The Impact of Technology Integration in Learning on Increasing Student Engagement
Bibliographic record
Abstract
Background:The Impact of Technology Integration in Learning on Increasing Student Engagement refers to how this technology integration can be used well in learning. With the integration of technology in learning, it will have a direct impact on students, both student involvement in using technology, as well as the impact on student activities and creativity. Research purposes:This research was conducted with the aim of finding out how much impact technology integration has on increasing student engagement. Apart from that, it also aims as an explanation of how important technology is today in the learning process. Method:The method used in this research is a quantitative method.This method is a way of collecting numerical data that can be tested. Data was collected through distributing questionnaires addressed to students. Furthermore, the data that has been collected from the results of distributing the questionnaire will be accessible in Excel format which can then be processed using SPSS. Results:From the research results, it can be stated that the impact of technology integration in learning can indeed have an influence on increasing student engagement. Because basically, today's students are more interested in using technology. However, as a teacher you also need to supervise your students in learning when using this technology. This aims to ensure that students actually use technology to learn. Conclusion:From this research, it can be concluded that the impact of technology integration in learning has a very big influence on student engagement. With technology, students can be creative in how they learn, increase students' knowledge in using technology, make students more enthusiastic about learning, and make students less likely to get bored while studying.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".