Students’ Attitudes Towards Learning, A Study on Their Academic Achievement and Internet Addiction
Bibliographic record
Abstract
Examining attitudes of students towards learning, their academic achievements and internet addictions is the mainfocus of this study. With the institution permission obtained from the Provincial Directorate of National Education ofİstanbul Governorship dated: 21.04.2016 and No: 59090411-20-E.4519158, a descriptive study in relationalscreening model was conducted. By evaluating the data of 355 students (158 male and 176 female), from 370students studying in the 9th, 10th, and 11th grades attending public high schools in the region of Kadıköy, İstanbulduring 2015-2016 academic year the outcome results were obtained. "Personal Information Form", "AttitudesTowards Learning" and “Computer Addiction for Adolescents” scales were used in order to collect research data. Byanalyzing the data with t test, one-way variance analysis (ANOVA) and correlation statistical research techniques inthe SPSS 22.0 program, findings were obtained. A significance level of 0.05 was taken as the basis in the appliedstatistics. As per the findings, a difference was found in terms of internet addiction according to gender, academicachievement, homework habits, family activity frequency variables. Furthermore, a negative relationship was foundaccording to the correlation analysis result between students' attitudes towards learning and internet addiction. Inconclusion, in the light of the the research findings, it is possible to express that differentiation of students' attitudestowards learning can support effective and efficient use of information technologies. However, the negativedifferentiation of the attitudes towards learning can generate Internet addiction as a result of inefficient use of theinformation technologies.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".