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Record W4250556383 · doi:10.18260/1-2--35414

Undergraduate Students as Visiting Students in the United Kingdom

2020· article· en· W4250556383 on OpenAlex

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

Venue2020 ASEE Virtual Annual Conference Content Access Proceedings · 2020
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of ManitobaUniversity of Toronto
Fundersnot available
KeywordsWorkforceTeamworkWork (physics)Medical educationEngineering managementSmart gridCompetition (biology)Computer scienceEngineeringPolitical scienceElectrical engineeringMedicine

Abstract

fetched live from OpenAlex

In this work-in-progress paper, we discuss our NSF-supported program designed to select, mentor, and send U.S. undergraduate students in electrical power engineering to the University of Strathclyde in the U.K. during summer to engage in research projects and research-related activities.We discuss the program need, logistics, design, and evaluation results.Each year, six new students participate in the program; they are selected via a nation-wide competition.Our primary motivation for this program is to provide students with experience in international research and help prepare the next generation of U.S. competitive STEM workforce capable of innovation.Moreover, the students will develop soft skills such as teamwork, oral and written communication, and time management.Since the operating parameters of the electric grid (e.g., frequency and voltage levels) are different in Europe from those in North America, the students will also gain a firsthand experience of different practices in distribution and transmission systems.The research performed under this program helps achieve the smart grid vision through a combination of technological advances and workforce training.Ultimately, this research will increase the utilization of smart grid infrastructure by integrating distributed renewable energy resources.Our preliminary evaluation results show that overall student participants were very satisfied with how the program is set up, designed, and run, giving an average score of 4.7 out of 5: 100% of scholars said they would consider graduate school after attending this program, and pointed to its catalyzing role.Students appreciated the international experience; for all of them, it was their first time living in a foreign country for an extended time.For more than half, this was also the first time to Europe; 90% of scholars said knowing what they know now, they would participate in the program again, citing hands-on research experience, learning about the culture, learning how others solve their power needs, and availability of PhD students to help them as the highlights of the program.We have received applications from 17 states, and 8 of 16 scholars were female, providing evidence for the effectiveness of our advertisement and minority recruitment plans.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.001
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 head0.065
GPT teacher head0.329
Teacher spread0.263 · 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