Digital Technologies in Engineering Education: A Scoping Review of Integrated Dynamic Teaching
Bibliographic record
Abstract
The rapid adoption of digital educational technologies has transformed engineering education, introducing new opportunities and challenges in course delivery. While these tools support dynamic and adaptive learning, their broader impact on student success across academic, technical, well-being, and community-building dimensions remains underexplored. This study conducts a scoping review to examine how digital technologies align with Integrated Dynamic Teaching (IDT) principles to address these four pillars. A systematic search of ERIC, Scopus, and Web of Science identified 371 articles, of which 31 empirical studies met the inclusion criteria. Results indicate that gamification platforms, virtual reality environments, and simulations are widely used to enhance academic engagement and conceptual understanding. However, digital tools explicitly designed to support student well-being and foster a sense of community remain underutilized. The review highlights key recommendations, including leveraging digital technologies to enhance mentorship opportunities, facilitate collaborative learning, and implement flexible course structures that accommodate diverse learning needs. These findings underscore the need for a more balanced integration of digital tools that not only improve academic performance but also promote student well-being and community engagement in engineering education.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".