Impact of Using Technology on Teacher-Student Communication/Interaction: Improve Students Learning
Bibliographic record
Abstract
This paper aims to investigate the teachers’ and students' views on using technology and its effect oncommunication/interaction. As noteworthy results have been succeeded in educational technology in recent years,evaluating the effects of technology integration on communication is now possible. Moreover, the impact oftechnology on the teachers' and students' communication is considered important. The present study examines thefactors or the impact of using technology between teacher-student communication/interaction in Turkey. This paperproposes both innovation diffusion theory (IDT) and integrating technology acceptance model (TAM) fromeducational communication perspectives. The case study method was used in the research. Case study is one of thequalitative approaches and requires in-depth analysis of a case resulting in a narrative description of behaviour orexperience of a person or a group. The sample of this study consists of 95 participants (77 students and 18 teachers)from a secondary school in Bursa/Turkey. Semi-Structured interviews were carried out with the participants includingissues; "using education technology on teacher and student technologies, the effect of using education technology andto improve teacher-student communication should be used asolution proposals for technologies in classrooms".Maxqda 11 program was used for the analysis of the interviews. As a result of the interviews, participants have beensuggested to use tablets in classrooms, sound insulation, use of never ending pen and no ringtones. The findings showthat the choice of educational technology is related to teachers’ perception which is communication/interaction withthe student can be enhanced using technology. However, the opposite is true for students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".