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Record W2911816293 · doi:10.18260/1-2--29801

Analysis of Student Utilization and Activities in a Campus Innovation Center

2020· article· en· W2911816293 on OpenAlexaboutno aff
William Kline, Timothy Chow, Tony Ribera

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsnot available
Fundersnot available
KeywordsCapstoneClass (philosophy)Center (category theory)Space (punctuation)Variety (cybernetics)Resource (disambiguation)Computer scienceQuarter (Canadian coin)BusinessEngineering managementKnowledge managementEngineeringGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The ABC Innovation Center has served our campus for six years providing support for competition teams, capstone and class projects, a maker space, and other campus activities. The 16,000 sq ft building provides project space, supervision, and access to fabrication and prototyping resources. The innovation center has been a popular and increasingly utilized resource with student entries to the building increasing 1.35 times in the last two years. This paper will report on student utilization of our innovation center through the analysis of activity records. Over the years of operation of the center, multiple measures of student activity and utilization have been collected including lock logs, team rosters, and training completions. This paper will analyze these sources of data and report on several aspects of growth and utilization of the center including a. overall level of student activity over the last three years, b. student activity broken down by time of year, quarter, and day, c. student gender and class year, and d. student participation in training classes. The paper will also report on assessment approaches that have been used and general student feedback on the center and activities. It is believed that these results are useful in a variety of situations including prioritizing center activities, marketing the program to students, allocating space, scheduling staff, and determining overall resource needs.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.149

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 communication0.0000.000
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 head0.022
GPT teacher head0.264
Teacher spread0.242 · 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
Published2020
Admission routes1
Has abstractyes

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