Blended Learning in First Year Engineering Labs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 2000, Queen’s Engineering adopted a new model for laboratory instruction to its common first year program. This involved moving from the traditional weekly physics and chemistry labs to a 12 week course on "Experimentation" - in which students learned how to design their own simple physics and chemistry experiments. Offered in a 12 week term, this course, called APSC100 Module 2, began with two shorter "tutorial labs" to introduce the key elements of experimental design then moved through a 6-week lab rotation where students practiced doing well-designed experiments, and finally culminated in a two week "Experimental Design Project". 
 The authors dedicated the summer of 2017 to restructuring this course. Much of the core content was retained, however significant changes were made to pace, method of content delivery, and deliverables. Changes include:
 An improvement in student preparation for the lab, through the introduction of on-line pre-lab content and quizzes, to be completed by students the night before their lab. 
 The elimination of post-lab homework. 
 A slower pace of introduction of early content – the original "2 tutorial lab" format was expanded to 4 tutorial labs 
 The introduction of "electronic lab templates". Templates include the lab instructions as well as blank boxes in which to include diagrams, Excel tables and figures, regression analysis, explanatory text and answers to questions. 
 A new Arduino-based altimeter lab introduces students to large variable data sets. 
 This paper will review the changes to the course, and report on the outcome of these changes following two years of offering the course in the new format.
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.
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 | 0.002 | 0.005 |
| 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 it