Session ASSESSMENT OF STUDENT PERCEPTIONS OF FIRST-YEAR ENGINEERING
Bibliographic record
Abstract
In the fallo 2000, Michigan Techado844 a coJ97 first year pro gramfo all engineering students. The pro gram co839CL o varioL math, science, and general educatio coi ses as well as oC engineeringcog se each semester. WhenvoC88 whethero no to adoh the pro gram, suppo9 was nearlyunanimo3 in all departments in the CoC lege o Engineering. Two exceptioL were in Electrical and CodCJ97 Engineering and in Chemical Engineering. Faculty in thesetwo departments expressedcoedCJ that the first year engineeringcoi ses wereto weightedtogh ds mechanical/civil applicatiol and they therefo e felt that the coe seswoC8 no be o interest no utilityto their students. After implementatio o the pro gram, students were surveyed regarding their levelo satisfactio withvario8 features o the engineeringcoi ses. The reactioC o chemical,coi puter, and electrical engineering studentsto thesequestio6 wereo f primary interest, basedo previo8J8CL xpressed facultycoyC699 abo6 the coC ses. This paper will present the results fro the survey with particularattentio paidto respo9CL bymajo and by gender. Index Terms ---co e requirements, freshman pro grams, student surveys. THE MICHIGAN TECH FIRST-YEAR PROGRAM First-year enst-year0 programs aregaink/ widespread popularityin the U.S. through several educationj reform efforts [1-3].In the fall of 2000, weimplemenVV acommon first-yearen-year0Vk program at Michigan Tech at the same time that the un versity switched from a quarter calenr0 to on basedon semesters. The curriculum template for the first year program atMichigan Tech ispresen/0 in Table I. Most studenV start the programdurin their first semester, however, approximately 30% of eachenh0:V( class isun)LL pared for Calculusdurin their first semesteron campus. This group ofstuden) enden in pre-calculusan othe...
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".