Effectiveness of a Software-based Service-learning Project in First-year Seminar Course for Engineering Freshmen During the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract The Service-Learning Project (SLP) component in an introductory engineering freshmen course at the University requires that students complete an engineering project from inception to implementation during their first semester. The project requirements are derived from specific needs of a non-profit community organization. Under normal circumstances, the SLP activities would produce a physical product or working prototype that would be installed at a community site. In fall 2020, due to the COVID-19 pandemic, various cautionary measures and guidelines were put in place by the University to prevent spreads of the virus within the campus community. This hampered the normal ways of carrying out SLP activities in this course. Under these circumstances, in an effort to mitigate the impacts on SLP activities and the overall course delivery, after much deliberation among all instructors of the course for about 75 engineering freshmen, a simpler but still meaningful project concept was devised. The SLP project addresses the needs of an environmental non-governmental organization whose mission includes forging a partnership between the private sector and a state government's department responsible for the conservation and natural resources in one of the Great Lakes areas in order to enhance educational programming. More specifically, the partner organization operates a set of buoys in one of the Great Lakes and collects necessary environmental data. There has been a compelling need for the capability of processing and presenting in a more meaningful way the raw data collected over the past several years and also those coming in every day in an ongoing basis. In this Traditional Research paper, we present a software-based SLP to establish the needed capability of processing raw environmental sensor data from numerous buoys on the lake. The software-based project was intended to minimize physical contacts among students during design and implementation while achieving the typical objectives of service-learning projects. Using Microsoft Excel as a software platform to develop a graphical user interface (GUI) and data processing capability, we assess, via survey instruments and reflection essays, 1) the pros and cons of such a project as an SLP, 2) the effectiveness of teamwork in a partly virtual environment, 3) student awareness of environmental monitoring in a real-world situation, and 4) their perception on significance of the GUI development compared to traditional service-learning projects that are normally physically installed at community sites. We also assess the use of self-regulated learning (SRL) skills under the current circumstances and compare them with the assessment results previously reported in the literature.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".