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Record W4384470894 · doi:10.5539/ies.v16n4p1

Comparing the Effectiveness of Newer Linework on the Mental Cutting Test (MCT) to Investigate Its Delivery in Online Educational Settings

2023· article· en· W4384470894 on OpenAlexvenueno aff
Theresa Green, Wade Goodridge, Jon E. Anderson, Eric Davishahl, Daniel Kane

Bibliographic record

VenueInternational Education Studies · 2023
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTest (biology)Mathematics educationSpatial abilityLikert scaleSocial psychologyCognitionDevelopmental psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to examine any differences in test scores between three different online versions of the Mental Cutting Test (MCT). The MCT was developed to quantify a rotational and proportion construct of spatial ability and has been used extensively to assess spatial ability. This test was developed in 1938 as a paper-and-pencil test, where examinees are presented with a two-dimensional drawing of a 3D object containing a cutting plane passing through the object. The examinee must then determine the cross-sectional shape that would result from cutting along the imaginary cutting plane. This work explored three versions of this test (the original and two adapted versions), administered online, to see if there were any differences on the versions regarding student performance. Versions differed in the linework quality displayed as well as shading shown on the surfaces. This study analyzed statics students’ scores on the three online versions of the MCT and on the original paper version of the MCT to identify which version of the test may be most optimal for administering to engineering students. Results showed that there was a statistically significant difference in students’ scores between multiple versions. Understanding which representations of the MCT items are most clear to students will provide insights for educators looking to improve and understand the spatial ability of their students.

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)0.002
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 score0.371
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.057
GPT teacher head0.350
Teacher spread0.293 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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