Exploring students’ intention to use LINE for academic purposes based on technology acceptance model
Bibliographic record
Abstract
The LINE application is often conceived as purely social space; however, the authors of this paper wanted to determine if it could be used for academic purposes. In this study, we examined how undergraduate students accepted LINE in terms of using it for classroom-related activities (e.g., submit homework, follow up course information queries, download materials) and explored the factors that might affect their intention to use it. Data were collected from 144 undergraduate students enrolled in an English course that utilized some activities based on LINE app using a questionnaire developed from TAM. Data were analyzed to see if relationships existed among factors when LINE was used to organize classroom experiences. The findings revealed that perceived usefulness and attitude toward usage had positive relationships with intention to use while perceived ease of use was positively related to perceived usefulness. In contrast with TAM assertions, this study did not find any relationship between perceived ease of use and attitude toward usage. Also, the number of social networking sites that students are using had no relationship with intention to use. The study also suggested some kinds of LINE-based learning activities preferred by students, which would be proposed for future courses. This study revealed several useful implications that TAM can be employed as a useful theoretical framework to predict and understand users’ intention to use new technologies in education.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".