Students’ Perception of the Link between Their Courses and Future Career
Bibliographic record
Abstract
Reflective writing is known to be helpful in enhancing understanding, promoting life-long learning, and shaping students’ identity as future professional engineers. Students in a second-year chemical engineering course were asked to write a reflective paragraph, maximum of one page, on how they expect to apply the concepts learned in the course in their future profession as an engineer. This task was introduced to students as part of a term project that was due on the last day of classes in the fall term of 2021 academic year. The reflection portion of the term project was worth 2% towards the final grade of the course. It should be noted that this course was focused on technical content and there was no guidance provided on critical reflective writing. This class is taken by students from four different programs including, chemical and biological engineering – process option (CHML), chemical and biological engineering – bio option (CHBE), environmental engineering (ENVL), and integrated engineering (IGEN). The student reflections were qualitatively analyzed using coding and thematic analysis to identify the common themes and skills mentioned by students. The total word count was over 52000 and, on average, students wrote 253 words for their reflection assignment, with a standard deviation of 102 words, a minimum of 54 and a maximum of 629 words. Six key themes were identified. The most common themes referred by students include “sustainability”, “general problem-solving strategy”, and “material and energy balances (MEB) as a backbone of process and product design”. These themes were specifically mentioned by 47%, 40%, and 27% of students, respectively. As expected, sustainability was the most popular theme between ENVL students followed by CHML, IGEN and CHBE students. The prevalent theme among IGEN students was “general problem-solving strategy” as over half of them saw it as the main takeaway of the course. Almost one third of CHML and CHBE students saw this course as the backbone for their program and future career, where as only 25% of ENVL students and only 10% of IGEN students believed so.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".