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The Challenges and Benefits of Multi-generational Undergraduate Research Projects involving Junior Students

2015· article· en· W2488177993 on OpenAlexafffund
Tomislav Terzin, Justin Reinke, Wyatt Warawa, Anna Duitruk, Oleksandra Zubova

Bibliographic record

VenueInternational Journal for Cross-Disciplinary Subjects in Education · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsMathematics educationUndergraduate researchEngineering ethicsPsychologyMedical educationPedagogyEngineeringEngineering managementMedicine

Abstract

fetched live from OpenAlex

Exposure to research at the undergraduate level can greatly enrich the university experience for the students involved. The benefits of an academic research project to an undergraduate student include: insight and preparation for scientific careers, developing creative and critical thinking skills, encouraging application of coursework concepts to the real-world, and promoting a balance between independent work and collaboration. Despite to all these advantages, undergraduate research is a challenging task for both the student and the supervisor. Undergraduate students have a very limited time available in their schedule to do research and they require more guidance than graduate students. To publish undergraduate research in biology, often more than a single generation of undergraduate researchers is required to be involved in the same project, due to low intensity of research and the interruption of research caused by graduation. The continuation of the same research involving new generation(s) of students brings many challenges described in this paper. Our goal was to provide solutions, using an example of our undergraduate research project on butterfly wing scales. This research was initiated in 2010 and completed in 2015. Work involved four generations of undergraduate students, with the aim of providing equal involvement opportunities to junior and senior biology students alike, as well as welcoming participation from students enrolled in diverse academic disciplines. While the inclusion of such a diverse group of students in an undergraduate research program is attractive, it also presents many novel challenges. We offer recommendations for general principles that could increase the efficiency of the undergraduate research while promoting positive research experience for the students involved.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.261
GPT teacher head0.492
Teacher spread0.231 · 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
Published2015
Admission routes2
Has abstractyes

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