Assessing the Impact of Transitioning Introductory Design Instruction to an Online Environment
Bibliographic record
Abstract
Abstract In their first academic term (1A), students in the Mechatronics Engineering (MTE) program at the University of Waterloo are required to take 5 courses including programming in C++ (MTE 121 – Digital Computation), and a course which introduces them to the profession (MTE 100 – Mechatronics Engineering). For the last 5 years, these courses have included both a multi-week, integrative, open-ended robotics project with deliverables in both courses, and a 2-day long curricular hackathon dubbed “Tron Days” which further integrates the 1A courses together by having students solve an ill-structured design task. In previous publications on these activities, there has been evidence of growth in student self-efficacy (as described by Bandura) in the skills relating to these projects (viz. programming, design, and teamwork), as well as growth in student epistemological development (as described by Perry). As these courses take place in students’ first academic term, this personal growth is crucial to maintain, even as we transitioned to online instruction for fall 2020. Both courses under study (in both 2019 and 2020) have implemented Felder and Brent’s suggested instructional conditions to facilitate intellectual growth, albeit with different emphases; and so epistemological development is expected in both years. This paper described the adaptations to MTE 100 and MTE 121 to transition them to an online teaching mode, including the revised Tron Days and course project, and investigated what impact, if any, this transition had on student self-efficacy and epistemological development. Since 2013, students in first year MTE have been invited to complete a start of term and end of term survey to capture the impacts of the semester’s instruction on individual students. Statistical analyses of these data showed students’ self-efficacy beliefs in programming and design improved from start to end of term in both 2019 and 2020, and student self-efficacy beliefs in teamwork improved from start to end of term in 2020. Regression analyses predicting end of term self-efficacy beliefs showed there was no statistically significant impact on programming or design beliefs based on year, however year was a statistically significant coefficient for the regression model predicting teamwork self-efficacy with students in 2020 rating higher. Analyses of data on personal epistemology was inconclusive due to poor validity and reliability. Overall, students showed improved self-efficacy beliefs after participating in the remote teaching environment of 2020, similar to the students who were taught in-person in 2019.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".