Exploring the Effects of Microsoft Teams Messaging App on Post Foundation Students' Writing Skills: A Socio-Constructivist Analysis
Bibliographic record
Abstract
This paper analyses the effects of interaction via Microsoft Teams private chat on Post Foundation students' writing skills. The current study was conducted at the University of Technology and Applied Sciences, Al Musannah, Oman, during the Spring of the Academic Year 2022-23. Of the sixty students registered for the Technical Writing course, twenty were selected as the purposive sample for this study as they were very active in using the Microsoft Teams private chat messaging app to interact with their teachers and peers. A qualitative approach was adopted to conduct a thematic analysis of the writing samples; hence, the participants were asked to send emails on different topics related to their course and instructed to use MS Teams private chat for interaction with their teacher and peers outside class hours. The thematic analysis of the emails was carried out in terms of formality, grammar, tone, word choice and context using a software package called QDA Miner Lite. The researchers found that students used abbreviations, shortened words, acronyms, slang, and emoticons for interaction through MS Teams private chat. However, they were very cautious in using formal and standard language in writing emails. Informal language and colloquial expressions were not found in their formal emails and academic writing, indicating that they were aware of the context and use of appropriate language. The researchers conclude that extensive social interaction on MS Teams private chat can significantly contribute to learners' communication skills without negatively impacting their academic English. Therefore, this study recommends the judicious integration of social media apps into English language courses to enhance ESL learners' communication skills.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".