The revolution in the electronic communication may give rise to new modes of communication. Electronic discourse is a new variety of language that leads to significant variations in written structure of language. Electronic discourse creates a kind of semi-speech that is between speaking and writing and it has its own features and graphology. This study attempts to present a comprehensive picture of electronic discourse as a new variety of language, its salient features. In addition, it aims to conduct linguistic analysis of the features found in the electronic discourse. The corpus of this study was 340 messages with total 4760 words. The findings indicate that only 25% of overall the corpus found to be electronic discourse. This finding come inconsistent with common notion that the students' electronic discourse is incomprehensible, extremely shortened 'code'. In addition, findings revealed that students use variety of discourse features such as shortening, clippings and contractions, unconventional spellings, word-letter replacement.
The current study investigated the patterns of English language tutors’ e-feedback practices during the coronavirus pandemic, learners’ response to tutors’ e- feedback and the important issues that emerged from e-feedback practices. Quantitative and qualitative analysis of the online questionnaire and the semi-structured interviews were performed. The findings indicate that (1) English language tutors concentrate slightly more on local issues than global issues, provide eight main types of e-feedback; clarification requests, general explanations and clarification, questions and commands, repetition, explicit feedback, elicitation and recasts respectively. In addition, tutors employ more written and audio e-feedback compared with the screencast via various online platforms such as Blackboard collaborative tools, Zoom, Microsoft Teams, Email, and WhatsApp. (2) The findings indicate that the students ask for more clarifications, express their understanding of the e-feedback, engage in discussions, comprehend the e-feedback and make successful revisions, express their misunderstanding of the e-feedback and just a few students ignore the e-feedback they receive. (3) The qualitative analysis of the semi-structured interviews revealed some important issues such as the tutors' preferences to online learning, the appropriateness of online platform tools in providing e-feedback, ways and timing of providing e-feedback.
This study aimed to investigate the instructor’s electronic feedback practices during the COVID 19 Pandemic in terms of the nature of the content of e-feedback, the formulation, the challenges, and the multimodal nature of the instructor’s e-feedback. This study used a qualitative case study to obtain data from the instructor’s e-feedback in three linguistic courses as delivered, practiced by the single English language instructor. The instructor’s e-feedback via Blackboard and WhatsApp platform and the follow-up interview were analyzed qualitatively. The findings indicate that (1) the highest number of instructor’s e-feedback focused on global issues as compared to local issues, (2) the instructor composed his e-feedback in the form of eight main categories: explanations, suggestions, clarifications, questioning, repetitions, statements, praises, and commands, (3) the instructor used more screencasts for providing e-feedback, followed by written and audio modes respectively. The thematic analysis (4) revealed the instructor’s positive impression on providing e-feedback through these interactive modes (written, audio, and screencast) and a range of challenging issues such as students’ preference issues, technical issues, timing issues, financial and areal issues. This study is significant because it provides us with a comprehensive picture of the patterns of the feedback content, the formulation of the e-feedback, the multimodality of the instructor’s e-feedback, and the significant issues that emerged from the instructor’s e-feedback practices. However, further research should include a relative group of instructors to determine the impact of e-feedback on learners.
There are different innovations in computer-mediated corrective feedback (henceforth CMCF), which help in offering corrective feedback to the students and proved to have an effect on learners' linguistics outcomes. This study aims to present comprehensive representation of what has been investigated in the area of CMCF. In addition, it aims to analyze the effectiveness of recent CMCF research regard to adopting different research designs, different technologies, settings & types of feedback, different participants' characteristics and different language and skill taught. The corpus of analysis consist 23 articles were collected from six well-known journals in the field of CALL from 2008 to 2014. The findings indicate that CMCF proved its effectiveness regardless adopting the above-mentioned variables.
Islamic translation is considered as a special distinguished sub-discipline of applied linguistics. It is one of the most important areas of translation because it carries the values and eternal message. Through the history, the first translation work was of religious books. This study attempts to evaluate the adequacy and acceptability of four machine translation (MT) systems (World lingo, Babylon translation, Google translate, Bing translator) in translating the Islamic texts. In addition, it aims to evaluate the Islamic translation outputs based on functional characteristics (accuracy, suitability, and well-formedness) and sub-characteristics (syntax, terminology, reliability, and fidelity). The findings indicted that Google Translate System is the most adequate and acceptable among the other three systems (World lingo, Babylon translation, Bing translator) in translating the Islamic texts. The findings also revealed that Google Translate is acceptable in producing Islamic translation outputs in regard to the following functional characteristics (accuracy, suitability, and well-formedness) and sub-characteristics (syntax, terminology, reliability and fidelity) due to Google Translate advancement.
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