closely related to English, despite the idealized idea of a computer language being purely mathematical rationality apart from messy human languages (Guzman & Gerald Soosai Raj, 2021). The implications on supportability, durability, and usefulness of different software implementation strategies between native and non-native English speakers might be experimentally measured in future research. To identify the aspects that non-native English speakers struggle with the most when studying a coding standard, one may assess the variations in mental demand experienced by native and non-native English speakers.
The development of a system of learning material that is compatible with the goal of occupational training and adjusts to the features of first-year learners as well as the instructional features of a course taught in English will be a crucial component of the plan. The future project will benefit from comprehensively utilizing a variety of cutting-edge teaching techniques and from developing an assessment system with different assessment modes that concentrate on learning students conceptual understanding and practical abilities. Students understanding and effective application of theoretical information, the development of practical abilities, and piqued enthusiasm in practices are all goals of teaching experiential content. It is necessary to carefully choose the experimental material due to the constrained course hours for experiments.
References
Alaofi, S. (2020). The impact of English language on non-native English speaking students performance in programming class. Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education. 585-586.
Aeiad, E., & Meziane, F. (2019). An adaptable and personalized E-learning system applied to computer science Programmes design. Education and Information Technologies, 24(2), 1485-1509.
Hagiwara, S., & Rodriguez, N. J. (2021). English learners (EL) and computer science (CS) learning: Equity issues. In Handbook of Research on Equity in Computer Science in P-16 Education. IGI Global. 70-87.
Guo, P. J. (2018). Non-native English speakers learning computer programming: Barriers, desires, and design opportunities. In Proceedings of the 2018 CHI conference on human factors in computing systems. 1-14.
Guzman, C. N., Xu, A., & Gerald Soosai Raj, A. (2021). Experiences of non-native English speakers learning computer science in a US university. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education. 633-639.
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