Living-Learning Communities in the First-year Engineering Experience
Bibliographic record
Abstract
This exploratory study investigates the design and impacts of a curated Living-Learning Community (LLC) piloted at a large student residence for a large (1000+ student) first-year engineering design course. Alongside the live-in Faculty-in-Residence (FiR), the research includes surveying first-year engineering students participating in this program and understanding the impacts on the lived student experience, and student motivation. The first-year engineering design, teamwork, and communication course is central to engineering education at this large institution. The course offers opportunities for students to work in multidisciplinary teams, applying term knowledge to authentic engineering applications, in a problem-based learning environment. The goal of the LLC is to create opportunities for interaction and scaffolded connection between like-minded students taking similar courses and inspire learning within the students living environment; an entire floor of the Residence is assigned to be an LLC for the 2022 academic year, with support from the Don, ResLife Office, and the Faculty-in-Residence (an engineering faculty member). The purpose of this study is to observe and report on the student experience throughout the duration of the course. We aim to learn how participating in the Living-Learning community can affect the perception of confidence in students’ learning of course concepts, and inter-team relations. In order to do so, students will be surveyed at multiple check points throughout the semester. Furthermore, additional relevant information will be gathered from the Living-Learning Community Don, teaching assistants, and the course instructors. The outcome of this analysis has the potential to educate us on the positive and negative connotations that come along with close-quarters learning. The applications of the results found with this study can be vast, from influencing future residence programs and the first-year engineering education experience. This study focuses on the student’s perception of their experience. Understanding how the perception of manageability of workload [1] can affect student mental health leads the curiosity of how close-quarters learning can affect the student experience, to what degree, and how this understanding can help influence future programming. A positive experience that improves student confidence of understanding and course material has the potential to positively improve mental health and engagement in education; we hope that this research brings together the academic and lived-experiences of students through this work.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".