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Record W2287608101 · doi:10.18260/1-2--13218

Undergraduate Research: The Lafayette Experience

2020· article· en· W2287608101 on OpenAlexaboutno aff
Mary J. S. Roth, Kristen L. Sanford Bernhardt

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsUndergraduate researchInstitutionMedical educationVariety (cybernetics)Class (philosophy)Quarter (Canadian coin)Mathematics educationPsychologySociologyComputer scienceMedicineSocial science

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Undergraduate Research: The Lafayette Experience Kristen L. Sanford Bernhardt, Mary J.S. Roth Lafayette College Introduction Lafayette College is an undergraduate institution with approximately 2200 students. On average, approximately 80 of those students are civil engineering majors; the Department of Civil and Environmental Engineering graduates anywhere from 12-25 students per class. The opportunity for students to conduct one-on-one research with a faculty member is a strength of the Lafayette College environment. Lafayette encourages undergraduate research in all disciplines through a variety of programs, including independent studies, honors theses, and paid research assistantships (called the EXCEL Scholars program). The Department of Civil and Environmental Engineering has been highly successful in involving students in research experiences through independent studies and as EXCEL scholars, and moderately successful at graduating students with honors theses. On average, approximately one quarter of the students in the department are involved in research with faculty in any given semester, and a higher percentage participate at some time during their Lafayette careers. There are many possible ways to define what constitutes a “successful” undergraduate research experience. As an institution, Lafayette College does not aim to send students specifically to industry or to graduate school; rather, the goal is to provide students with experiences that will enable them to make informed decisions about their future. We consider the experience of a student who discovers that he or she does not enjoy research to be as much a success as the student whose experience spurs an application to graduate school. A successful research experience also must satisfy faculty needs. Lafayette’s tenure and promotion requirements include scholarly work. With no graduate research assistants, faculty members often must rely on undergraduate researchers for assistance. Research products, such as papers and presentations, are quantifiable measures of research productivity, and these products can result from student research experiences. A research product not only helps faculty members, it gives students a specific goal and a sense of accomplishment, and it provides a distinguishing characteristic for the student’s resume or graduate school applications. The objective of this paper is to examine the undergraduate research experience in our department at our institution. We first describe in detail the types of experiences that are available to our students. We then summarize the last five years of student research projects conducted in the department. Based on this information and discussions with department faculty, we summarize the lessons we have gleaned from this study. Finally, we outline our plans both for increasing student involvement and for increasing the quality of the experiences. Proceedings of the 2004 American Society for Engineering Education Annual Conference & Exposition Copyright © 2004, American Society for Engineering

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.154
Threshold uncertainty score0.515

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0120.004
Scholarly communication0.0090.006
Open science0.0020.013
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.1540.042

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 head0.191
GPT teacher head0.372
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations8
Published2020
Admission routes1
Has abstractyes

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