The Negative Impact of Technology on Social Networking among Students at UTM Skudai 2016
Bibliographic record
Abstract
Recently social media network such as Facebook, Instagram, Twitters and Youtube has led to the popularity. Social media, which once act as an electronic connection between users has gained wider acceptability and usability and is also becoming probably the most important communication tools and is addictive among students especially in the higher education. There’s no denying the benefits we have gained from technological advancements, but as with all things in life moderation is key. Many students tend to use social medias against the ethics enshrines by Islamic laws therefore awareness about the dangers of excessive use of electronics will help in avoiding any undesirable issue. The negative impacts of addictive usage of social medias includes isolation, lack of social skills and bonds, obesity, depression, poor sleep habit, increase bullying, lack of privacy, lack of social and sexual boundaries, and mental and emotional disturbances. Therefore, this study examines the significant impact of social media on UTM students and to identify recommendations to overcome the negative impact. Quantitative method is applied in this research distributed to students in Universiti Teknologi Malaysia where the respondents is required to rate on a likert scale basis. Questionnaire is developed to explore the participating students’ the level of social media’s usage and its negative impact. At the end of this paper, some suggestions are included to overcome the negative impact for a better use to this social networking site. The last and not least, this study will be of great benefits to the university as it has shown the dangers of uncontrolled use of these social medias by students and therefore the need to put in place measures to prevent the negative effects.Keywords : Technology, Social Medias, Impact of Social Medias, Students, UTM
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".