An Efficient Merge of Online Teaching and Distance Learning Strategies in Chemical Engineering Computer Applications During the Covid Pandemic
Bibliographic record
Abstract
The goal of this research is to find evidence-based methods for converting hands-on computer programming lab instruction into a remote teaching technique that achieves targeted learning results without sacrificing soft skills. Both instructors and students were faced with a significant hurdle, which evidently requires a shift to distance learning and teaching a fifth-year chemical engineering computer applications course during the COVID-19 pandemic. We employed a mixed online technique to solve these problems in this undergraduate course at Elmergib University, which eased the transition from traditional face-to-face learning in the classroom to the setting of online programming training for chemical engineering applications instructions. The synchronous component of the education was performed using Google Meet video conferencing platforms. While the asynchronous part of the teaching was accomplished by broadcasting pre-recorded lecture videos into a learning management system, Google Classroom is an excellent choice (LMS), allowing students to go at their own pace when studying and progressing. Throughout this teaching process technique, instructors' assessments of students' learning and academic achievement served as an indicator of students' interest in self-monitoring skills. The study found that having a few hours of daily electricity outage combined with an inconsistent or poor internet connection had a favourable influence on students and teachers. Deep knowledge with widely available internet-based teaching resources, such as managing virtual classroom learning management systems and video-based lecturing tools through Google Meet, is a challenge for instructors
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 | 0.004 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".