Emerging Digital Technologies: Building Competencies of STEM Pre-Service Teachers
Bibliographic record
Abstract
This study investigated the level of emerging digital technologies’ competencies of Science, Technology, Engineering, and Mathematics (STEM) pre-service teachers. It employed a descriptive survey research design. A sample of 357 STEM pre-service teachers from a Nigerian university were selected purposively, based on the criteria that they were willing to participate in the online test. The Science, Technology, Engineering and Mathematics Emerging Digital Technologies’ Competencies Test (STEM-EDTCT, r=0.84) was used to collect data online, through a Google form. The data collected were analyzed using descriptive mean, standard deviations, simple percentages, and inferential statistics (independent t-test and analysis of variance). The results showed that the level of emerging digital technologies’ competencies of STEM pre-service teachers was low, regardless of their mode of entry into the university. The study also found a significant gender difference in the level of digital competencies, with male pre-service teachers scoring higher than their female counterparts. Based on the findings, it is recommended that Nigeria’s policy on pre-service teacher-training should focus on acquiring skills and competencies, particularly in digital technologies. STEM pre-service teachers should be equipped with known and emerging technologies to enable them to deliver knowledge and information effectively.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".