The Relationship Between Digital Game Addiction, Communication Skills and Loneliness Perception Levels of University Students
Bibliographic record
Abstract
Today, with the developing technology, the use of computers, mobile phones and the internet has become indispensable tools of people’s lives. Technology has created new risks while facilitating the living conditions. Especially, there are various addiction concepts that negatively affect human life. Digital game addiction has been added to the concepts of addiction. Game addiction negatively affects the cognitive, psychological and social life of the individual. It is seen that such addiction rapidly spreading around the world are also widespread among children and young people in Turkey. The young population in Turkey is quite intense; it is necessary to investigate the problems related to digital games and find the optimal solution. In this research, it is aimed to investigate the digital game addiction, communication and loneliness perception levels of university students in terms of demographic variables. The sample of the study included 646 students studying at İnönü University in the 2018-2019 academic year. A survey that consists of personal information form, Digital Game Addiction Scale, Communication Skills Scale and UCLA Loneliness scale were used to collect data. The data obtained from the study were analyzed using independent t-test, one-way analysis of variance test (ANOVA) and Pearson’s Product-Moment Correlation Analysis. According to the results; digital game addictions of the participants vary according to gender, grade, parental educational degree, daily playing time and number of siblings. However, income level has no effect on digital gaming addiction. While gender, grade level, mother’s educational degree, duration of play and number of siblings have effects on communication skills; father’s education level and income level have no effect on it. There are significant relationships between students’ perception of loneliness and gender, mother and father educational degree and duration of playing time. One of the main finding obtained in the study; is a significant relationship between digital game addiction and communication skills while there is no statistically significant correlation between digital game addiction and loneliness.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".