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Record W2910166032 · doi:10.24908/pceea.v0i0.13009

Will that be on the exam? - Student perceptions of memorization and success in engineering

2018· article· en· W2910166032 on OpenAlexafffundvenue
Ryan Clemmer, Karen Gordon, Julie Vale

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsUniversity of Guelph
FundersUniversity of Guelph
KeywordsMemorizationMathematics educationPsychologyPerceptionCurriculumSample (material)PedagogyChemistry

Abstract

fetched live from OpenAlex

In engineering, it is important for students to develop strong problem analysis skills; however, this skill development may be hindered by a reliance on memorization. In this study, a survey was used to investigate undergraduate engineering student perspectives towards their curriculum and memorization and their styles using Bigg’s revised two-factor Study Process Questionnaire (R-SPQ-2F).The majority of the participants are characterized as students having good study habits, a deep motivation, and deep strategies when approaching their education. They generally recognize the decreasing importance of memorization as they progress in the engineering curriculum. There is also a fairly large subset of students that are classified as deep motivation but surface strategy. Most students believe that at least 50% of an exam should contain questions similar sample problems or assignment questions and surface learners tend to perceive exams to be unfair if too many questions are dissimilar. There was no observed correlation between grades and the R-SPQ-2F results in the courses examined. These results tend to support the hypothesis that surface strategies, including memorization, are being employed by undergraduate students as a means of obtaining adequate performance in lieu of problem analysis skill development.

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)0.001
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 score0.234
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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

Citations2
Published2018
Admission routes3
Has abstractyes

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