Comparative analysis of remote, hands-on, and human-remote laboratories in manufacturing education
Bibliographic record
Abstract
Abstract In the years 2020 - 2023, different concepts of material characterization laboratories as part of a forming technology course in the third bachelor year at the department of mechanical engineering, TU Dortmund University, Germany, have been implemented and evaluated. With the remote experiment, students were able to perform a standard tensile test for steel and aluminum fully autonomously. A so-called human-remote type experiment was used for the in-plane torsion test, where the instructor was equipped with cameras, microphone and head-set, such that students could control the instructor via web and observe the results of their actions in real-time. With the ability to go back to campus, additional experiments, such as the Nakajima test, which is used to characterize the formability in metals, was performed hands-on by the students in the lab. Through a comparative analysis of students' self-assessment regarding different learning outcomes prior and after the course, it was found that given a choice, students usually prefer hands-on labs over human-remote ones. For digital laboratories, the human-remote lab is the preferred choice over the remote experiment. Analyzing the students' overall course performance, it was shown that all types of laboratories provide a sufficient teaching input to perform well regarding several metrics tested in the course.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".