The Future Classroom: Analyzing the Integration and Impact of Digital Technologies in Science Education
Bibliographic record
Abstract
The present study is a systematic and bibliometric literature review aimed at evaluating the incorporation of digital technologies in science education and their subsequent impacts. The review exclusively utilizes the Scopus database and covers literature from January 2019 to December 2023. The primary focus is on empirical studies that investigate the use of digital technologies in science education and their effects on educational outcomes such as student engagement, motivation, and academic performance. Notably, the key findings reveal a significant increase in the number of publications during this period, indicating a growing interest in the role of digital technologies in enhancing science education. The review corroborates the transformation of science education through digital technologies such as augmented reality (AR), virtual reality (VR), and blended learning environments, which have made learning more interactive, personalized, and accessible. However, challenges such as the digital divide, resistance from educators, and the need for continuous professional development persist. These challenges emphasize the importance of strategies to enhance digital literacy among educators and promote equitable access to technology. The review recommends the development of comprehensive training programs for educators, ensuring that all students have access to the necessary digital tools, and maintaining robust data protection measures. By addressing these issues, the integration of digital technologies in science education can be optimized, resulting in enhanced educational outcomes and better preparation of students for a technology-driven world.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".