Teaching Engineering Graduate Online Students in the U.S. from Pakistan—A Case Study
Bibliographic record
Abstract
Outsourcing is seen from various points of views by individuals in different industries. When it comes to educating science and technology students, and for that matter, students of any discipline, up until recently, outsourcing was not a possibility. With the recent advances in computer and network technology, it is now possible to teach a live online distance-learning course from anywhere around the world. The need for the instructor and the students to be physically present in a common classroom is eliminated. In an on-line course, students and teacher get the opportunity to interact with each other. The computer-based distance learning approach is still in its relative infancy. But since the teaching and learning is done online, via the use of computers connected to inter-connected networks and satellites, the need to be geographically co-located does not exist any longer. The instructor and the students can be physically distributed at various locations, around the city, around the country, or around the world and still be able to teach and learn from each other as if they were present together in the same classroom. Since the medium of instruction is computers, the instructors and students can be outsourced. The flip side of the argument is that since the instructor is not physically present in the same room with the students, they do not get to interact with each other face to face. The instructor can continue to derive a complex mathematical equation without realizing that a student has walked away from their computer. This paper explores the positives and negatives of distance learning distributed education. Advantages and disadvantages of DL are discussed and a few solutions to the challenges, experienced by the instructors, are addressed. The research is based on the author’s experience of teaching online courses from within the city, across the states, and finally across the continents. Amongst other findings, the author discovered that when teaching an online distance learning class, distance is not a factor. While teaching online classes, the author travelled across different cities, states, and internationally and the students did not realize that the instructor was out of town and could not meet with them in the office after class.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".