Global Research Trends on Learning Technology in Psychology: A Bibliometrics Analysis
Bibliographic record
Abstract
The urgency of this research stems from the growing use of learning technology in psychology, particularly since the COVID-19 pandemic accelerated the adoption of remote and technology-based learning. Although numerous studies have been conducted, the understanding of global trends in this topic remains limited. This study aims to analyze global trends related to learning technology in psychology using bibliometric methods. The methodology employed in this research is bibliometric analysis, using VOSviewer software to map prominent topics frequently discussed in academic literature from 2004 to 2024. The findings reveal that deep learning, self-efficacy, social relationships, and the impact of COVID-19 are dominant themes within learning technology in psychology. Additionally, a significant increase in publications and citations on this topic has been observed over the past two decades. The study also identifies six main clusters that illustrate interconnections among themes. In conclusion, learning technology is increasingly vital in educational psychology, particularly in student motivation, engagement, and performance. This research comprehensively maps global trends in learning technology within psychology. It identifies opportunities for further study, particularly regarding the impact of technology in the post-pandemic era.
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 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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.151 | 0.227 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".