Understanding the Patterns of the Usage of Mobile Telecommunication Services by Selected Undergraduate Students in Nigeria
Bibliographic record
Abstract
The New technology and information tend to have a lot of attractions on students, especially young adults which constitute the sample of this paper.However, mobile telecommunications offer various services that interest young people.These services are used to create and maintain social interactions while also relying on it for educational purposes.This paper is based on empirical research to examine the academic and social uses of mobile telecommunication services by first-year Information Technology (IT) students at Lagos State University, Nigeria (LASU).Students' use of mobile telecommunications services is analyzed using the Technology Acceptance Model (TAM) and the theory of planned behaviour.The study identifies challenges that could affect the use of mobile telecommunication services and it also pinpoints some factors that influence the acceptance and usage of mobile phones in education.The analysis enables the understanding of the significance of the variables and their influences on students' perceptions.The significance of these factors as well as limitation of the study was ascertained.This paper provides insight into the usage of mobile telecommunication services for different academic and social activities as well as presenting the impact of mobile telecommunication services on students' life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".