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Record W3189360944 · doi:10.18260/1-2--37010

Effectiveness of a Software-based Service-learning Project in First-year Seminar Course for Engineering Freshmen During the COVID-19 Pandemic

2024· article· en· W3189360944 on OpenAlex
Wookwon Lee, Pezhman A. Hassanpour, Saeed Tiari

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2021 ASEE Virtual Annual Conference Content Access Proceedings · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsService-learningGeneral partnershipGovernment (linguistics)Engineering educationEngineering managementEngineeringComputer scienceBusinessPsychologyPedagogy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.348
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it