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Record W2741050295 · doi:10.18260/p.25530

Impact of an Extracurricular Activity Funding Program in Engineering Education

2016· article· en· W2741050295 on OpenAlexaff
Emily Marasco, R. Paul, Stephanie Hladik, Marcela Rodriguez, Laleh Behjat, Lynne Cowe Falls

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTRIPS architectureExperiential learningMedical educationVariety (cybernetics)Engineering educationPublic relationsPsychologyPolitical sciencePedagogyEngineeringComputer scienceEngineering managementMedicine

Abstract

fetched live from OpenAlex

Abstract Participation in extracurricular activities improves engineering students’ professional and leadership skills, civic-engagement and engineering abilities. These activities provide students with cultural and scientific immersion, and are an excellent complement to a technical engineering degree. However, students can be restricted in their ability to participate due to limited finances and due to lack of awareness on opportunities. To minimize this challenge, the *name* Student Activity Fund (SSAF) was developed to promote participation in activities that enhance engineering education and leadership development through a variety of activities. This paper will discuss the details of the SSAF, and provide insight into the levels of impact seen from the program. Annually, students apply to the fund competition as either individuals or groups. The applications must include proposed budgets, detailed itineraries, and a clear description of how the activity will contribute to their leadership, professional, and personal development. In addition, the students must report back to the fund indicating how the moneys were spent and how they brought their experience and knowledge back to campus. A wide range of activities are eligible, however all activities should be experiential in nature and highly participatory. Some examples of successful applications in the past include the solar car team, educational trips to major cities, group studies abroad, and academic conferences. Activities should complement and enhance classroom learning and the engineering graduate attributes. Applications are evaluated by a committee of students, alumni and faculty. When the evaluation committee reviews applications they are looking for students who have clearly demonstrated how the activity will enhance their engineering education. Successful applicants must also submit a final report afterwards describing the impact on their learning experience, a reflection on their personal and leadership development goals, and a description of their contribution. The paper will discuss the evolution of the fund since its inception ten years ago. Data to be presented includes the number of funded students and groups, percentage of applications compared to activities funded, and trends in the funding applications activity types over the years. When discussing the impact of these activities, we will look at three factors. Firstly, the factor of time will investigate immediate, short term and long term impacts. The second factor, culture, will compare the impact on the individual to the impact brought back to the University community. And lastly, the factor of depth will gain insight into whether the impact is on the surface or if the impact has a much deeper change. All of these will consider both personal and academic growth for the students, their peers, the faculty and faculty members. The SSAF provides a model for encouraging extra-curricular activities for other schools as it reduces the barrier to these experiences while building student leadership through the application and competition. The paper will also recommend how to further increase the success and impact of an extracurricular activity funding program in engineering education.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.281
Teacher spread0.272 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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