Investigating pedagogical opportunities of educational technologies in developing countries: Physics Education Technology workshops for Bangladeshi science, technology, engineering and mathematics teachers
Bibliographic record
Abstract
Abstract Recently, an unprecedented number of people worldwide gained access to science, technology, engineering, and mathematics (STEM) education technologies. However, educators in developing countries encounter distinct challenges when attempting to incorporate these innovations into their practice. This mixed‐method study investigates the adoption of Physics Education Technology (PhET) computer simulations by secondary STEM teachers in Bangladesh. The study explored the challenges these educators face in learning to integrate PhET‐enhanced pedagogies and to assess the potential of this technology in developing nations. The primary researcher, a native Bangladeshi science educator, facilitated 3 h long workshops for 129 teachers, enabling the team to gather unique insights. Data collection encompassed online questionnaires, observations, and interviews. The analysis uncovered struggles faced by teachers which include limited pedagogical skills and subject knowledge, constrained lesson time, insufficient school support, restricted access to technology, and poor technological expertise. Despite these difficulties, educators acknowledged the potential of PhET‐enhanced pedagogies to improve student engagement. This study suggests that developing nations can leverage PhET's offline accessibility, wealth of teaching resources, and diverse language options within simulations to realize significant benefits. To address the identified challenges, we recommend translating PhET resources into native languages, developing instructional videos, employing flipped classroom methodologies, providing additional teacher training, and establishing professional learning communities. Moreover, the research underscores the potential of PhET to advance STEM education not only in Bangladesh but also in other developing countries with similar circumstances. Future studies could explore the impact of professional learning communities on facilitating the integration of technology to enhance STEM learning.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".